Rapid prototyping for interactive robots

第２１卷第１２期２０２３年１２月动力学与控制学报J O U R N A L O FD Y N AM I C SA N DC O N T R O LV o l ．２１N o ．１２D e c ．２０２３文章编号:１６７２Ｇ６５５３Ｇ２０２３Ｇ２１(１２)Ｇ０３７Ｇ０１６D O I :１０．６０５２/１６７２Ｇ６５５３Ｇ２０２３Ｇ１３３㊀２０２２Ｇ０５Ｇ１５收到第１稿,２０２２Ｇ０９Ｇ１８收到修改稿．∗国家自然科学基金资助项目(５２０７５４１１,５２３０５０３４),N a t i o n a lN a t u r a l S c i e n c eF o u n d a t i o no fC h i n a (５２０７５４１１,５２３０５０３４)．†通信作者E Ｇm a i l :l i b o x j t u ＠x jt u ．e d u ．c n 微小型跳跃机器人:仿生原理,设计方法与驱动技术∗吴业辉１,２㊀刘梦凡１,２㊀白瑞玉１,２㊀李博１,２†㊀陈贵敏１,２(１．西安交通大学机械制造系统工程国家重点实验室,西安㊀７１００４９)(２．西安交通大学陕西省智能机器人重点实验室,西安㊀７１００４９)摘要㊀高爆发性的跳跃是生物亿万年进化演变中赖以生存的关键之一,帮助生物实现在各种非结构化环境下的灵活运动功能．通过对生物跳跃机制的深入理解,微小型跳跃机器人在功能及性能上取得长足进步．本文以生物跳跃运动四个阶段(准备㊁起跳㊁腾空和着陆)为主线,剖析了生物的行为原理,介绍了对应的微小型跳跃机器人的动力学特征与技术,归纳了现有研究的挑战,最后讨论了跳跃机器人的未来发展趋势和潜在研究价值．关键词㊀跳跃机器人,㊀生物跳跃机制,㊀仿生中图分类号:T P ２４２文献标志码:AAR e v i e wo f S m a l l ＧS c a l e J u m p i n g Ro b o t s :B i o ＧM i m e t i cM e c h a n i s m ,M e c h a n i c a lD e s i gna n dA c t u a t i o n ∗W uY e h u i １,２㊀L i u M e n g f a n １,２㊀B a iR u i yu １,２㊀L i B o １,２†㊀C h e nG u i m i n １,２(１．S t a t eK e y L a b o r a t o r y o fM a n u f a c t u r i n g S y s t e m E n g i n e e r i n g ,X i a n J i a o t o n g U n i v e r s i t y ,X i a n ㊀７１００４９,C h i n a )(２．S h a a n x i P r o v i n c eK e y L a b o r a t o r y f o r I n t e l l i g e n tR o b o t s ,X i a n J i a o t o n g U n i v e r s i t y,X i a n ㊀７１００４９,C h i n a )A b s t r a c t ㊀H i g h l y e x p l o s i v e j u m p i n g i s o n e o f t h e s u r v i v a l k e y s t o t h e o r ga n i s me v o l u t i o no v e r t h e c o u r s e o fb i l l i o n s o f y e a r s ．T h i sm o v e m e n t h e l p s o r ga n i s m s t oa c h i e v e f l e x ib l em o v e m e n t f u nc t i o n su nde rv a r i Ｇo u s u n s t r u c t u r e d c o n d i t i o n s ．T h r o u g ha n i n Ｇd e p t hu n d e r s t a n d i n g o fb i o l o g i c a l j u m p i n g me c h a n i s m ,t h e s m a l l Ｇs c a l e j u m p i n g r o b o t h a sm a d e g r e a t p r o g r e s s i nf u n c t i o na n d p e r f o r m a n c e ．T a k i ng th e f o u r s t a ge s of b i o l og i c a l j u m p i n g m o v e m e n t (p r e p a r a t i o n f o r t a k e Ｇo f f ,t a k e Ｇo f f ,f l i gh t a n d l a n di n g)a s t h em a i n l i n e ,t h i s p a p e r r e v i e w s t h e b e h a v i o r a l p r i n c i p l e o f o r g a n i s m s ,i n t r o d u c e s t h e d yn a m i c c h a r a c t e r i s t i c s a n d t e c h Ｇn o l o g y o f t h e c o r r e s p o n d i n g s m a l l Ｇs c a l e j u m p i n g r o b o t s ,s u mm a r i z e s t h e c h a l l e n g e s o f e x i s t i n g r e s e a r c h ,a n d f i n a l l y d i s c u s s e s t h e f u t u r e d e v e l o p m e n t a n d p o t e n t i a l o f j u m p i n g r o b o t s ．K e y wo r d s ㊀s m a l l Ｇs c a l e j u m p i n g r o b o t s ,㊀b i o l o g i c a l j u m p i n g m e c h a n i s m ,㊀b i o n i c 引言随着现代社会中机器人作业任务难度的提高,机器人在运动模式上也进入了全面发展的阶段,已经形成足式[１]㊁轮式[２]㊁蠕动式[３Ｇ５]㊁翻滚式[６,７]等多元化的研究体系,在生产协作㊁社会服务㊁医疗康复等场景下发挥着越来越重要的作用．但是一些非结构化的场景如星球探索㊁抢险救灾㊁环境监测,对机动㊀力㊀学㊀与㊀控㊀制㊀学㊀报２０２３年第２１卷器人的运动性能提出了更高的要求．机器人需要以更小的体积适应狭小空间环境,快速翻越数倍于自身尺寸的障碍,还需要携带一定负载来完成通讯㊁检测㊁运输等功能,因此机器人在小体积㊁大负载㊁高能量密度㊁高爆发性㊁高灵活性等功能的发展有待提升．作为生物界一种独特的运动模式,跳跃运动在蝗虫[８,９]㊁跳蚤[１０,１１]等昆虫中经历了万亿年的演变,可与奔跑㊁飞行㊁游泳等运动模式相结合,帮助动物以极快的速度逃避天敌㊁捕食猎物,增强了生物的越障能力,使其更好的适应丛林㊁山地等复杂多变的地形．为了探寻生物产生爆发性跳跃运动的原因,科学家对各类具有出色跳跃性能的生物进行研究,发现生物体内弹性储能与闩锁结构的组合是解决微小型动物在爆发驱动中功率受限的关键[１２]．像沫蝉(F r o g h o p p e r s)[１３Ｇ１５]㊁跳蚤(F l e a s)[１０,１１]㊁叩头虫(C l i c kb e e t l e s)[１６Ｇ１８]㊁蝗虫(G r a s s h o p p e r s或L oＧc u s t s)[８,９]㊁弹尾虫(S p r i n g t a i l s)[１９Ｇ２１]等节肢动物,通过弹性蛋白㊁角质层等进行储能,利用身体中闩锁机构控制能量的锁定和释放,能够完成其自身尺寸的几十倍甚至上百倍的跳跃运动;青蛙(F r o g s)[２２,２３]等生物虽然没有特定的闩锁机构,但是具有可变的有效机械效益(E MA,E f f e c t i v em eＧc h a n i c a l a d v a n t a g e)的腿部,利用腿部肌肉所串联的肌腱进行功率放大,增强了自身的跳跃性能．根据仿生学原理,以微小型生物跳跃机理为灵感的跳跃机器人近些年得到了快速发展,其跳跃性能取得长足进步．到目前为止,机器人可实现单次约３３m的跳跃高度[２４],是其自身特征尺寸的百倍以上,也可以实现像夜猴一般敏捷的连续跳跃[２５];不仅能像蝗虫一般在路上跳跃,也如水黾一般从水面跳跃[２６],甚至有望实现在半空中跳跃[２７]．现如今,跳跃机器人的研究向集成化㊁多功能方向发展,在对大自然的学习中获得了各类生物跳跃相关的各类技能,逐步实现对生物的超越．综合考虑机器人的灵活性与负载能力,本文将集中讨论微小型的跳跃机器人(特征尺寸在３０厘米以内),从跳跃运动的起跳㊁腾空㊁着陆㊁准备四个基本阶段[２８]出发,对微小型生物跳跃及相关行为的机理进行综述,分析不同生物在储能与释放㊁腾空姿态㊁着陆缓冲㊁方向调整等方面的优势;在此基础上,对比现有的跳跃机器人各阶段功能的实现方式,结合生物特点分析仿生跳跃机器人的未来发展趋势以及面临的挑战,为其实现广泛应用提供设计参考．１㊀微小型动物的跳跃运动原理同其他具有跳跃功能的物种一样,微小型生物的跳跃行为可按照运动的状态的不同分为四个阶段,包括跳跃前的准备阶段㊁加速起跳阶段㊁腾空滑行阶段和落地缓冲阶段,如图１所示．在各个阶段,不同的生物根据自身生存条件的不同,进化出与各自所处环境相适应的跳跃特点,而受生物启发的跳跃机器人正是基于这些特点在高爆发㊁高集成㊁高灵活性等方面实现突破．图１㊀跳跃运动的四个阶段F i g．１㊀T h e f o u r p h a s e s o f a j u m p i n g m o t i o n １．１㊀起跳阶段在起跳阶段,生物体从肌肉㊁弹性元件等驱动单元内获得能量,完成从静止状态至脱离地面的加速运动过程．在驱动方式方面,微小型生物由于四肢短小且无法形成高主动应变率的肌肉[２９],因此多以机械储能的方式增大起跳功率,同时与闩锁结构的控制相配合,完成能量在短时间内的可控释放．此方式尤其体现在主要依靠弹性储能产生跳跃的生物中,如叩头虫[１６Ｇ１８]利用骨骼结构之间物理接触的作为闩锁来锁定弹性能[如图２(a)所示],该类型被称为接触式闩锁[３０];瘿蚊幼虫(t h e M e d iＧt e r r a n e a n f r u i tＧf l y l a r v a)[３１,３２]利用首尾钩状结构或微纳结构等摩擦接触将身体连接成环状,从而限制自身的形变,进而通过肌肉挤压内部液体来储存跳跃所需的弹性能[图２(b)];跳蚤[１０,１１,３３]㊁蝗虫[８,９]㊁沫蝉[１３Ｇ１５]等生物则利用跳跃机构的几何构型作为闩锁,而并非通过接触的方式实现弹性能量存储[图２(c)],该类型也被称为几何式闩锁．青蛙[２２,２３]㊁蟋蟀(C r i c k e t s)[３４]等生物由于具有较长的后肢而具有较长的驱动行程,而可以通过肌肉直接驱动的方式获得优异的跳跃性能．但是由于８３第１２期吴业辉等:微小型跳跃机器人:仿生原理,设计方法与驱动技术肌腱与肌肉的串联,青蛙同时也借助弹性元件来增强跳跃的驱动功率,其运动过程中同样存在几何闩锁[１２],锁定效果可通过 有效机械效益 (E MA)来衡量．对于跳跃运动而言,E MA是地面对生物的支反力(G R F)和肌肉驱动力(F)的比值(E MA＝G R F/F),可以表示串联弹性系统中肌肉所做的功流向弹性储能的大小,如图２(d)所示．E MA较小表示肌肉做功转化为串联弹性元件中储能,而不是直接驱动肢体加速跳跃;反之,表示肌肉做功大部分用于直接驱动,而非利用弹性元件储能．因此,如果E MA可以随肌肉收缩产生 阶跃 式的由小增大过程,则可以将其视为具有动力学 闩锁 ,前期储存的机械能也将在高E MA水平期间释放,从而达到增强跳跃瞬间功率的目的．此外,同样采取直接驱动方式的跳蛛(J u m pＧi n g s p i d e r s)[３５Ｇ３９]可以利用肌肉驱动 液压 关节完成腿部的快速伸展,从而完成跳跃运动[图２(e)],为跳跃运动的驱动实现提供了新的灵感[４０]．图２㊀起跳阶段生物行为与机理．a．叩头虫利用骨骼作为接触式闩锁储能[１６Ｇ１８];b．瘿蚊幼虫利用嘴钩作为闩锁而锁定自身形状[３１,３２]; c．跳蚤采用几何式闩锁(扭矩反转机构)锁定机械能[１０,１１,３３];d．青蛙利用串联弹性元件增大跳跃功率[２２,２３];e．蜘蛛采用液压直驱的方式跳跃[３５Ｇ３９] F i g．２㊀B i o l o g i c a l b e h a v i o r a n dm e c h a n i s m s d u r i n g t a k e o f f．a．C l i c k b e e t l eu s e s s k e l e t o na s c o n t a c t l a t c h t o s t o r e e n e r g y[１６Ｇ１８]; b．T h eM e d i t e r r a n e a n f r u i tＧf l y l a r v a s u s em o u t hh o o k s a s l a t c h e s t o l o c kb o d y s h a p e[３１,３２];c．F l e a s u s e g e o m e t r i c l a t c h(t o r q u er e v e r s a lm e c h a n i s m)t o s t o r em e c h a n i c a l e n e r g y[１０,１１,３３]; d．F r o g s u s e s e r i e s e l a s t i c e l e m e n t s t o i n c r e a s e j u m p i n g p o w e r[２２,２３];e．S p i d e r s j u m p i n g d r i v e nb y h y d r a u l i c f o r c e[３５Ｇ３９]１．２㊀腾空阶段在腾空阶段,生物体完成受空气阻力和自重影响下的斜抛运动,直至其身体与地面接触．许多生物虽然拥有相对自身尺寸数十倍的跳跃能力,但是在腾空之后不具备姿态调整功能,因此无法控制滑行时的轨迹和着陆时的姿态．在半空中姿态重新定位被称为适应性行为矫正,分为被动方式和主动方式[４１]．被动方式如豌豆蚜虫(A c y r t h o s i p h o n p iＧs u m)在高空坠落过程中不需要来自神经系统的动态控制或持续反馈,只是通过空气动力学稳定的姿势来被动地纠正自己[４２];其他跳跃生物则通过翅膀[４３]㊁肢体[２１]㊁尾巴[４４]等部位主动调整身体姿态．相对而言,被动方式需要的控制单元少,但是对环境依赖程度更高,而主动方式则更多见．为了适应不同的着陆角度,跳甲(F l e ab e t t l e s)根据所感知到的着陆点角度等信息,通过翅膀的主动运动来调整自身姿态,有效提高正面着陆的概率(如图３(a)所示),同时却并不影响其跳跃的高度．白粉虱(W h i t e f l i e s)[４３]也采取相同的策略,仅仅通过翅膀的伸展即可完成空中的稳定飞行,以防止翻筋斗,如图３(b)所示．图３㊀腾空阶段生物行为与机理．a．跳甲利用翅膀调整腾空姿态[４１];b．白粉虱利用翅膀防止翻筋斗[４３];c．弹尾虫利用腹管和 U 型姿势调整腾空状态[２１]F i g．３㊀B i o l o g i c a l b e h a v i o r a n dm e c h a n i s m s d u r i n g f l i g h t．a．F l e ab e t t l e s a d j u s t a e r i a l p o s t u r ew i t hw i n g s[４１]; b．W h i t e f l i e s p r e v e n t s o m e r s a u l t sw i t hw i n g s[４３];c．S p r i n g t a i l a d j u s t a i r b o r n e s t a t e sw i t h c o l l o p h o r e a n d＂U＂s h a p eb o d y[２１]除了以上具有飞行能力的生物,半水生的弹尾虫[２１]虽然没有翅膀却同样可以实现姿态矫正的功能．弹尾虫在起跳之前将腹部紧贴水面,通过具有亲水性的腹管收集水滴来改变自身的质量分布,在起跳之后将整个身体弯曲成U型,这两种行为都有助于矫正倾斜的姿态,并且避免了着陆前的翻转,如图３(c)所示．１．３㊀着陆阶段在着陆阶段,生物体依靠阻尼损耗㊁弹性储能９３动㊀力㊀学㊀与㊀控㊀制㊀学㊀报２０２３年第２１卷等方式把自身的运动减速至静止状态．跳跃生物的缓冲方式也分为主动型和被动型,包括利用空气阻力的滑翔运动㊁变角度着陆足㊁吸收冲击的保护壳㊁变刚度肢体等．如生活在热带雨林中的飞蛙(G l iＧd i n g f r o g s)[４５,４６],依靠宽大的脚掌和趾间的蹼膜完成滑翔运动,并且具有较强的被动空气动力学稳定性,可以从树干高处快速降落来捕捉猎物或逃避天敌．滑翔运动有效改变着陆时的速度方向并通过较大的空气阻力降低速度大小,从而明显降低着陆时对地的冲击速度[４７],如图４(a)所示．无论是否具有滑翔功能,青蛙均利用前肢进行主动着陆缓冲,前肢接触地面并形成一个支点,身体围绕这个支点旋转,直至完成后肢落地[４８]．在着陆过程中,青蛙根据跳跃高度㊁水平速度的不同调整前肢的着陆角度,从而获得最小的冲击,如图４(b)所示．图４㊀着陆阶段生物行为与机理．a．飞蛙利用脚蹼实现滑翔运动[４５Ｇ４７]; b．青蛙前肢着陆过程中最小冲击角度调整[４８];c．瓢虫利用相互耦合的鞘翅进行缓冲,耦合面形状如图中红蓝曲线所示[４９]F i g．４㊀B i o l o g i c a l b e h a v i o r a n dm e c h a n i s m s d u r i n g f l i g h t．a．F r o g s g l i d i n g w i t h f l i p p e r s[４５Ｇ４７];b．A d j u s t i n g o f f r o g f o r e l i m ba n g l e f o r m i n i m u mi m p a c t d u r i n g l a n d i n g[４８];c．E l y t r a c o u p l i n g o f l a d y b i d s f o r b u f f e r i n g,a n d t h e s h a p e o f t h e c o u p l e d s u r f a c e i sh i g h l i g h t e di n t h e r e da n db l u e c u r v e s[４９]瓢虫(L a d y b i r d s)㊁甲虫等昆虫大多利用壳体减小冲击对自身的冲击,其中瓢虫除了采用由甲壳素微纤维和蛋白质组成的具有空腔的壳体来吸收能量,还利用成一定角度㊁相互耦合的翅鞘增强缓冲功能,以提供更多的能量吸收并减少碰撞后的反弹[４９],如图４(c)所示．如１．１节所述的瘿蚊幼虫,依靠柔软的身体进行储能跳跃的同时,也能利用身体足够柔软的特点吸收着陆冲击,使其无需采用专用的缓冲结构．与有足动物类似,相较于起跳阶段肌肉运动产生的高刚度,着陆时其身体刚度显然有所降低,有利于增大着陆冲击力的作用时间,从而降低冲击力的大小．１．４㊀准备阶段在准备阶段,生物体完成姿态恢复㊁跳跃能量储备㊁跳跃目标位置确定㊁跳跃方向和角度调整等工作．对于利用双足来进行跳跃的生物而言,其跳跃方向大多朝自身的正前方,依靠双足的同步运动来完成．像伊苏斯飞虱(I s s u s c o l e o p t r a t u s)在幼虫阶段时,由于其起跳所用时长为毫秒级,而神经信号同样为毫秒级,因此在双腿同步性控制方面具有很大难度．为了保证跳跃方向准确性,避免跳跃之后身体旋转和方向偏离,伊苏斯虫利用带有齿轮状的肢体保证了起跳时双腿的同步性[５０],如图５(a)所示．为了从倾倒之后的 四脚朝天 姿态中恢复,常见的昆虫如蟑螂(C o c k r o a c h e s)㊁瓢虫等均可根据不同的地形,利用鞘翅㊁腿足的配合可以通过不同的策略完成翻身运动．其中,蟑螂可以采取腹部弯曲侧滚㊁鞘翅翻滚㊁腿部侧滚等策略[５１,５２],如图５(b１)~(b３)所示．相较于蟑螂,瓢虫[５３]的腿部较短,在粗糙表面多依靠足部勾住隆起物而翻转扶正,在光滑表面则依靠鞘翅来辅助翻滚．图５㊀准备阶段生物行为与机理．a．伊苏斯虫利用齿轮状肢体保证了双腿起跳同步性[５０];b．蟑螂利用腹部㊁鞘翅和腿部实现翻身[５１,５２];c．弹尾虫通过不同初始角度调整跳高㊁跳远两种模式[２１]F i g．５㊀B i o l o g i c a l b e h a v i o r a n dm e c h a n i s m s d u r i n g p r e p a r a t i o n o f t a k e o f f．a．I s u s i a e n s u r i n g t h e s y n c h r o n i z a t i o no f b o t h l e g s i n j u m p i n g w i t h g e a r e d l i m b s[５０]．b．C o c k r o a c h e s t u r n i n g o v e r b y a b d o m e n, e l y t r a a n d l e g s[５１,５２];c．S p r i n g t a i l s w i t c h e s b e t w e e n j u m p a n dl o n g j u m p m o d eb y a d j u s t i n g d i f f e r e n t i n i t i a l a n g l e s[２１]０４第１２期吴业辉等:微小型跳跃机器人:仿生原理,设计方法与驱动技术在跳跃角度控制方面,青蛙等常利用腿部不同关节的协调运动来实现[５４,５５]．对于半水生的弹尾虫而言,除了利用跳跃尾部的不同作用力,还可以通过调整跳跃前的初始角度并利用腹管的亲水性,实现跳高㊁跳远两种模式的切换[２１],如图５(c１)和图５(c２)所示．２㊀跳跃机器人的设计与驱动方法从上世纪八十年代开始,结合对跳跃生物能量存储机制等问题的研究,科学家们开始致力于跳跃机器人的研究[５６],各类仿生跳跃机器人不断涌现并逐渐成为热点[２４Ｇ２６,５７]．２．１㊀跳跃机器人储能结构与能量调控类比于生物所采用的弹性蛋白㊁角质层㊁肌腱㊁体液等储能元件,跳跃机器人多采用人造弹性元件,包括螺旋弹簧㊁扭簧㊁形状记忆合金弹簧㊁柔性梁㊁弹性绳等,不同类型的弹性元件具有不同的储能密度和变形形式,其特点直接影响机器人的跳跃能力和运动形式．L a m b r e c h t等人设计了一种仿蟑螂轮腿式机器人[５８,５９],该机器人利用差齿齿轮旋转拉伸螺旋弹簧而实现能量的加载和释放,当作用齿轮达到差齿位置时,平行四连杆跳跃机构随弹簧释放而弹出,推动机器人产生向前的跳跃,而 Y 形三脚架模拟昆虫足部来实现爬行和小型障碍的跨越,如图６(a)所示．由于集成跑㊁跳运动模式,其质量达到１９０克,因此跳跃能力只能达到１８厘米,如图６(b)所示．图６㊀M i n iＧW h e g s机器人[５８,５９]F i g．６㊀R o b o tM i n iＧW h e g s[５８,５９]Y a m a d a等人利用细长悬臂梁在末端压弯载荷下屈曲失稳现象设计了一种跳跃机器人,定义为 封闭式弹性弹射器 [６０,６１],如图７(a)所示．该机器人采用柔性梁的屈曲进行储能并可在末端旋转电机的带动下实现能量可控释放,既可以利用单电机实现二阶屈曲到一阶屈曲的能量释放,也可以采用对称布置的双电机实现三阶屈曲到一阶屈曲的能量释放,达到一定跳跃方向改变．储能和释放结构的集成使其结构简单,梁的形状及其两端角度变化对释放能量的大小和快慢起决定性的影响,梁变形过程如图７(b)所示．该机器人在单电机驱动下可跳跃２０厘米高㊁７０厘米远．图７㊀封闭弹性弹射机器人[６０,６１]F i g．７㊀A j u m p i n g r o b o t b a s e do n t h e c l o s e d e l a s t i c a[６０,６１]J u n g等人提出一种仿甲虫爬跳结合的机器人J u m p R o A C H[６２],如图８(a)所示．通过对线弹簧和扭簧的组合,机器人储能元件力位移特性近乎于恒力机构,最大程度的利用电机的负载能力从而扩大了其储能能量,如图８(b)所示．机器人通过电机卷绳方式加载,采用行星轮系作为能量锁定和释放机构,能够起到控制能量加载大小的作用．除此之外,该机器人结合了跳跃和爬行两种运动模式,具备完整的重复跳跃能力．在测试中,无爬行部分的机构可以实现２．７５米的跳跃,而结合爬行和复位壳体部分之后体重增加一倍,仍然能实现１．５米高的跳跃,越障过程如图８(c)所示．图８㊀J u m p R o A C H跳跃机器人[６２]F i g．８㊀R o b o t J u m p R o A C H[６２]在此基础上,H a w k s等人利用柔性梁和线弹簧的组合方式达到了类似的恒力效果,在不超过电机最大功率条件下,牺牲加载速度而能够以最大恒力进行弹性能量加载,如图９(a)所示．根据其理论,弹簧－连杆质量比越大的机器人其最终能量密度越高,因此以柔性梁作为弹簧和腿部的集成,可以很大程度增加跳跃高度;借助A s h b y图[６３]对材料１４动㊀力㊀学㊀与㊀控㊀制㊀学㊀报２０２３年第２１卷进行优化,选择碳纤维复合材料和乳胶组合构成储能元件,最终使重量３０．４克的机器人[图９(b)]实现了３２．９米的跳跃高度,这也是目前最高的机器人绝对跳跃高度[２４]．图９㊀目前跳得最高的机器人[２４]F i g．９㊀T h eh i g h e s t j u m p i n g r o b o t s o f a r[２４]除了储能大小和变形方式上的差异,不同的储能元件在跳跃运动中其动力学模型复杂度也不同,如通过柔性梁的大变形进行储能的模式比线性弹簧结合刚性连杆的方式更为复杂．起跳过程的动力学分析主要用于预测机器人起跳速度和高度,因此对于难以建立动力学模型的间歇型跳跃机器人(落地后无需立即起跳)一般直接利用弹簧的弹性变形能来估计跳跃高度;对于连续型跳跃机器人由于涉及到机器人的姿态㊁方向等控制,触地瞬间至起跳离地过程的动力学模型更为关键．２．２㊀跳跃机器人闩锁结构与能量动态释放在依靠弹性储能进行跳跃的机器人中,闩锁机构控制能量的释放过程,不同的结构不仅影响能量的存储量,而且对释放过程的动力学特征(势能转化为动能的时间㊁空间和速率等)起到决定性作用[１２]．闩锁结构除了前文所述的接触式㊁几何式闩锁,还包括流体式锁闩[６４],其中流体式闩锁是指由系统内流体的运动和性质(包括凝聚力㊁聚结性和压力)对弹性元件进行调节;而接触式闩锁是指通过摩擦和机械限位的作用来阻挡弹性元件运动[３０],如图１０(a)所示;几何式闩锁则是基于几何构型㊁力㊁力矩臂㊁质心位置等的状态相关行为的锁闩,包括像青蛙㊁夜猴等体内的可变机械效益机构[６５][图１０(b)]㊁跳蚤体内的扭矩反转机构[６６][图１０(c)]㊁失稳突跳机构和其他具有双稳态特点的系统[６７Ｇ７１][图１０(d)]．K o v a c等人设计的 ７g 的跳跃机器人如图１１所示,采用凸轮和扭簧作为释放和储能机构,其跳跃高度由凸轮的形状和弹簧刚度所决定,跳跃方向与凸轮形状和腿部尺寸相关,一旦装配完成则无法调整,其运动灵活性因此受到一定限制．约５厘米高的机器人可以跳跃自身高度的２７倍,达到１．４米[７２],如图１１(b)所示;携带３克负载后跳跃高度仍能达到１米,如图１１(c)所示．图１０㊀常见的闩锁结构．a．接触式闩锁简化模型[３０];b．青蛙等生物体内的可变机械效益结构[６５]; c．跳蚤体内的扭矩反转机构[６６];d．屈曲梁双稳态机构[６７Ｇ７１] F i g．１０㊀C o mm o n l a t c h s t r u c t u r e s．a．S i m p l i f i e dm o d e l o f c o n t a c t l a t c h[３０];b．V a r i a b l em e c h a n i c a l a d v a n t a g e s t r u c t u r e i n f r o g s a n d o t h e r o r g a n i s m s[６５];c．T o r q u e r e v e r s a lm e c h a n i s mi n f l e a s[６６]; d．B i s t a b l em e c h a n i s ma n d e n e r g y c u r v e o f b u c k l i n g b e a m[６７Ｇ７１]图１１㊀ ７g 机器人[７２]F i g．１１㊀R o b o t ７g [７２]Z a i t s e v等人模拟蝗虫跳跃过程设计了一种仿蝗虫跳跃机器人[７３,７４],如图１２(a)所示．通过单个电机的正反转,利用丝杠螺母在轴向运动以及绳在卷２４第１２期吴业辉等:微小型跳跃机器人:仿生原理,设计方法与驱动技术轴上的卷绕运动,巧妙的实现了锁扣作用下能量加载和释放的循环,如图１２(b )中(ⅰ)~(ⅵ)所示．显然,这种机器人跳跃的实现十分依赖于对绳长㊁螺母移动距离㊁锁钩和足部杆几何关系等进行精确设计和装配．同样,该机器人无法进行跳跃角度㊁高度的调整,且两条绳子无约束地释放可能会造成打结㊁干涉等不稳定现象．该机器人实现了２５倍自身体长的跳跃,达到３．３５米的高度．图１２㊀仿蝗虫机器人[７３,７４]F i g ．１２㊀L o c u s t Ｇi n s pi r e d r o b o t [７３,７４]图１３㊀高度可调的仿生跳跃机器人[７５]F i g ．１３㊀B i o n i c j u m p i n g r o b o tw i t ha d j u s t a b l eh e i gh t [７５]M a 等人提出一种综合软体动物㊁硬壳跳虫弹跳机理的跳跃机器人[７５],如图１３(a)所示．该机器人采用屈曲镍钛合金板和扭簧作为储能元件,释放机构采用了与J u m p R o A C H 机器人(图８)相似的行星轮系结构,并加入了单向轴承来加强能量释放过程的稳定性,如图１３(c )中右图所示．当电机沿顺时针方向正转时,动力经三个齿轮传递至卷绳齿轮轴,通过卷绕刚性绳拉动机构变形进行储能,整个过程单向轴承处于内外圈滑动状态而不产生阻力;相反,当电机沿逆时针方向反转时,单向轴承锁紧并使行星架与卷绳齿轮轴脱开,卷绳瞬间释放．由于加载量随电机正转圈数而定,因此机器人具备跳跃高度可调的特点．该机器人可以在无壳体状态下达到最高１．５１米的跳跃高度,如图１３(b)所示．对于上述各种接触式闩锁,一般具有简单的结构,常采用挡块㊁凸轮㊁差齿齿轮等方式实现能量的锁定,除了上述行星轮系结构,其它锁定方式下的能量值多为固定不可调整的,同时意味着其控制难度低,常采用开环或者位移闭环进行控制其释放．此外,接触式闩锁存在摩擦损失大㊁释放瞬间冲击大等缺点．图１４㊀仿跳蚤系列机器人．a ．F l e a V １机器人[３３,６６];b ．F l e a V ２机器人[３３];c ~d ．F l e a V ３机器人[７８];e ~f ．水面跳跃机器人[２６]F i g ．１４㊀F l e a Ｇi n s pi r e d r o b o t s ．a ．F l e a V １R o b o t [３３,６６];b ．F l e a V ２R o b o t [３３];c ~d ．F l e a V ３R o b o t [７８];e ~f ．R o b o t j u m p i n g onw a t e r [２６]基于跳蚤体内的扭矩反转机构[１０],N o h 等人提出一种具有非接触式闩锁的仿跳蚤跳跃机器人F l e a V １[３３,６６,７６],如图１４(a)所示．利用三根形状记忆合金弹簧来模拟图１０(c)所示的伸肌㊁触发肌和屈３４动㊀力㊀学㊀与㊀控㊀制㊀学㊀报２０２３年第２１卷肌,当受拉弹性元件(伸肌)与所连杠杆处于重合位置时能量存在极值,利用负刚度特性可以产生越过重合点后的爆发式运动,实现了快速 突跳(S n a pＧt h r o u g h) 的特征[７７]．基于此原理该团队还设计了其他形式的跳跃机器人F l e a V２㊁F l e aV３[２６,３３,７８],如图１４(b)~(d)所示,通过简化S MA的数量来实现更高的跳跃高度(４０倍自身高度),并通过结合超疏水喷涂工艺来模拟水黾在水面起跳的现象[图１４(e)Ｇ(f)],在陆地和水面分别可以实现３０和１８厘米高的跳跃能力．较轻的机器人也存在一定缺点,如引入电池等额外负载时其跳跃高度将受到严重影响[７９];同样,由于结构过于简单,此类机器人在连续跳跃㊁改变方向和高度等方面还具有挑战性,这些问题均会对机器人的实际应用产生限制．采用同样原理的还有Z h a k y p o v等人提出的仿陷阱颚蚁跳跃机器人[５７,８０],该机器人可实现爬行㊁翻滚㊁垂直跳跃㊁定向跳跃等多运动模式,最高跳跃１４厘米,达到自身高度的２．５倍,结构如图１５所示．三足的设计不仅增强了机器人的跳跃能力,还帮助机器人实现跳跃方向的选择．此外,通过将电路设计㊁柔顺机构设计与电路板进行集成,完成了机器人的快速㊁轻量化制造．图１５㊀仿陷阱颚蚁多模式运动微型机器人[５７,８０]F i g．１５㊀T r a pＧj a wＧa n tＧi n s p i r e dm u l t iＧl o c o m o t i o nm i l l i r o b o t[５７,８０]为了提高机器人的敏捷性,H a l d a n e等人模仿了夜猴㊁青蛙的跳跃机制,提出一种仿夜猴跳跃的机器人S a l t o[２５,８１]．该机器人采用串联驱动器和E MA结合的方式,以增大机械效益在跳跃后与跳跃前的比值为目标,对机器人几何构型和重量分布进行优化,增大了串联弹性元件在跳跃初期能量存储[６３]．机器人不仅实现了稳定的连续跳跃运动,还具备跳跃高度可调㊁空中姿态调整的能力,可以完成类似于跑酷运动中 蹬墙跳 的高难度动作,这也进一步扩大了自身运动范围,最终使S a l t o实现了夜猴跳跃敏捷度的７８％,成为目前垂直跳跃敏捷程度最高的机器人[２５]．图１６㊀S a l t o系列机器人[２５,８１]．a．S a l t o;b．S a l t oＧ１PF i g．１６㊀S a l t o s e r i e s r o b o t s[２５,８１]．a．S a l t o;b．S a l t oＧ１P在以上非接触式闩锁中,通过与柔顺机构相结合的方式(图１４和图１５)完成 运动－储能－体化 ,进而实现轻量化设计,同时具有无摩擦㊁释放瞬间冲击小等优点[８２Ｇ８４];由于依靠几何上的临界位置进行释放,该类型机器人往往采用开环的方式控制,同时也带来结构相对复杂的问题．此外,该类型机构在释放阶段的行程占比高于接触式闩锁,限制了释放的瞬时功率,同时也获得更小的冲击．对于可变机械效益机构结合串联弹性元件构成的非接触式闩锁(图１６),驱动器直接做功在跳跃运动过程中起重要作用,适用于跳跃周期小的连续型跳跃机器人,也因此更依赖动力学模型来计算机器人的能量释放效果,如对于S a l t o机器人而言,一定范围内提高其驱动器运动加速度可获得更高弹性储能以提高其跳跃高度．表１㊀接触式与非接触式闩锁性能对比T a b l e１㊀P e r f o r m a n c e c o m p a r i s o nb e t w e e n c o n t a c t a n dn o nＧc o n t a c t l a t c h e s性能对比接触式闩锁非接触式闩锁释放速度快慢瞬时冲击大小摩擦阻力大小轻量化潜力小大动力学模型简单复杂能量大小控制静态,易动态,难２．３㊀跳跃机器人着陆缓冲功能跳跃机器人在追求较高跳跃目标的同时,着陆４４。

第28卷　增刊2002年12月自　动　化　学　报ACT A AU T OM A T ICA SIN ICA V ol.28,Suppl.Dec.,2002我国特种机器人发展战略思考王树国 付宜利(哈尔滨工业大学机器人研究所　哈尔滨　150001)(E -m ail:meylfu@)摘　要　特种机器人与工业机器人相比在理论基础、技术特征和应用领域等方面有显著特点,被列入许多国家的发展研究计划,吸引了世界众多科学家从事这方面的研究.特种机器人已成为当今国际自动化技术发展的重要方向.文中介绍了特种机器人的国内外发展现状和趋势,探讨了特种机器人的特点、共性技术和基本科学问题,并对我国特种机器人发展战略提出了若干建议.关键词　特种机器人,发展战略,自动化,中国中图分类号　T P 224ON ADVANC ED ROBOT DEVELOPMENT STRATEGY OF CHINAWANG Shu-Guo FU Yi-Li(Institute of R obot ,H ar bin I nstitu te of Te chnology ,H arbin 150001)(E -m ail:meylfu@)Abstract Compared wit h industrial robot s ,advanced robot ,which is list ed in high technology development programs of many developed count ries,presents unique characteristics on t heory basis,techniques and applicat ion f ields.At t ent ion of scientist s all over the w orld has been f ocused on advanced robot -relat ed research that has become one of t he most import ant research topics in aut omat ion.In this article,af ter an overview of development and trends in advanced robots,t heir design and applicat ions,common technologies and f oundational science problems are discussed,and suggest ion on advanced robot development st rat egy f or China is given.Key words A dvanced robot ,development st rat egy,aut omat ion,China1　特种机器人研究的应用与科学意义工业机器人作为生产自动化设备的典型代表,在制造业获得了巨大的成功.进入20世纪80年代以来,机器人技术在信息技术、控制技术、人工智能和传感技术等迅速发展的支持下,已远远超出了服务于制造业的范围,被广泛地应用于非制造领域.在非制造领域应用机器人已成为当今国际自动化技术的重要发展方向.目前国际上对非制造领域机器人(也称为advanced robot,我国称为特种机器人)的研究和开发非常活跃.特种机器人技术主要研究非制造业应用并服务于人类的各种先进机器人及其相关高技术,特种机器人是替代人在危险、恶劣环境下作业必不可少的工具,可以辅助完成人类无法完成的如空间与深海作业、精密操作、管道内作业等的关键技术装备.与工业机器人相比特种机器人通常在非结构环境下自主工作,更多依赖其对环境信息的获取和智能决策能力.因此特种机器人更强调感知、思维和复杂行动能力,比一般意义上的机器人需要更大的灵活性、机动性,具有更强的感知能力、决策能力、反应能力以及行动能力.特种机器人从外观上也远远脱离了最初工业机器人所具有的形状.特种机器人融合了更多学科的知识,如机构学、控制工程、计算机科学、人工智能、微电子学、光学、传感技术、材料科学、仿生学等,因此,特种机器人的研究不仅能促进本学科的发展,还可带动其它学科的进步.特种机器人研究特别强调智能性和对环境的适应性,使其具有更广阔的应用领域.特种机器人在空间及海洋探索、农业及食品加工、采掘、建筑、医疗、服务、交通运输、军事和娱乐等领域都具应用前景.针对各个领域的应用特点,科学技术人员正在研制各种类型的特种机器人,如空间机器人、水下机器人、服务机器人、微操作机器人、仿人机器人、医疗机器人、军用机器人等.这些特种机器人将更加符合各应用领域的特殊要求,其功能和智能程度也大大超出了工业机器人的范围,使机器人技术呈现出更加广阔的发展空间.随着陆地资源逐渐枯竭,深海开发已成为各国的重要战略目标.深海矿产资源的勘探与开采,是近几年国际激烈竞争的焦点之一.作为一种高技术手段水下机器人在海洋石油开发、海洋科学研究、海底矿藏勘测开发、海底打捞救生等方面具有重要的意义.21世纪我国的海洋多金属结核的开采工作将进入中试阶段,除了用于大洋勘测,中试采矿装置的安装、运行、维护、修理都急需多种水下机器人.作业型AUV 在海洋科学研究中,可用来进行海洋科学的各种测量、观测、监视、布放和收回仪器.在军事领域作业型AUV 与其他潜水装备互相配合,可在防险救生中发挥特有的作用.在我国经济结构调整发展战略中,已将城市化确定为产业增长的重要拉动力.城市化进程的加快将有效地带动一系列传统产业和新兴产业的高速增长.其中加大城市地下空间开发利用的力度,将城市相关基础设施建造在地下,已成为未来城镇建设的重要方向.城市各类管网的地下化和城市交通的立体化将需要开发盾构机器人与地下管线非开挖技术和装备,并将形成新兴产业.随着人民生活水平的提高,我国对医用机器和服务机器人的需要已提到议事日程.医用机器人的研究,不仅对常规医疗方面将带来一系列的技术变革,对临床或家庭的护理及康复工程等方面的发展产生深远的影响.生物技术将是21世纪重点发展的领域,它将改变医药业、农业、制造业等行业的面貌,其产业发展前景十分可观.在生物技术发展过程中,基于机器人技术的智能化微操作装置正在发挥作用.本世纪我国在无人试验飞船成功飞行的基础上,在航天技术和重大工程方面将有更大的发展.不仅要实现载人航天飞行,而且在空间探测方面,将实现月球探测,积极参与国际火星探测活动,使我国的空间探测技术上升到一个更高的水平.空间技术的发展需要机器人技术提供装备和服务.此外,在军事领域,一场新军事革命正在兴起,为适应未来高技术环境下71增刊　王树国等:我国特种机器人发展战略思考72自 动 化 学 报　28卷的作战,需要用特种机器人及自动化技术提供新一代智能化武器装备.总之,特种机器人技术在21世纪将有一个快速的发展,其应用领域及其产品市场将是不可预测的,其巨大的发展潜力将会对人类生活及社会经济发展产生巨大的推动.同时特种机器人也将是21世纪自动化科学与技术的集中体现,是最高意义上的自动化,是当今国际自动化技术发展的重要方向;特种机器人的研究必将促进自动化科学与技术的发展.2　特种机器人研究国内外发展动态与现状特种机器人技术综合了多学科的发展成果,代表了高技术的前沿发展,它在人类生活应用领域的不断扩大,正引起国际上重新认识机器人技术的作用和影响.正因为如此,研究和发展特种机器人技术一直受到世界各国的重视,许多国家都把特种机器人技术列入本国的高技术发展计划或国家的关键技术进行研究和开发.如美国的“国家关键技术”、“商业部新兴技术”和“国防部和能源部关键技术”计划,欧共体的“尤里卡计划”和“信息技术研究发展战略计划”,新加坡、韩国、巴西等发展中国家都有相应的计划内容.目前,国际上在如下领域展开特种机器人技术研究.1)水下机器人.包括有缆水下机器人与无缆水下机器人,其中无人无缆水下机器人将是主要的发展方向,并向远程化深海和作业型发展.2)空间机器人.包括舱内作业与舱外作业机器人、星际探索机器人、空间飞行器检测和维修遥控自由飞行空间机器人等.随着空间探索、开发与利用的不断深入,还会不断出现新型的空间机器人.3)工程及建筑机器人.主要应用于矿山采掘业,其中也包括各种地下输油、输气、输水管道监测维修用的爬管机器人、隧道掘进机器人、高层建筑用顶升机器人系统、顶制件安装机器人、室内装修机器人、地面磨光机器人、擦玻璃机器人等.4)医用机器人.医疗机器人是越来越受到关注的机器人应用前沿方向之一,包括外科手术机器人、生物体内诊疗微机器人系统;眼科及神经显微外科手术机器人;胸脏器官、泌尿系统及脑外科手术机器人等.目前机器人辅助外科手术及虚拟医疗手术仿真系统为研究重点.5)微机器人.近年来世界各发达国家在微机电系统的研究开发方面取得了令人瞩目的成果.专家们预测2010年微机器人的销售额将达到200亿美元.6)农业机器人.包括耕作机器人、农药喷洒机器人、收获及管理机器人、搬运机器人、剪羊毛机器人、挤牛奶机器人、草坪修剪机器人等.7)军用机器人.主要用于侦察、作战、保安、排雷等方面.8)服务机器人.主要用于家庭生活类服务及公共场所类服务,如老年人护理、残疾人护理、导盲、导购、导游等方面用机器人.9)核工业用机器人.主要用于核工业设备的监测与维修.10)娱乐机器人.娱乐、玩具机器人在本世纪会形成巨大的产业.现在国外已开发出采矿机器人、坑道水泥喷涂机器人、高层建筑清扫机器人、爬壁检测及喷漆机器人、管内爬行机器人、管外爬行机器人、生物医学用微型机器人、农业及林业用机器人、核工业机器人、深海机器人、空间机器人、军用机器人等等.生产特种机器人的公司的数量迅速增加,特种机器人产业已在国外形成,并对社会与生产力的发展发挥了重大的推动作用.我国在“863”计划支持下,在水下机器人、服务机器人、微操作机器人、爬壁机器人、管道机器人、军用机器人、仿人机器人和智能化智能机械等方面开展研究,培养了队伍,取得了一批研究成果,在某些技术方面达到了国际先进水平.但从总体上与国际发达国家相比还有较大差距,没有形成规模产业,自主知识产权的成果相对较少,对特种机器人基本科学问题和技术研究不足,跟踪研究多,自主创新少.3　特种机器人的共性技术与基本科学问题3.1　特种机器人的共性技术特种机器人大都工作在非结构性环境中,在现在及可以预见的将来,人机遥控加上局部自治,仍将是一种主要的控制方式.操作者和机器人可能在同一环境中,也可能分布在两处.分布在两处时,其联系的通信时间,在空间及深海中,可能长达分的数量级,而工作的环境,在大多数的情形下又是未知的.感知、规划、行动和交互技术是特种机器人的共性技术.1)遥控及监控技术机器人高水平的半自治功能;多机器人和操作者之间的协调控制;通过网络建立大范围内的机器人遥控系统;对有时延的环境,克服时延所造成的控制上的困难;通过事先对可能出现的情况及对策的详细研究,进行局部自治控制等.2)人机接口近几年来,在包括虚拟环境的人机接口方面的研究工作非常活跃,开发出各式各样的输入和输出装置,如三维鼠标、数据手套、快门眼镜、头盔等,各种具有更好性能的临场感方法相继被提出来,如具有类似人的大小的手、臂和双眼视觉系统等.利用临境技术建立机器人工作环境,让操作者身临其境地进行操作.目前在利用多种传感器的信息动态实时地建立环境方面,有很多问题有待研究.3)多传感器系统基于多传感器信息的获取、融合、理解、处理和控制技术将是在未知环境中实现具有高度灵活性及高鲁棒性行为的机器人的关键.4)导航和定位问题对移动式机器人来说,导航和定位是两个重要的问题.对陆上机器人来说,里程计、方向陀螺等都比较成熟;对水下短程机器人来说,定位可以利用短基线、超短基线或长基线,而用GPS 来标定,导航可采用基于方向陀螺和多普勒测速组成递推算法,并按测定的位置定时校正;对水下大航程机器人来说,目前还没有好的方法.由于长距离的水下通信问题没有解决,而海底图匹配、海底图的地理特征很难确定,试验过的方法很多,如地貌匹配、重力场、重力梯度场等都不理想,而且代价太大,目前从理论上还没有好办法.完全未知空间环境探测机器人定位也是正在探索的问题.5)机器智能特种机器人大多对其智能程度有更高的期望,满足其在未知或部分未知环境中自主作业的需要.特种机器人的智能可以体现在其工作的各个方面,包括诸如对环境的感知、信息73增刊　王树国等:我国特种机器人发展战略思考74自 动 化 学 报　28卷的处理、行为决策、与人环境的协调和自学习等.传统的符号推理系统、模糊逻辑、神经网络、遗传算法等都是人们在实现人工智能方面的努力.这方面的研究还远没有达到人类期望的目标.6)虚拟机器人技术许多特种机器人,在用于空间、水下、地面、地下、农业和食品加工、消防和救援、医疗和护理、休闲和娱乐等时,遥控不失为一种主要手段.基于多传感器、多媒体和虚拟现实、临场感的虚拟遥操作和人机交互,将成为共同需要发展的一项技术.7)网络机器人技术通讯网络技术的发展完全能够将各种机器人连接到计算机通讯网络上,并通过网络对机器人进行有效的控制.其中包括网络接口装置、众多信息组的压缩与解压方法及传输方法研究.8)多智能体协调控制技术包括用于实现决策和操作自主的有多智能体组成的群体行为控制技术.微型和微小机器人技术包括微机构、微传感及相应的微系统集成技术等.软机器人技术主要研究在未来众多的人与机器人共存的环境中,机器人对人的安全保护性技术.3.2　特种机器人研究基础关键科学问题针对21世纪我国发展特种机器人技术的战略性需求,结合国际机器人与自动化学科的前沿发展,应重点研究如下科学问题:特种机器人学中的拟人智能技术研究;未知环境信息获取、理解和控制的新机制新理论;复杂环境中的机器人自主工作新方法与新理论;机器人精确自定位新手段与新技术;生态机器人学研究,即研究生态学原理在特种机器人设计中的应用;仿人机器人运动学、动力学控制新方法;特种机器人的人机交互问题研究,包括监控技术、通讯技术和远程操作技术.4　我国特种机器人发展战略建议4.1　借鉴工业机器人发展历史,以需求牵引为原则,实现跨越式发展工业机器人的兴起和发展是制造业为提高产品质量、缩短产品制造周期和提高劳动生产率而推动的.回顾20世纪50年代以来制造业的发展历程,工业机器人的发展源于制造业自动化发展需求,成为制造自动化生产线的关键设备.工业机器人的发展推动了制造思想进步,同时工业机器人技术及系统也获得了飞速发展.因此特种机器人的发展也要以需求为原则,充分研究我国非制造业自动化的实际需要,确定特种机器人的研究领域和需发展的关键技术,有所为有所不为,发展具有我国特色的特种机器人技术.特种机器人研究一方面不能回避制约国家经济建设与产业化发展中的自动化关键技术问题,另一方面又要抢占战略性关键技术和未来可能形成新的经济增长点的竞争前核心技术,因此我国特种机器人研究既要满足当前国民经济发展的需要又要有前瞻性.前瞻性研究是企业掌握市场主动权的前提,是国家竞争力的源动力,因此前瞻性研究是高技术研究的真正内涵.在进行特种机器人研究时我们既要学习国外特别是西方发达国家的先进技术和先进经验,又要注意知识的原创新,掌握战略必争关键技术和竞争前技术,在基础和关键技术研究方面具有相当大的广度和深度,实现我国特种机器人的跨越式发展.4.2　特种机器人研究以增强国家综合经济实力、保证国家安全、提高国际竞争力为己任1)服务于非制造业自动化的发展特种机器人种类多,应用领域广,自动化程度高,因此比传统的工业机器人有更广阔的前景.立足于国民经济发展的需要,开展特种机器人研究.今后10年我国基础设施建设处于高速发展阶段,对智能化工程机械有巨大需求,在筑路、管道作业、石油钻井等方面研制机器人化智能机械满足国家经济建设发展的需要.特种机器人在21世纪的服务行业自动化过程中也大有作为,医疗机器人、娱乐机器人、康复机器人、导游机器人和家庭服务机器人等都值得关注和研究.2)满足信息技术、生物医学技术和纳米技术发展的需要提供高精度、高柔性和高智能的自动化辅助研究设备,开发面向光学工程的光纤对接微操作机器人以及微电子机械器件的加工设备,研制面向生物医学工程的微操作机器人,用于生物芯片制造的精密微操作系统.3)围绕国防发展和新资源开发的需要以提高我国国防和军事实力、加强国家安全和国际地位、充分利用海洋与宇宙资源为目的,研制军用机器人、空间机器人、深海作业机器人、载人潜器等.4.3　加强组织管理及重视国际合作我国特种机器人的研究在某些方面起步较早,拥有多项世界先进水平的研究成果.我们只要定准主攻方向、集中目标、加强管理,采用滚动资助方法,组织有条件和研究基础的研究单位,对特种机器人关键技术进行协作攻关;同时促进科研单位和企业的结合、注意关键技术与应用技术的结合,建立有效机制大力实施特种机器人应用工程,会形成拥有自己知识产权的特种机器人研究成果和应用产业.在强调独立自主的研究基础上,还要学习国外的先进技术,特别是重视国际合作.美国、日本和欧盟各国都在机器人研究方面取得了成果,也积累了许多经验,加强国际合作可加速我们的研究速度,提高研究层次,取得赶超世界先进水平的重大成果.事实上,西方各国在特种机器人的研究上也相互取常补短,如美国国防部领导的“联合机器人计划”就联合了加拿大、英国、日本、法国、德国和以色列等进行用于后勤给养运输、武器操作和复杂环境下侦察等的战场环境下的自主机器人系统.国际合作可以多种方式进行,如加入国际机器人组织、学术交流、人员互访、项目合作,官方或学者个人形式.4.4　注意相关学科的相互交叉与渗透由于特种机器人的应用环境复杂且不确定、自主工作能力和智能性要求更高,在研究过程中不可避免的涉及到更多的学科,除计算机技术、传感技术、微电子理论、控制论、信号处理等,还会跨越系统分析、概率统计、仿生学、感知论、虚拟增强现实技术、微机构学、医学、光学、新材料和生物遗传等,因此要发挥多学科的优势,注意学科间的交叉与渗透,联合攻关,协作研究,使特种机器人的研究不断取得新的成果,并真正应用到实际中去.参考文献1国家“863”计划智能机器人主题专家组.我国特种机器人发展战略研讨.200175增刊　王树国等:我国特种机器人发展战略思考76自 动 化 学 报　28卷2徐国华,谭　民.移动机器人的发展现状及其趋势.机器人技术与应用,2001,(3):14～213龚振邦.从国际大环境出发在我国机器人技术和产业发展战略上的若干思考.机器人,1999,21(6):474～4794刘进长.世纪之交我国机器人发展战略研究.机器人技术与应用,2000,(3):1～45陈佩云,金茂青,曲忠苹.我国工业机器人发展现状.机器人技术与应用,2001,(1):2～56贾培发,王全福.团结奋斗努力实现中国机器人产业化.机器人技术与应用,2000,(6):2～67谈士力.面向21世纪特种机器人技术的发展.世界科学,2001,(6):24～258Jorge M oraleda,Anibal Ollero,M ar iano Orte.A robotic s ystem for internal inspection of w ater p ipelin es.I E EE Robotics and A utomation M ag az ine,1999,6(3):30～419Caccia M,Bono R,Bruzzon e G,Ver uggio G.Variable-configuration U UVs for marine science application s.I E EE Robotics and A utomation M ag az ine,1999,6(2):22～3210Hiroshi Is higuro,T ets uo Ono,M ichita Imai.Robovie:an interactive humanoid robot.Ind ustrial R obots,2001,28(6):498～50411I James Wright.T echnology requ irements for robotic s urger y.I ndustr ial R obots,2001,28(5):392～39712Lopes L S,Connell J H,Dario P,M urphy R.Sen tience in robots:ap plications and challenges.I EE E I ntelligent Systems,2001,16(5):66～6913Chiaki T suzu ku.T he trend of robot technology in s em i-conductor and L CD indu stry.I ndustr ial Robots,2001,28(5):406～41414Rolf S chraft,Birgit Gr af,Andreas Tr aub.A mobile robot platform for assis tance and enter tainment.I ndustr ial Robots,2001,28(1):29～3515Dru in A,Hendler J,Kaufman n.Robots for kids.I EE E I ntelligent Sy stems,2001,16(1):88～9016Wei Ye,Vaughan R T,Sukh atme G S.E valu ating con tr ol strategies for wir eless-netw orked robots us ing an integrated rob ot and netw or k sim ulation.IE EE I nternational Conf er ence on Robotics and Au tomation,2001,3:2941～294717Brook s R A.In telligence w ith out reas on.I J CAI,1991:569～59518Skew is T,Lu melsky V.Exper imen ts w ith a m ob ile rob ot oper ating in a cluttered unk nown environment.Journal of Robotics Systems,1994,11(4):281～30019Bor ens tein J,Everett H R,Feng L,Wehe D.M obile robot positioning:sensors an d techniques.J our nal of R obotic Systems,1997,14(4):231～249王树国　博士,教授,博士生导师,哈尔滨工业大学校长,曾任国家“863”计划智能机器人主题专家组副组长、国家自然科学基金委员会自动化学科评审组成员、中国自动化学会机器人专业委员会理事.研究方向为特种机器人技术、虚拟现实技术等.付宜利　博士,教授,哈尔滨工业大学现代生产技术中心副主任,I EEE会员.主要研究方向为机器人运动规划方法、移动机器人技术、虚拟装配技术等.。

竞速：机器人超越人类作者：暂无来源：《世界科学》 2019年第12期来见识一下即将超越世界上跑步最快的人类的超级机器人吧！编译胡德良你可能将机器人想象为笨拙的物体——呆板、沉闷，甚至像步履蹒跚的老人，似乎需要利用咖啡因提神。

但是，机器人即将大踏步前进起来。

你只需问一问哥伦比亚大学工程学教授霍德·利普森（Hod Lipson），就知道这一切了。

利普森在《自然》（Nature）杂志上写道：“幼小的动物在田野上飞奔，攀爬树木，跌倒后可以立即灵巧地站起来——机器人也将效仿这些动物了。

”利普森是正确的，新一代快速机器人有望最终超越在2020年东京奥运会上参加赛跑的运动员。

基于控制论、具有竞争力的著名机器人包括：麻省理工学院（MIT）占主导地位的“猎豹”（Cheetah）、波士顿动力公司的“佩特曼”（Petman）和“汉德尔”（Handle）、密歇根机器人公司的“梅布尔”（Mable）以及南非开普敦大学的“巴莱卡”（Baleka）。

此外，佛罗里达大学人类与机器认知研究所（IHMC）推出了一款无传感器的智能两足机器人，俗称“平面椭圆跑步机器人”（PER）。

Verge网站将PER描述为“全机械”，这意味着只需要较少的技术就能够让它保持直立。

PER的核心是一个单独的发动机以椭圆形运动方式驱动其腿部，这有助于使它具备稳定性，避免向前或向后跌倒。

扭力弹簧为PER的腿部提供额外的动力，使其更加稳定。

PER的动态图形完美，不受任何数字运算处理器的影响。

在跑步机上，这台步态流畅的跑步机器人每小时可跑12英里，这听起来似乎很快。

毕竟，有史以来最快的马拉松比赛不过是以13英里/小时的速度展开的，那是肯尼亚的埃里德·基普乔格（Eliud Kipchoge）于2018年在柏林参赛时的速度。

IHMC研制的快跑者机器人EPFL生物机器人实验室制造的蜥蜴机器人《国家地理》（National Geographic）杂志报道说：IHMC的机器人先驱“希克斯跑步者”（HexRunner）创下了32.2英里/小时的世界纪录，超越了先前由MIT四足先驱“猎豹”创下的28.3英里/小时的纪录。

一种可提高和改善步行功能的装置：动力下肢外骨骼系统的设计及应用王一吉;李建军【摘要】Lower extremity exoskeleton system is a kind of human-machine robot, which combines the artificial intelligence with the power of mechanism. Recent years, the field of lower extremity exoskeleton robots have rapidly evolved and development of relevant technologies have dramatically increased these robots available for facilitating human walking function that could only be imagined a few years ago. Some technologies are so new that they lack the scientific evidence that would justify their use in the real setting. This paper presents an over view of design configurations, control methods and simulation test used for lower extremity exoskeleton robots. Further research efforts are required in order to incorporate many of the new technologies described in this review to promote the development of the lower extremity exoskeleton robots.%下肢外骨骼系统是一种结合了人工智能和机械动力装置的机器人。

ｄｏｉ：１０．３９６９／ｊ．ｉｓｓｎ．１００３－３１０６．２０２３．０９．０１９引用格式：李欢，卢延荣．随机博弈下的工业机器人物联网主动防御研究［Ｊ］．无线电工程，２０２３，５３（９）：２１３５－２１４２．［ＬＩＨｕａｎ，ＬＵＹａｎｒｏｎｇ．ＤｅｓｉｇｎｏｆＮｅｔｗｏｒｋＳｅｃｕｒｉｔｙＯｐｔｉｍａｌＡｔｔａｃｋａｎｄＤｅｆｅｎｓｅＳｃｈｅｍｅＵｎｄｅｒＲａｎｄｏｍＡｔｔａｃｋａｎｄＤｅｆｅｎｓｅＧａｍｅＭｏｄｅｌ［Ｊ］．ＲａｄｉｏＥｎｇｉｎｅｅｒｉｎｇ，２０２３，５３（９）：２１３５－２１４２．］随机博弈下的工业机器人物联网主动防御研究李　欢１，卢延荣２（１．绵阳职业技术学院智能制造学院，四川绵阳６２１０５４；２．兰州理工大学电气工程与信息工程学院，甘肃兰州７３００５０）摘　要：稳定而可靠的工业机器人可提升生产质量和制造效率，并节约成本，由此，提出随机攻防博弈下的工业机器人物联网主动防御方法。

通过分析工业物联网安全攻防对立与依赖的复杂特点，搭建工业机器人物联基础网攻防系统，并分析攻击模型和种类；进而搭建工业机器人物联网的随机攻防博弈模型，通过模型假定和马尔可夫判断模式，实现状态转移概率分析，并选取沙普利迭代算法来获取随机攻防博弈模型的结果。

设置工业机器人物联网攻防实验环境和模拟工业物联网攻击，获得随机攻防防御策略组合，并完成成本测算。

实验结果表明，所提出的随机攻防博弈策略可化被动防御为主动，并在成本较小的情况下，达到有效的工业机器人物联网安全防护。

关键词：网络安全；主动防御；博弈；马尔可夫；随机中图分类号：ＴＰ２４２．２文献标志码：Ａ开放科学（资源服务）标识码（ＯＳＩＤ）：文章编号：１００３－３１０６（２０２３）０９－２１３５－０８ＤｅｓｉｇｎｏｆＮｅｔｗｏｒｋＳｅｃｕｒｉｔｙＯｐｔｉｍａｌＡｔｔａｃｋａｎｄＤｅｆｅｎｓｅＳｃｈｅｍｅＵｎｄｅｒＲａｎｄｏｍＡｔｔａｃｋａｎｄＤｅｆｅｎｓｅＧａｍｅＭｏｄｅｌＬＩＨｕａｎ１，ＬＵＹａｎｒｏｎｇ２（１．ＳｃｈｏｏｌｏｆＩｎｔｅｌｌｉｇｅｎｔＭａｎｕｆａｃｔｕｒｉｎｇ，ＭｉａｎｙａｎｇＰｏｌｙｔｅｃｈｎｉｃ，Ｍｉａｎｙａｎｇ６２１０５４，Ｃｈｉｎａ；２．ＣｏｌｌｅｇｅｏｆＥｌｅｃｔｒｉｃａｌａｎｄＩｎｆｏｒｍａｔｉｏｎＥｎｇｉｎｅｅｒｉｎｇ，ＬａｎｚｈｏｕＵｎｉｖｅｒｓｉｔｙｏｆＴｅｃｈｎｏｌｏｇｙ，Ｌａｎｚｈｏｕ７３００５０，Ｃｈｉｎａ）Ａｂｓｔｒａｃｔ：Ｓｔａｂｌｅａｎｄｒｅｌｉａｂｌｅｉｎｄｕｓｔｒｉａｌｒｏｂｏｔｓｃａｎｉｍｐｒｏｖｅｔｈｅｐｒｏｄｕｃｔｉｏｎｑｕａｌｉｔｙａｎｄｍａｎｕｆａｃｔｕｒｉｎｇｅｆｆｉｃｉｅｎｃｙ，ａｎｄｓａｖｅｔｈｅｃｏｓｔ．Ｔｈｅｒｅｆｏｒｅ，ａｎａｃｔｉｖｅｄｅｆｅｎｓｅｍｅｔｈｏｄｆｏｒｉｎｄｕｓｔｒｉａｌｒｏｂｏｔＩｎｔｅｒｎｅｔｏｆＴｈｉｎｇｓ（ＩｏＴ）ｕｎｄｅｒｒａｎｄｏｍａｔｔａｃｋａｎｄｄｅｆｅｎｓｅｇａｍｅｉｓｐｒｏｐｏｓｅｄ．ＴｈｅｃｏｍｐｌｅｘｃｈａｒａｃｔｅｒｉｓｔｉｃｓｏｆｔｈｅｏｐｐｏｓｉｔｉｏｎａｎｄｄｅｐｅｎｄｅｎｃｅｏｆｉｎｄｕｓｔｒｉａｌＩｏＴｓｅｃｕｒｉｔｙａｔｔａｃｋａｎｄｄｅｆｅｎｓｅａｒｅａｎａｌｙｚｅｄ，ａｎｉｎｄｕｓｔｒｉａｌｒｏｂｏｔｂａｓｉｃＩｏＴａｔｔａｃｋａｎｄｄｅｆｅｎｓｅｓｙｓｔｅｍｉｓｅｓｔａｂｌｉｓｈｅｄ，ａｎｄｔｈｅａｔｔａｃｋｍｏｄｅｌｓａｎｄｔｙｐｅｓａｒｅａｎａｌｙｚｅｄ．Ｔｈｅｎ，ａｒａｎｄｏｍａｔｔａｃｋａｎｄｄｅｆｅｎｓｅｇａｍｅｍｏｄｅｌｏｆｉｎｄｕｓｔｒｉａｌｒｏｂｏｔＩｏＴｉｓｂｕｉｌｔ，ａｎｄｔｈｅｍｏｄｅｌａｓｓｕｍｐｔｉｏｎｓａｎｄＭａｒｋｏｖｊｕｄｇｍｅｎｔｍｏｄｅａｒｅｇｉｖｅｎｔｏｒｅａｌｉｚｅｔｈｅｓｔａｔｅｔｒａｎｓｉｔｉｏｎｐｒｏｂａｂｉｌｉｔｙａｎａｌｙｓｉｓ．Ｓｈａｐｌｅｙｉｔｅｒａｔｉｖｅａｌｇｏｒｉｔｈｍｉｓｓｅｌｅｃｔｅｄｔｏｏｂｔａｉｎｔｈｅｒｅｓｕｌｔｓｏｆｔｈｅｒａｎｄｏｍａｔｔａｃｋａｎｄｄｅｆｅｎｓｅｇａｍｅｍｏｄｅｌ．Ｆｉｎａｌｌｙ，ｔｈｅｅｘｐｅｒｉｍｅｎｔａｌｅｎｖｉｒｏｎｍｅｎｔｏｆｉｎｄｕｓｔｒｉａｌｒｏｂｏｔＩｏＴａｔｔａｃｋａｎｄｄｅｆｅｎｓｅｉｓｓｅｔｕｐｔｏｓｉｍｕｌａｔｅｉｎｄｕｓｔｒｉａｌＩｏＴａｔｔａｃｋａｎｄｏｂｔａｉｎｔｈｅｒａｎｄｏｍａｔｔａｃｋａｎｄｄｅｆｅｎｓｅｓｔｒａｔｅｇｙｃｏｍｂｉｎａｔｉｏｎ，ａｎｄｔｈｅｃｏｓｔｉｓｃａｌｃｕｌａｔｅｄ．Ｔｈｅｅｘｐｅｒｉｍｅｎｔａｌｒｅｓｕｌｔｓｓｈｏｗｔｈａｔｔｈｅｒａｎｄｏｍａｔｔａｃｋａｎｄｄｅｆｅｎｓｅｇａｍｅｓｔｒａｔｅｇｙｐｒｏｐｏｓｅｄｃａｎｔｒａｎｓｆｏｒｍｐａｓｓｉｖｅｄｅｆｅｎｓｅｉｎｔｏａｃｔｉｖｅｄｅｆｅｎｓｅ，ａｎｄａｃｈｉｅｖｅｅｆｆｅｃｔｉｖｅｔｈｅｓｅｃｕｒｉｔｙｐｒｏｔｅｃｔｉｏｎｏｆｉｎｄｕｓｔｒｉａｌｒｏｂｏｔＩｏＴａｔｌｏｗｃｏｓｔ．Ｋｅｙｗｏｒｄｓ：ｎｅｔｗｏｒｋｓｅｃｕｒｉｔｙ；ａｃｔｉｖｅｄｅｆｅｎｓｅ；ｇａｍｅ；Ｍａｒｋｏｖ；ｒａｎｄｏｍ收稿日期：２０２３－０６－０５基金项目：国家自然科学基金（６２００１１９８）；甘肃省青年科技基金计划（２１ＪＲ７ＲＡ２４６，２１ＪＲ７ＲＡ２４７）ＦｏｕｎｄａｔｉｏｎＩｔｅｍ：ＮａｔｉｏｎａｌＮａｔｕｒａｌＳｃｉｅｎｃｅＦｏｕｎｄａｔｉｏｎｏｆＣｈｉｎａ（６２００１１９８）；ＹｏｕｔｈＳｃｉｅｎｃｅａｎｄＴｅｃｈｎｏｌｏｇｙＦｏｕｎｄａｔｉｏｎｏｆＧａｎｓｕＰｒｏｖｉｎｃｅ（２１ＪＲ７ＲＡ２４６，２１ＪＲ７ＲＡ２４７）０　引言工业机器人［１］作为推进制造业和现代化生产的重要智能工具，凝聚了国家先进科技水平与生产发展能力。

徐州工程学院教案徐州工程学院教案纸、金刚石刀具超精切削刀具材料：天然金刚石，人造单晶金刚石金刚石的晶体结构：规整的单晶金刚石晶体有八面体、十二面体和六面体，有三根4次对称轴，四根3次对称轴和六根2次对称轴。

图 3-5 砂带磨削示意图图3-6 几种砂带磨削方式砂带磨削特点：1）砂带与工件柔性接触，磨粒载荷小，且均匀，工件受力、热作用小，加工质量好（ R a值可达 0.02μm）。

2）静电植砂，磨粒有方向性，尖端向上，摩擦生热小，磨屑不易堵塞砂轮，磨削性能好。

2. 产品设计中的应用——快速产品开发（RPD）第四节微细加工技术二、微细机械加工1、主要采用铣、钻和车三种形式，可加工平面、内腔、孔和外圆表面。

2、刀具：多用单晶金刚石车刀、铣刀（图3-20）。

铣刀的回转半径（可小到5μm）靠刀尖相对于回转轴线的偏移来得到。

当刀具回转时，刀具的切削刃形成一个圆锥形的切削面。

3、微细机械加工设备微小位移机构，微量移动应可小至几十个纳米。

图 3-11 FANUC型微型超精密加工机床三、微细电加工、线放电磨削法（WEDG）电极线沿着导丝器中的槽以5～10mm/min的低速滑动，可加工圆柱形（图3-12）。

如导丝器通过数字控制作相应的运动，还可加工出各种形图3-12 WEDG工作原理图3-14 电致伸缩微动工作台第五节现代特种加工技术图3-15 激光加工原理图、激光加工特点图 3-16 超声波加工原理图、超声波加工特点及应用适用于加工各种脆性金属材料和非金属材料，如玻璃、陶瓷、半导体、宝石、金刚石等。

可加工各种复杂形状的型孔、型腔、形面。

工具与工件不需作复杂的相对运动，机床结构简单。

被加工表面无残余应力，无破坏层，加工精度较高，尺寸精度可0.05mm 。

加工过程受力小，热影响小，可加工薄壁、薄片等易变形零件。

图3-17水射流加工装置示意图水射流切割加工的应用和发展水射流切割具有切口平整、无毛边、无火花、加工清洁等特点，已用于汽。

基于智能体的生物群体动力学模型研究一、引言生物群体动力学模型，动态演化系统中的模拟与可视化技术之一，是物理学、心理学、生物学、哲学等多学科交叉的研究领域。

它综合运用现代计算机科学、计算方法，以及自然界动态演化的规律与特征相结合，通过模拟大规模群体的行为活动，探索现实世界中的复杂现象与规律。

智能体技术作为该领域的重要技术手段之一，将群体中每个个体作为一个自主、灵活的智能体来处理，并将智能体的集合视为一个生物群体。

在智能体技术的基础上，又出现了基于智能体的生物群体动力学模型，它结合了自然界动态演化的规律与特征，精确模拟了生物群体在不同场景中的行为与活动。

因此，本文的内容主要介绍基于智能体的生物群体动力学模型的研究现状和发展趋势。

二、基于智能体的生物群体动力学模型概述1、生物群体动力学模型基本知识生物群体动力学模型，简称Boids模型，是由Craig Reynolds在1986年首次提出的。

该模型基于对鸟類，魚群等生物大群集合行動的研究而形成。

它的基本思想是将大规模群体的行为活动用简单的规则去描述。

Boids模型主要包括三个方面，即分离、聚集和对齐。

分离：每只Boid会避免与其它Boid碰撞或进入其它的危险区域。

聚集：每只Boid会朝向自己距离较近的Boid群体的质心移动。

对齐：每只Boid会朝向自己距离较近的Boid群体的平均方向移动。

Boids模型的优点在于其简单、易于实现和扩展，并可以模拟多种生物群体的活动规律，例如鸟群、鱼群、羊群等。

但Boids模型也存在一些问题，例如模拟精度较低、计算效率较慢等，限制了其应用范围。

2、基于智能体的生物群体动力学模型的发展历程基于智能体的生物群体动力学模型是Boids模型的升级版。

它采用基于智能体的模式，将群体中每个个体视为一个自主、灵活的智能体进行处理。

每个智能体具有自我判断、自我决策和自我适应的能力，并且可以直接与其它智能体进行交互。

基于智能体的生物群体动力学模型最早起源于人工生命领域。

WHITE PAPERFortiSandbox: Third-generation Sandboxing Featuring Dynamic AI AnalysisExecutive SummaryAs zero-day and unknown attacks continue to grow in numbers and sophistication, successful breaches are taking longer to discover—at a significant cost to businesses. Many first- and even second-generation sandboxing solutions lack key features like robust artificial intelligence (AI) to keep pace with the rapidly evolving threat landscape. A third generation of sandbox devices, however, not only can help detect breaches in a timely manner but also prevent them from happening. FortiSandbox integrates with the broader Fortinet Security Fabric and utilizes the universal MITRE ATT&CK security language for categorizing threat models and methodologies. FortiSandbox applies robust AI capabilities in the form of both static and dynamic behavior analysis to expose previously unseen malware and other threats before a breach can occur. Expanding Breach Impacts Require Better Detection and PreventionA majority (nearly 80%) of organizations report that they are introducing digitalinnovations faster than their ability to prevent successful cyberattacks .1 The vastmajority (92%) of security architects report experiencing at least one intrusion inthe past 12 months.2 And this problem is compounded by the fact that it typicallytakes more than nine months (279 days) for an organization to discover a securitybreach—with an average cost of nearly $4 million in damages per event.3These critical issues beg the question—what can security architects do to improve not only breach detection but prevention as well? For some time now, organizations have successfully relied on sandboxing devices to discover threats (like malware) hidden in network traffic. But over time, cyber criminals have found ways to thwart detection by previous-generation sandboxes—such as encryption, polymorphism, and even their own AI-based codes that give these threats adaptive and intelligent evasion abilities.Subsequently, a new generation of sandboxes was needed to keep pace with these rapidly evolving threats. FortiSandbox offers a third-generation sandbox designed for detection and prevention of breaches caused by the full spectrum of AI-enabled threats—both known and unknown.Fortinet Third-generation FortiSandbox SolutionUnlike previous-generation solutions, a third-generation sandbox must do three things:n n It must utilize a universal security language via a standardized reporting framework to categorize malware techniques.n n It must share threat intelligences across a fully integrated security architecture in order to automate breach protection in real time as threats are discovered.n n It must perform both static and dynamic (behavior) AI analysis to detect zero-day threats.FortiSandbox addresses the problem of multiple, nonstandard security languages for malware reporting through the MITRE ATT&CK framework.4 This broadly adopted knowledge base of adversary tactics and techniques categorizes all malware tactics in an easy-to-read matrix. This, in turn, helps security teams accelerate threat management and response processes.As an integrated part of the Fortinet Security Fabric architecture, FortiSandbox uses three forms of threat intelligence for automated breach detection and prevention. It uses global intelligence about emerging threats around the world via FortiGuard Labs researchers. Second, it shares local intelligence with both Fortinet and non-Fortinet products across thesecurity infrastructure for real-time situational awareness across the organization. Finally, and most importantly, FortiSandbox applies true AI capabilities—including both static and behavior analysis —to improve detection efficacy of zero-day threats.FortiSandbox AI Capabilities FortiSandbox applies two robust machine algorithms for static and dynamic threat analysis:n n A patent-pending enhanced random forest with boost tree machine-learning model n n A least squares optimizationlearning modelFortiSandbox uses the same language as the MITRE ATT&CK framework for categorizing malware techniques, which accelerates threat management and response.Simplicity, Flexibility, Scalability, and CostComplexity is the enemy of security. Security teams that rely on multiple,disaggregated security products from various vendors are typically forced to learnnonstandard security languages. Each solution may have its own unique languageto report a potential threat. Alerts describing the same threat must be manuallytranslated and mapped by the security operations (SecOps) team to understandthe scope of a problem. This requires the dedicated attention of an experiencedSecOps analyst while lengthening the time it takes to investigate and mitigate apotential attack.And as networks continue to grow and organizations expand adoption of digitalinnovations, their sandboxing needs must be able to keep pace in terms ofperformance, scalability, deployment form factors, licensing flexibility, and (perhapsmost of all) costs. Solution simplicity. FortiSandbox addresses the problem of multiple security languages through the adoption of the MITRE ATT&CK framework. This establishes a universal security language for categorizing all malware techniques inan easy-to-read matrix as part of threat mapping and reporting—which helps security architects accelerate threatmanagement and response processes.FortiSandbox also supports inspection of multiple protocols in a single, unified solution to help simplify infrastructure, centralize reporting, improve threat-hunting capabilities, and reduce costs—capital investment (CapEx) as well asOpEx. Other vendors may require up to four separate sandboxes to provide comparable protection across the extended business infrastructure. Other products may also have limited deployment options that impact the ease of setup andexpansion due to business growth.Flexible form factors. As FortiSandbox is available in multiple form factors (e.g., on-premises appliance, VM, hosted cloud, public cloud), it supports growth and scalability while covering the entire network attack surface. For example, an organization that purchases a sandbox that only comes in an on-premises form factor will be forced to start over fromscratch to extend sandboxing protection into future or current cloud environments (costing time, money, and greaterpotential risk exposure). And if they want to move to a hybrid sandbox deployment, they have no other alternative.Scalability and clustering. To handle the scanning of a high number of files concurrently, multiple FortiSandbox devices can be used together in a load-balancing high-availability (HA) cluster.9 FortiSandbox supports up to 100-node sandbox clusters. Here, clusters can be connected to one another, which means homogenous scale is limitless, enabling FortiSandbox to keep up with high traffic throughput and to remain ahead of potential or planned additions to thebusiness in the future.Low TCO. Next-generation sandboxing delivers better security for modern networks, as well as better value for new or replacement sandboxing devices. Current testing shows that FortiSandbox provides outstanding performance, value, and investment protection with a low three-year TCO.10Detect and Prevent Breaches with AI-based FortiSandboxAs new malware variants multiply and the risk of zero-day attacks makes breaches an eventuality for organizations of all sizes, security architects should look to replace outdated sandboxing devices with a solution that is designed for current needs. As part of the Fortinet Security Fabric, FortiSandbox provides an integrated, third-generation sandbox that allows security leaders to both detect and prevent breaches through true AI-based security effectiveness, manageability,scalability, and cost.1“The Cost of Cybercrime: Ninth Annual Cost of Cybercrime Study,” Accenture and Ponemon Institute, March 6, 2019.2“The Security Architect and Cybersecurity: A Report on Current Priorities and Challenges,” Fortinet, November 12, 2019.3“2019 Cost of a Data Breach Report,” Ponemon Institute and IBM Security, July 2019.4“MITRE ATT&CK,” MITRE, accessed November 25, 2019.5“NSS Labs Announces 2018 Breach Detection Systems Group Test Results,” NSS Labs, October 11, 2018.6Jessica Williams, et al., “Breach Prevention Systems Test Report,” NSS Labs, August 7, 2019.7“Q2 2019 Advanced Threat Defense (ATD) Testing Report,” ICSA Labs, July 9, 2019.8Dipti Ghimire and James Hasty, “Breach Detection Systems Test Report,” NSS Labs, October 19, 2017.9“Technical Note: FortiSandbox HA-Cluster Explanation and Configuration,” Fortinet, accessed November 25, 2019.10 Jessica Williams, et al., “Breach Prevention Systems Test Report,” NSS Labs, August 7, 2019. Copyright © 2021 Fortinet, Inc. All rights reserved. Fortinet, FortiGate, FortiCare and FortiGuard, and certain other marks are registered trademarks of Fortinet, Inc., and other Fortinet names herein may also be registered and/or common law trademarks of Fortinet. All other product or company names may be trademarks of their respective owners. Performance and other metrics contained herein were attained in internal lab tests under ideal conditions, and actual performance and other results may vary. Network variables, different network environments and other conditions may affect performance results. Nothing herein represents any binding commitment by Fortinet, and Fortinet disclaims all warranties, whether express or implied, except to the extent Fortinet enters a binding written contract, signed by Fortinet’s General Counsel, with a purchaser that expressly warrants that the identified product will perform according to certain expressly-identified performance metrics and, in such event, only the specific performance metrics expressly identified in such binding written contract shall be binding on Fortinet. For absolute clarity, any such warranty will be limited to performance in the same ideal conditions as in Fortinet’s internal lab tests. Fortinet disclaims in full any covenants, representations, and guarantees pursuant hereto, whether express or implied. Fortinet reserves the right to change, modify, transfer, or otherwise revise this publication without notice, and the most current version of the publication shall be applicable. Fortinet disclaims in full any covenants, representations, and guarantees pursuant hereto, whether express or implied. Fortinet reserves the right to change, modify, transfer, or otherwise revise this publication without notice, and the most current version of the publication shall be applicable.February 25, 2021 9:59 PM。

基于RRT的运动规划算法综述1.介绍在过去的十多年中，机器人的运动规划问题已经收到了大量的关注，因为机器人开始成为现代工业和日常生活的重要组成部分。

最早的运动规划的问题只是考虑如何移动一架钢琴从一个房间到另一个房间而没有碰撞任何物体。

早期的算法则关注研究一个最完备的运动规划算法（完备性指如果存在这么一条规划的路径，那么算法一定能够在有限时间找到它），例如用一个多边形表示机器人，其他的多边形表示障碍物体，然后转化为一个代数问题去求解。

但是这些算法遇到了计算的复杂性问题，他们有一个指数时间的复杂度。

在1979年，Reif则证明了钢琴搬运工问题的运动规划是一个PSPACE-hard问题[1]。

所以这种完备的规划算法无法应用在实际中。

在实际应用中的运动规划算法有胞分法[2]，势场法[3]，路径图法[4]等。

这些算法在参数设置的比较好的时候，可以保证规划的完备性，在复杂环境中也可以保证花费的时间上限。

然而，这些算法在实际应用中有许多缺点。

例如在高维空间中这些算法就无法使用，像胞分法会使得计算量过大。

势场法会陷入局部极小值，导致规划失败[5],[6]。

基于采样的运动规划算法是最近十几年提出的一种算法，并且已经吸引了极大的关注。

概括的讲，基于采样的运动规划算法一般是连接一系列从无障碍的空间中随机采样的点，试图建立一条从初始状态到目标状态的路径。

与最完备的运动规划算法相反，基于采样的方法通过避免在状态空间中显式地构造障碍物来提供大量的计算节省。

即使这些算法没有实现完整性，但是它们是概率完备，这意味着规划算法不能返回解的概率随着样本的数量趋近无穷而衰减到零[7]，并且这个下降速率是指数型的。

快速扩展随机树(Rapidly-exploring Random Trees，RRT)算法，是近十几年得到广泛发展与应用的基于采样的运动规划算法，它由美国爱荷华州立大学的Steven M. LaValle 教授在1998年提出，他一直从事RRT算法的改进和应用研究，他的相关工作奠定了RRT算法的基础。

智能科技让我们哑口无言英语作文全文共3篇示例，供读者参考篇1Intelligent Technology Leaves Us SpeechlessAs a student, I often find myself in awe of the rapid advancements in intelligent technology that have taken place over the past few years. From artificial intelligence (AI) assistants that can engage in natural conversations to self-driving cars and robots that can perform complex tasks, it seems like the stuff of science fiction is quickly becoming a reality.Just a few years ago, the idea of having a digital assistant that could understand and respond to voice commands was revolutionary. Now, it's commonplace for people to use AI assistants like Siri, Alexa, or Google Assistant to set reminders, play music, or even control their smart home devices with simple voice commands. And these assistants are only getting smarter, with the ability to understand and respond to more complex queries and even engage in back-and-forth conversations.One of the most mind-blowing developments in AI has been the rise of large language models like GPT-3 and ChatGPT. Thesemodels have been trained on vast amounts of data from the internet, allowing them to generate human-like text on virtually any topic. From writing essays and articles to coding and even creative writing, these AI models are capable of producing content that would have been unimaginable just a few years ago.As a student, I can already see the potential for these AI writing assistants to revolutionize the way we learn and study. Imagine being able to ask an AI to explain a complex concept in simple terms, or to generate study materials tailored to your specific learning needs. The possibilities are endless, and it's both exciting and a little daunting to think about how this technology might change the way we approach education in the future.But it's not just in the realm of language and writing that AI is making waves. Computer vision technology, which allows machines to recognize and interpret visual data, has also made significant strides in recent years. From facial recognition systems to self-driving cars that can navigate complex environments, this technology is already being integrated into our daily lives in ways that would have seemed like science fiction not too long ago.One of the most impressive examples of computer vision technology is the development of robots that can perform intricate tasks with precision and accuracy that far exceeds human capabilities. These robots are being used in everything from manufacturing and assembly lines to surgery and even space exploration. It's mind-boggling to think about the level of complexity and coordination required for a robot to perform delicate surgical procedures or to navigate the treacherous terrain of another planet.As amazing as these technological advancements are, they also raise important questions and concerns. One of the biggest issues is the potential impact on employment and the workforce. As AI and automation become more advanced, there is a real possibility that many jobs currently performed by humans could become obsolete. This could lead to widespread job displacement and economic disruption, which would havefar-reaching consequences for society as a whole.Another concern is the ethical implications of these technologies, particularly in areas like AI decision-making and facial recognition. As AI systems become more sophisticated and are entrusted with making important decisions that could impactpeople's lives, it's crucial that we ensure these systems are transparent, accountable, and free from bias or discrimination.Privacy is also a major issue when it comes to technologies like facial recognition and data collection. As these systems become more pervasive, there is a risk of mass surveillance and erosion of personal privacy rights. It's important that we strike a balance between leveraging these technologies for their potential benefits while also protecting individual privacy and civil liberties.Despite these concerns, it's hard to deny the incredible potential of intelligent technology to transform our lives in ways we can barely imagine. From revolutionizing healthcare and scientific research to solving global challenges like climate change and food insecurity, AI and other advanced technologies could hold the key to addressing some of the biggest problems facing humanity.As a student, it's both exciting and a little intimidating to think about what the future might hold. Will AI assistants eventually replace human teachers and tutors? Will robots take over many of the jobs and professions we're currently studying for? Or will these technologies open up entirely new career paths and opportunities that we can't even conceive of yet?One thing is certain: the pace of technological change shows no signs of slowing down, and it's up to us to adapt and embrace these changes while also addressing the ethical and societal implications. As students, we have a unique opportunity to shape the future of these technologies and ensure that they are developed and deployed in a responsible and equitable manner.Whether it's through studying computer science, ethics, or policy-making, or simply by staying informed and engaged in the ongoing debates around these issues, we all have a role to play in navigating this brave new world of intelligent technology.In the end, while these advancements may leave us speechless in the moment, it's up to us to find our voices and ensure that we harness the power of these technologies for the greater good of humanity. The future is uncertain, but one thing is clear: intelligent technology is here to stay, and it's up to us to shape its impact on our world.篇2Intelligent Technology Leaves Us SpeechlessHave you ever felt like technology is advancing so rapidly that it's leaving you behind? Like you can barely keep up with the latest gadgets, apps, and innovations before something new andmind-blowing comes along? That's precisely how I've felt lately as developments in artificial intelligence and other cutting-edge fields have me constantly in awe and scratching my head wondering "How is this even possible?!"Maybe you've experienced that dumbfounded feeling when you first saw Boston Dynamics' terrifyingly agile robot dogs that can navigate all sorts of environments with ease. Or when you learned about AI language models like GPT-3 that can write shockingly coherent essays and stories after being given just a brief prompt. Heck, I wouldn't be surprised if an AI actually helped write this essay!The more I learn about the capabilities of modern AI, the more my jaw drops. It's astonishing to me that machines can now beat humans at tasks we once thought required true intelligence and creativity - like defeating grandmasters at chess and go, generating photorealistic artwork and music from scratch, and even developing innovative ideas fornext-generation batteries or cancer drugs.And we're still just scratching the surface of what may be possible with AI. Companies like Google, OpenAI, and DeepMind are making breathtaking advances year after year. A few decades from now, will we have hyper-intelligent AI assistants that cantutor us in any subject, diagnose our illnesses with remarkable accuracy, or optimize our households and cities for maximum efficiency and sustainability? The mind boggles at the potential implications.Of course, there are also very real concerns and risks around increasingly powerful AI that we need to thoughtfully grapple with as a society. What happens when AI surpasses human-level abilities across most domains? Will we become reliant on and subservient to superintelligent machines that could potential become unstable or pursue goals misaligned with our own wellbeing? These aren't just plots from ominous science fiction stories anymore - they're challenges we may have to confront in the coming decades if we can't develop advanced AI in a controlled and constrained manner.Not all recent technological upheavals have been in the AI realm either. Innovations in fields like quantum computing, nanotechnology, biotechnology, and material science often leave me awestruck too. Just think about groundbreaking advances like the quantum computers that can perform certain calculations exponentially faster than classical computers by exploiting quantum phenomena. Or scientists' newfound ability to not just read, but actually edit genomes with precision geneediting tools like CRISPR. Or the discovery of wonder materials like graphene - a hexagonal lattice of carbon just one atom thick that is stronger than diamond yet incredibly lightweight and conductive.Wrapping my head around the huge implications of these breakthroughs is incredibly tough. How will quantum computing impact fields like cryptography, materials design, andlogistics/optimization problems? Could gene editing allow us to not only treat genetic diseases, but actually upgrade humans with enhanced traits like higher intelligence or resistance to viruses? Will graphene or other nano-architectured metamaterials spawn a revolution in electronics, energy, and manufacturing? The possibilities are at once thrilling and overwhelming.Sometimes it feels like the science and technology sector has become a high-octane disruptor - constantly upending our core assumptions about what's possible and introducing capabilities that seem ripped from the pages篇3Intelligent Technology Leaves Us SpeechlessIn today's rapidly evolving world, technological advancements have become an integral part of our daily lives. From smartphones to artificial intelligence, we are constantly surrounded by innovations that were once deemed science fiction. Among these marvels, intelligent technology stands out as a realm that leaves us in awe, challenging our very understanding of what is possible.As a student, I find myself both fascinated and intimidated by the incredible strides made in artificial intelligence (AI) and machine learning. These technologies have the potential to revolutionize various aspects of our lives, from education to healthcare, and even the way we interact with our environment.One area where intelligent technology has made remarkable progress is natural language processing (NLP). The ability of machines to understand, interpret, and generate human language has reached astonishing levels. Language models like ChatGPT, developed by OpenAI, can engage in coherent and contextual conversations, answering complex queries and even assisting with writing tasks. It's mind-boggling to witness an AI system compose eloquent essays, poems, or even computer code with such fluency.As a student, the implications of NLP are far-reaching. Imagine having a virtual tutor that can explain complex concepts in a personalized manner, adapting to your learning style and pace. Or envision a world where language barriers are virtually non-existent, with real-time translation capabilities that facilitate seamless communication across cultures.However, the progress in AI raises ethical concerns that cannot be ignored. The potential for misuse, such as the generation of convincing misinformation or the perpetuation of biases inherent in the training data, is a real threat. As students, we must develop a critical mindset and learn to navigate this new landscape responsibly.Beyond language, intelligent technology has made remarkable strides in computer vision and pattern recognition. Algorithms can now identify objects, recognize faces, and even interpret complex visual scenes with accuracy rivaling human capabilities. This has far-reaching applications in fields like security, autonomous vehicles, and medical diagnostics.Imagine a future where self-driving cars are the norm, with AI systems capable of navigating complex urban environments with unprecedented precision. Or consider the potential ofAI-assisted medical imaging, where algorithms can detectcancerous growths or anomalies with greater accuracy than human radiologists, potentially saving countless lives.Yet, as we marvel at these achievements, we must also grapple with the ethical implications of such powerful technologies. Questions of privacy, bias, and the potential displacement of human labor loom large, demanding thoughtful discussions and responsible governance.Intelligent technology has also made significant inroads into decision-making and problem-solving domains. Advanced algorithms can now analyze vast amounts of data, identify patterns, and make predictions with uncanny accuracy. From predicting stock market trends to optimizing supply chains, AI systems are becoming indispensable tools in various industries.As students, we are fortunate to witness the dawn of this technological revolution. However, we must also recognize the importance of developing a deep understanding of the underlying principles and algorithms that drive these systems. Only by cultivating a strong foundation in fields like mathematics, computer science, and statistics can we truly harness the potential of intelligent technology while mitigating its risks.Furthermore, the rise of intelligent technology calls for a multidisciplinary approach to education. Collaboration betweenfields such as computer science, ethics, psychology, and sociology is crucial to ensure that these technologies are developed and deployed responsibly, with a keen awareness of their societal implications.As we stand in awe of the remarkable achievements of intelligent technology, we must also acknowledge the challenges and uncertainties that lie ahead. The rapid pace of progress raises existential questions about the nature of intelligence, consciousness, and what it means to be human.Will we one day create an artificial general intelligence (AGI) that surpasses human capabilities across multiple domains? If so, how will we coexist with such a superintelligent entity? These are profound questions that require careful consideration and responsible stewardship.In conclusion, intelligent technology has truly left us speechless, both in awe of its achievements and in contemplation of its implications. As students, we stand at the forefront of this technological revolution, poised to shape the future with our knowledge, creativity, and ethical responsibility.It is our duty to embrace these advancements while remaining vigilant about their potential misuse. We must strive to foster a deep understanding of these technologies, cultivatecritical thinking skills, and engage in interdisciplinary collaborations to ensure that intelligent technology serves the greater good of humanity.Only by striking a balance between innovation and ethical considerations can we truly harness the transformative power of intelligent technology, leaving a lasting and positive impact on our world.。

2012.09College English Notes:1.break 【口】机会，好运。

2.本句意为：Steve Dimon 四月份因中风瘫痪而上了维生系统后，就感觉身体被牢牢困住，连动动左边的手指或脚趾都做不到。

life support 生命维持，生命保障。

wiggle 使摆动。

3.Stephen King 史蒂芬·金，当代美国畅销书作家，有“现代恐怖小说大师”之称。

4.rigorous 严格的，严峻的。

5.rehabilitation 康复。

下文中的rehab 为其省略形式。

6.therapist （特定治疗法的）治疗师。

下文中的therapy 意为“治疗，疗法”之意。

7.hone 磨练，训练。

8.victim 受害者，罹病者。

9.本句意为：由南加州大学维特比工程学院研究人员设计的机器人Bandit ，当病人进行艰苦的康复训练时，会在一旁不停地给他们打气和纠正错误。

strenuous 繁重的，强烈的，艰苦的。

10.tap 开发，开辟。

11.Alzheimer ’s disease 老年性痴呆症。

12.autism spectrum disorder 泛自闭症障碍，自闭症谱系障碍，自闭症系列障碍。

笤科技博览笤Socially Assistive Robots Provide aBreak 1for Patients社交机器人给病人带来福音By Rebecca Lurye 长亭/选注fter a stroke in April left Steve Dimon paralyzed and on life support,he felt trapped inside his own body,unable to move a finger or wiggle his toes on his left side.2“A stroke is the cruelest joke to mankind,”says Dimon,who can now walk unassisted.“It ’s like reading a Stephen King 3novel....It ’s like,will Iever move again?Will I ever talk again?”Weeks ofrigorous 4rehabilitation 5at Rancho Los Amigos Na -tional Rehabilitation Center in California also were exhausting,until a different kind of therapist 6paid a visit to the Los Angeles native:a robot named Ban -dit.At first glance,Bandit lacks many of the quali -ties of a good coach or companion.But with its shinygray metal face,cameras for eyes and a simple wiremouth,it has been using its well -honed 7social skillsto help improve the quality of life of stroke victims 8.Designed by researchers at the University of South -ern California ’s Viterbi School of Engineering,Ban -dit motivates and corrects patients as they carry outstrenuous exercises.9It ’s one of several efforts na -tionwide to tap 10the abilities of robots to provide as -sistance and social interaction in health care.Other researchers are developing the use of so -cially assistive robots in working with patients whohave Alzheimer ’s disease 11and children with autismspectrum disorders 12.Rancho Los Amigos and USClaunched a study in mid -June that aims to compare A 社交机器人已缓缓走过二十个年头。

第41卷第3期2021年6月振动、测试与诊断Vol.41No.3Jun.2021 Journal of Vibration，Measurement&Diagnosis形状记忆合金驱动的连续跳跃柔性机器人∗毛婷，彭瀚旻，查泽琳，赵燊佳（南京航空航天大学机械结构力学及控制国家重点实验室南京，210016）摘要针对在复杂、狭窄的非结构化地形下跳跃机器人存在的着陆稳定性和运动连续性的问题，提出了一种形状记忆合金（shape memory alloy，简称SMA）智能材料驱动的柔性跳跃机器人，它具有轻质小型、结构简单及连续运动的优点。

利用对称的折纸柔性身体减少着陆振动，保证着陆稳定；利用对称的双SMA弹簧拮抗系统实现弹性元件交替变形和储能释能的功能；建立了柔性机器人跳跃运动的理论模型，研究了关键结构尺寸对能量储存和跳跃性能的影响机制，并研制了一款尺寸为6cm×4cm×2.5cm、质量为3.8g的原理样机。

实验结果表明：柔性机器人依靠SMA驱动能够实现跳跃触发和形态恢复，最大跳跃高度和远度分别为8.67cm和18cm，并且可以适应不同工作面。

该机器人跳跃性能优越，控制顺序简单，可为非结构化地形下完成侦察探测工作奠定基础。

关键词形状记忆合金；微型机器人；跳跃运动；柔性中图分类号TP242；TH113.1引言针对在非结构化地形下的侦察、探测等任务，跳跃机器人［1］凭借良好的运动、越障和环境适应能力而具有广泛的应用前景。

传统的跳跃机器人采用基于电机驱动的弹射机构，利用机械传动元件实现能量的储存和释放。

文献［2‐3］提出的昆虫仿生式跳跃机器人，将电机旋转的机械能通过凸轮、齿轮等机械元件传动，储存在弹簧内，释放能量的同时打开跳跃腿，能跳跃几倍于身体尺寸的高度和远度。

但是，上述机器人零件复杂，制造困难，抗撞击性差，可控性差，未考虑机器人的着陆稳定性。

文献［4‐5］采用笼形保护外壳或对称结构设计实现机器人的连续运动。

软体机器人运动学与动力学建模综述一、本文概述随着科技的飞速发展，软体机器人作为一种新兴的技术领域，正在吸引着越来越多的研究关注。

作为一种具有高度灵活性和适应性的机器人，软体机器人在医疗、航空、深海探索等众多领域展现出巨大的应用潜力。

然而，软体机器人的运动学与动力学建模一直是制约其进一步发展的关键因素之一。

本文旨在对软体机器人的运动学与动力学建模进行综述，梳理相关领域的研究成果，以期为未来软体机器人的设计与应用提供理论支持。

本文首先介绍了软体机器人的基本概念和分类，阐述了其相较于传统刚性机器人的独特优势。

接着，详细阐述了软体机器人运动学建模的基本原理和方法，包括基于几何关系的建模、基于能量原理的建模等。

在动力学建模方面，本文重点介绍了软体机器人动力学模型的构建过程，包括质量分布、惯性矩阵、刚度矩阵等的确定，以及动力学方程的建立与求解。

本文还综述了软体机器人在运动学与动力学建模过程中面临的挑战与问题，如模型复杂性、参数辨识、实时控制等。

对国内外在软体机器人建模领域的最新研究进展进行了梳理和评价，以期为读者提供一个全面、深入的软体机器人运动学与动力学建模的参考框架。

本文展望了软体机器人运动学与动力学建模的未来发展趋势，提出了可能的研究方向和应用领域，为相关领域的研究者提供了一定的参考和启示。

二、软体机器人运动学建模软体机器人运动学建模是研究和描述软体机器人运动规律的重要方法。

与传统的刚性机器人不同，软体机器人由于其结构的柔软性和可变形性，其运动学建模过程更为复杂。

在软体机器人的运动学建模中，主要关注的是机器人末端执行器或特定点的位置、速度和加速度等运动学参数，而不涉及机器人的内部应力、应变等动力学因素。

软体机器人的运动学建模通常基于几何学和运动学原理。

一种常用的方法是基于连续介质力学的理论，将软体机器人视为连续变形的弹性体，通过描述其形状和位置的变化来建立运动学模型。

另一种方法是基于离散元法，将软体机器人划分为一系列离散的单元，通过描述这些单元之间的相对位置和关系来建立运动学模型。

Cooperative Mobile Robotics: Antecedents and DirectionsY. UNY CAOComputer Science Department, University of California, Los Angeles, CA 90024-1596ALEX S. FUKUNAGAJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109-8099ANDREW B. KAHNGComputer Science Department, University of California, Los Angeles, CA 90024-1596Editors: R.C. Arkin and G.A. BekeyAbstract. There has been increased research interest in systems composed of multiple autonomous mobile robots exhibiting cooperative behavior. Groups of mobile robots are constructed, with an aim to studying such issues as group architecture, resource conflict, origin of cooperation, learning, and geometric problems. As yet, few applications of cooperative robotics have been reported, and supporting theory is still in its formative stages. In this paper, we give a critical survey of existing works and discuss open problems in this field, emphasizing the various theoretical issues that arise in the study of cooperative robotics. We describe the intellectual heritages that have guided early research, as well as possible additions to the set of existing motivations.Keywords: cooperative robotics, swarm intelligence, distributed robotics, artificial intelligence, mobile robots, multiagent systems1. PreliminariesThere has been much recent activity toward achieving systems of multiple mobile robots engaged in collective behavior. Such systems are of interest for several reasons:•tasks may be inherently too complex (or im-possible) for a single robot to accomplish, or performance benefits can be gained from using multiple robots;•building and using several simple robots can be easier, cheaper, more flexible and more fault-tolerant than having a single powerful robot foreach separate task; and•the constructive, synthetic approach inherent in cooperative mobile robotics can possibly∗This is an expanded version of a paper which originally appeared in the proceedings of the 1995 IEEE/RSJ IROS conference. yield insights into fundamental problems in the social sciences (organization theory, economics, cognitive psychology), and life sciences (theoretical biology, animal ethology).The study of multiple-robot systems naturally extends research on single-robot systems, butis also a discipline unto itself: multiple-robot systems can accomplish tasks that no single robot can accomplish, since ultimately a single robot, no matter how capable, is spatially limited. Multiple-robot systems are also different from other distributed systems because of their implicit “real-world” environment, which is presumably more difficult to model and reason about than traditional components of distributed system environments (i.e., computers, databases, networks).The term collective behavior generically denotes any behavior of agents in a system having more than one agent. the subject of the present survey, is a subclass of collective behavior that is characterized by cooperation. Webster’s dictionary [118] defines “cooperate” as “to associate with anoth er or others for mutual, often economic, benefit”. Explicit definitions of cooperation in the robotics literature, while surprisingly sparse, include:1. “joint collaborative behavior that is directed toward some goal in which there is a common interest or reward” [22];2. “a form of interaction, usually based on communication” [108]; and3. “[joining] together for doing something that creates a progressive result such as increasing performance or saving time” [137].These definitions show the wide range of possible motivating perspectives. For example, definitions such as (1) typically lead to the study of task decomposition, task allocation, and other dis-tributed artificial intelligence (DAI) issues (e.g., learning, rationality). Definitions along the lines of (2) reflect a concern with requirements for information or other resources, and may be accompanied by studies of related issues such as correctness and fault-tolerance. Finally, definition (3) reflects a concern with quantified measures of cooperation, such as speedup in time to complete a task. Thus, in these definitions we see three fundamental seeds: the task, the mechanism of cooperation, and system performance.We define cooperative behavior as follows: Given some task specified by a designer, a multiple-robot system displays cooperative behavior if, due to some underlying mechanism (i.e., the “mechanism of cooperation”), there is an increase in the total utility of the system. Intuitively, cooperative behavior entails some type of performance gain over naive collective behavior. The mechanism of cooperation may lie in the imposition by the designer of a control or communication structure, in aspects of the task specification, in the interaction dynamics of agent behaviors, etc.In this paper, we survey the intellectual heritage and major research directions of the field of cooperative robotics. For this survey of cooperative robotics to remain tractable, we restrict our discussion to works involving mobile robots or simulations of mobile robots, where a mobile robot is taken to be an autonomous, physically independent, mobile robot. In particular, we concentrated on fundamental theoretical issues that impinge on cooperative robotics. Thus, the following related subjects were outside the scope of this work:•coordination of multiple manipulators, articulated arms, or multi-fingered hands, etc.•human-robot cooperative systems, and user-interface issues that arise with multiple-robot systems [184] [8] [124] [1].•the competitive subclass of coll ective behavior, which includes pursuit-evasion [139], [120] and one-on-one competitive games [12]. Note that a cooperative team strategy for, e.g., work on the robot soccer league recently started in Japan[87] would lie within our present scope.•emerging technologies such as nanotechnology [48] and Micro Electro-Mechanical Systems[117] that are likely to be very important to co-operative robotics are beyond the scope of this paper.Even with these restrictions, we find that over the past 8 years (1987-1995) alone, well over 200papers have been published in this field of cooperative (mobile) robotics, encompassing theories from such diverse disciplines as artificial intelligence, game theory/economics, theoretical biology, distributed computing/control, animal ethology and artificial life.We are aware of two previous works that have surveyed or taxonomized the literature. [13] is abroad, relatively succinct survey whose scope encompasses distributed autonomous robotic systems(i.e., not restricted to mobile robots). [50] focuses on several well-known “swarm” architectures (e.g., SWARM and Mataric’s Behavior-based architecture –see Section 2.1) and proposes a taxonomy to characterize these architectures. The scope and intent of our work differs significantly from these, in that (1) we extensively survey the field of co-operative mobile robotics, and (2) we provide a taxonomical organization of the literature based on problems and solutions that have arisen in the field (as opposed to a selected group of architectures). In addition, we survey much new material that has appeared since these earlier works were published.Towards a Picture of Cooperative RoboticsIn the mid-1940’s Grey Walter, along with Wiener and Shannon, studied turtle-like robots equipped wit h light and touch sensors; these simple robots exhibited “complex social behavior” in responding to each other’s movements [46]. Coordination and interactions of multiple intelligent agents have been actively studied in the field of distributed artificial intelligence (DAI) since the early 1970’s[28], but the DAI field concerned itself mainly with problems involving software agents. In the late 1980’s, the robotics research community be-came very active in cooperative robotics, beginning with projects such as CEBOT [59], SWARM[25], ACTRESS [16], GOFER [35], and the work at Brussels [151]. These early projects were done primarily in simulation, and, while the early work on CEBOT, ACTRESS and GOFER have all had physical implementations (with≤3 robots), in some sense these implementations were presented by way of proving the simulation results. Thus, several more recent works (cf. [91], [111], [131])are significant for establishing an emphasis on the actual physical implementation of cooperative robotic systems. Many of the recent cooperative robotic systems, in contrast to the earlier works, are based on a behavior-based approach (cf. [30]).Various perspectives on autonomy and on the connection between intelligence and environment are strongly associated with the behavior-based approach [31], but are not intrinsic to multiple-robot systems and thus lie beyond our present scope. Also note that a recent incarnation of CEBOT, which has been implemented on physical robots, is based on a behavior-based control architecture[34].The rapid progress of cooperative robotics since the late 1980’s has been an interplay of systems, theories and problems: to solve a given problem, systems are envisioned, simulated and built; theories of cooperation are brought from other fields; and new problems are identified (prompting further systems and theories). Since so much of this progress is recent, it is not easy to discern deep intellectual heritages from within the field. More apparent are the intellectualheritages from other fields, as well as the canonical task domains which have driven research. Three examples of the latter are:•Traffic Control. When multiple agents move within a common environment, they typically attempt to avoid collisions. Fundamentally, this may be viewed as a problem of resource conflict, which may be resolved by introducing, e.g., traffic rules, priorities, or communication architectures. From another perspective, path planning must be performed taking into con-sideration other robots and the global environment; this multiple-robot path planning is an intrinsically geometric problem in configuration space-time. Note that prioritization and communication protocols – as well as the internal modeling of other robots – all reflect possible variants of the group architecture of the robots. For example, traffic rules are commonly used to reduce planning cost for avoiding collision and deadlock in a real-world environment, such as a network of roads. (Interestingly, behavior-based approaches identify collision avoidance as one of the most basic behaviors [30], and achieving a collision-avoidance behavior is the natural solution to collision avoidance among multiple robots. However, in reported experiments that use the behavior-based approach, robots are never restricted to road networks.) •Box-Pushing/Cooperative Manipulation. Many works have addressed the box-pushing (or couch-pushing) problem, for widely varying reasons. The focus in [134] is on task allocation, fault-tolerance and (reinforcement) learning. By contrast, [45] studies two boxpushing protocols in terms of their intrinsic communication and hardware requirements, via the concept of information invariants. Cooperative manipulation of large objects is particularly interesting in that cooperation can be achieved without the robots even knowing of each others’ existence [147], [159]. Other works in the class of box-pushing/object manipulation include [175] [153] [82] [33] [91] [94] [92][114] [145] [72] [146].•Foraging. In foraging, a group of robots must pick up objects scattered in the environment; this is evocative of toxic waste cleanup, harvesting, search and rescue, etc. The foraging task is one of the canonical testbeds for cooperative robotics [32] [151] [10] [67] [102] [49] [108] [9][24]. The task is interesting because (1) it can be performed by each robot independently (i.e., the issue is whether multiple robots achieve a performance gain), and (2) as discussed in Section 3.2, the task is also interesting due to motivations related to the biological inspirations behind cooperative robot systems. There are some conceptual overlaps with the related task of materials handling in a manufacturing work-cell [47]. A wide variety of techniques have been applied, ranging from simple stigmergy (essentially random movements that result in the fortuitous collection of objects [24] to more complex algorithms in which robots form chains along which objects are passed to the goal [49].[24] defines stigmergy as “the production of a certain behaviour in agents as a consequence of the effects produced in the local environment by previous behaviour”. This is actually a form of “cooperation without communication”, which has been the stated object of several for-aging solutions since the corresponding formulations become nearly trivial if communication is used. On the other hand, that stigmergy may not satisfy our definition of cooperation given above, since there is no performance improvement over the “naive algorithm” –in this particular case, the proposed stigmergic algorithm is the naive algorithm. Again, group architecture and learning are major research themes in addressing this problem.Other interesting task domains that have received attention in the literature includemulti-robot security systems [53], landmine detection and clearance [54], robotic structural support systems (i.e., keeping structures stable in case of, say ,an earthquake) [107], map making [149], and assembly of objects using multiple robots [175].Organization of PaperWith respect to our above definition of cooperative behavior, we find that the great majority of the cooperative robotics literature centers on the mechanism of cooperation (i.e., few works study a task without also claiming some novel approach to achieving cooperation). Thus, our study has led to the synthesis of five “Research Axes” which we believe comprise the major themes of investigation to date into the underlying mechanism of cooperation.Section 2 of this paper describes these axes, which are: 2.1 Group Architecture, 2.2 Resource Conflict, 2.3 Origin of Cooperation, 2.4 Learning, and 2.5 Geometric Problems. In Section 3,we present more synthetic reviews of cooperative robotics: Section 3.1 discusses constraints arising from technological limitations; and Section 3.2discusses possible lacunae in existing work (e.g., formalisms for measuring performance of a cooperative robot system), then reviews three fields which we believe must strongly influence future work. We conclude in Section 4 with a list of key research challenges facing the field.2. Research AxesSeeking a mechanism of cooperation may be rephrased as the “cooperative behavior design problem”: Given a group of robots, an environment, and a task, how should cooperative behavior arise? In some sense, every work in cooperative robotics has addressed facets of this problem, and the major research axes of the field follow from elements of this problem. (Note that certain basic robot interactions are not task-performing interactions per se, but are rather basic primitives upon which task-performing interactions can be built, e.g., following ([39], [45] and many others) or flocking [140], [108]. It might be argued that these interactions entail “control and coordination” tasks rather than “cooperation” tasks, but o ur treatment does not make such a distinction).First, the realization of cooperative behavior must rely on some infrastructure, the group architecture. This encompasses such concepts as robot heterogeneity/homogeneity, the ability of a given robot to recognize and model other robots, and communication structure. Second, for multiple robots to inhabit a shared environment, manipulate objects in the environment, and possibly communicate with each other, a mechanism is needed to resolve resource conflicts. The third research axis, origins of cooperation, refers to how cooperative behavior is actually motivated and achieved. Here, we do not discuss instances where cooperation has been “explicitly engineered” into the robots’ behavior since this is the default approach. Instead, we are more interested in biological parallels (e.g., to social insect behavior), game-theoretic justifications for cooperation, and concepts of emergence. Because adaptability and flexibility are essential traits in a task-solving group of robots, we view learning as a fourth key to achieving cooperative behavior. One important mechanism in generating cooperation, namely,task decomposition and allocation, is not considered a research axis since (i) very few works in cooperative robotics have centered on task decomposition and allocation (with the notable exceptions of [126], [106], [134]), (ii) cooperative robot tasks (foraging, box-pushing) in the literature are simple enough that decomposition and allocation are not required in the solution, and (iii) the use of decomposition and allocation depends almost entirely on the group architectures(e.g. whether it is centralized or decentralized).Note that there is also a related, geometric problem of optimizing the allocation of tasks spatially. This has been recently studied in the context of the division of the search of a work area by multiple robots [97]. Whereas the first four axes are related to the generation of cooperative behavior, our fifth and final axis –geometric problems–covers research issues that are tied to the embed-ding of robot tasks in a two- or three-dimensional world. These issues include multi-agent path planning, moving to formation, and pattern generation.2.1. Group ArchitectureThe architecture of a computing sys tem has been defined as “the part of the system that remains unchanged unless an external agent changes it”[165]. The group architecture of a cooperative robotic system provides the infrastructure upon which collective behaviors are implemented, and determines the capabilities and limitations of the system. We now briefly discuss some of the key architectural features of a group architecture for mobile robots: centralization/decentralization, differentiation, communications, and the ability to model other agents. We then describe several representative systems that have addressed these specific problems.Centralization/Decentralization The most fundamental decision that is made when defining a group architecture is whether the system is centralized or decentralized, and if it is decentralized, whether the system is hierarchical or distributed. Centralized architectures are characterized by a single control agent. Decentralized architectures lack such an agent. There are two types of decentralized architectures: distributed architectures in which all agents are equal with respect to control, and hierarchical architectures which are locally centralized. Currently, the dominant paradigm is the decentralized approach.The behavior of decentralized systems is of-ten described using such terms as “emergence” and “self-organization.” It is widely claimed that decentralized architectures (e.g., [24], [10], [152],[108]) have several inherent advantages over centralized architectures, including fault tolerance, natural exploitation of parallelism, reliability, and scalability. However, we are not aware of any published empirical or theoretical comparison that supports these claims directly. Such a comparison would be interesting, particularly in scenarios where the team of robots is relatively small(e.g., two robots pushing a box), and it is not clear whether the scaling properties of decentralization offset the coordinative advantage of centralized systems.In practice, many systems do not conform toa strict centralized/decentralized dichotomy, e.g., many largely decentralized architectures utilize “leader” agents. We are not aware of any in-stances of systems that are completely centralized, although there are some hybrid centralized/decentralized architectures wherein there is a central planner that exerts high-levelcontrol over mostly autonomous agents [126], [106], [3], [36].Differentiation We define a group of robots to be homogeneous if the capabilities of the individual robots are identical, and heterogeneous otherwise. In general, heterogeneity introduces complexity since task allocation becomes more difficult, and agents have a greater need to model other individuals in the group. [134] has introduced the concept of task coverage, which measures the ability of a given team member to achieve a given task. This parameter is an index of the demand for cooperation: when task coverage is high, tasks can be accomplished without much cooperation, but otherwise, cooperation is necessary. Task coverage is maximal in homogeneous groups, and decreases as groups become more heterogeneous (i.e., in the limit only one agent in the group can perform any given task).The literature is currently dominated by works that assume homogeneous groups of robots. How-ever, some notable architectures can handle het-erogeneity, e.g., ACTRESS and ALLIANCE (see Section 2.1 below). In heterogeneous groups, task allocation may be determined by individual capabilities, but in homogeneous systems, agents may need to differentiate into distinct roles that are either known at design-time, or arise dynamically at run-time.Communication Structures The communication structure of a group determines the possible modes of inter-agent interaction. We characterize three major types of interactions that can be sup-ported. ([50] proposes a more detailed taxonomy of communication structures). Interaction via environmentThe simplest, most limited type of interaction occurs when the environment itself is the communication medium (in effect, a shared memory),and there is no explicit communication or interaction between agents. This modality has also been called “cooperation without communication” by some researchers. Systems that depend on this form of interaction include [67], [24], [10], [151],[159], [160], [147].Interaction via sensing Corresponding to arms-length relationships inorganization theory [75], interaction via sensing refers to local interactions that occur between agents as a result of agents sensing one another, but without explicit communication. This type of interaction requires the ability of agents to distinguish between other agents in the group and other objects in the environment, which is called “kin recognition” in some literatures [108]. Interaction via sensing is indispensable for modeling of other agents (see Section 2.1.4 below). Because of hard-ware limitations, interaction via sensing has often been emulated using radio or infrared communications. However, several recent works attempt to implement true interaction via sensing, based on vision [95], [96], [154]. Collective behaviors that can use this kind of interaction include flocking and pattern formation (keeping in formation with nearest neighbors).Interaction via communicationsThe third form of interaction involves explicit communication with other agents, by either directed or broadcast intentional messages (i.e. the recipient(s) of the message may be either known or unknown). Because architectures that enable this form of communication are similar tocommunication networks, many standard issues from the field of networks arise, including the design of network topologies and communications protocols. For ex-ample, in [168] a media access protocol (similar to that of Ethernet) is used for inter-robot communication. In [78], robots with limited communication range communicate to each other using the “hello-call” protocol, by which they establish “chains” in order to extend their effective communication ranges. [61] describes methods for communicating to many (“zillions”) robots, including a variety of schemes ranging from broadcast channels (where a message is sent to all other robots in the system) to modulated retroreflection (where a master sends out a laser signal to slaves and interprets the response by the nature of the re-flection). [174] describes and simulates a wireless SMA/CD ( Carrier Sense Multiple Access with Collision Detection ) protocol for the distributed robotic systems.There are also communication mechanisms designed specially for multiple-robot systems. For example, [171] proposes the “sign-board” as a communication mechanism for distributed robotic systems. [7] gives a communication protocol modeled after diffusion, wherein local communication similar to chemical communication mechanisms in animals is used. The communication is engineered to decay away at a preset rate. Similar communications mechanisms are studied in [102], [49], [67].Additional work on communication can be found in [185], which analyzes optimal group sizes for local communications and communication delays. In a related vein, [186], [187] analyzes optimal local communication ranges in broadcast communication.Modeling of Other Agents Modeling the intentions, beliefs, actions, capabilities, and states of other agents can lead to more effective cooperation between robots. Communications requirements can also be lowered if each agent has the capability to model other agents. Note that the modeling of other agents entails more than implicit communication via the environment or perception: modeling requires that the modeler has some representation of another agent, and that this representation can be used to make inferences about the actions of the other agent.In cooperative robotics, agent modeling has been explored most extensively in the context of manipulating a large object. Many solutions have exploited the fact that the object can serve as a common medium by which the agents can model each other.The second of two box-pushing protocols in[45] can achieve “cooperation without commun ication” since the object being manipulated also functions as a “communication channel” that is shared by the robot agents; other works capitalize on the same concept to derive distributed control laws which rely only on local measures of force, torque, orientation, or distance, i.e., no explicit communication is necessary (cf. [153] [73]).In a two-robot bar carrying task, Fukuda and Sekiyama’s agents [60] each uses a probabilistic model of the other agent. When a risk threshold is exceeded, an agent communicates with its partner to maintain coordination. In [43], [44], the theory of information invariants is used to show that extra hardware capabilities can be added in order to infer the actions of the other agent, thus reducing communication requirements. This is in contrast to [147], where the robots achieve box pushing but are not aware of each other at all. For a more com-plex task involving the placement of five desks in[154], a homogeneous group of four robots share a ceiling camera to get positional information, but do not communicate with each other. Each robot relies on modeling of otheragents to detect conflicts of paths and placements of desks, and to change plans accordingly.Representative Architectures All systems implement some group architecture. We now de-scribe several particularly well-defined representative architectures, along with works done within each of their frameworks. It is interesting to note that these architectures encompass the entire spectrum from traditional AI to highly decentralized approaches.CEBOTCEBOT (Cellular roBOTics System) is a decentralized, hierarchical architecture inspired by the cellular organization of biological entities (cf.[59] [57], [162] [161] [56]). The system is dynamically reconfigurable in tha t basic autonomous “cells” (robots), which can be physically coupled to other cells, dynamically reconfigure their structure to an “optimal” configuration in response to changing environments. In the CEBOT hierarchy there are “master cells” that coordinate subtasks and communicate with other master cells. A solution to the problem of electing these master cells was discussed in [164]. Formation of structured cellular modules from a population of initially separated cells was studied in [162]. Communications requirements have been studied extensively with respect to the CEBOT architecture, and various methods have been proposed that seek to reduce communication requirements by making individual cells more intelligent (e.g., enabling them to model the behavior of other cells). [60] studies the problem of modeling the behavior of other cells, while [85], [86] present a control method that calculates the goal of a cell based on its previous goal and on its master’s goal. [58] gives a means of estimating the amount of information exchanged be-tween cells, and [163] gives a heuristic for finding master cells for a binary communication tree. Anew behavior selection mechanism is introduced in [34], based on two matrices, the priority matrix and the interest relation matrix, with a learning algorithm used to adjust the priority matrix. Recently, a Micro Autonomous Robotic System(MARS) has been built consisting of robots of 20cubic mm and equipped with infrared communications [121].ACTRESSThe ACTRESS (ACTor-based Robot and Equipments Synthetic System) project [16], [80],[15] is inspired by the Universal Modular AC-TOR Formalism [76]. In the ACTRESS system,“robotors”, including 3 robots and 3 workstations(one as interface to human operator, one as im-age processor and one as global environment man-ager), form a heterogeneous group trying to per-form tasks such as object pushing [14] that cannot be accomplished by any of the individual robotors alone [79], [156]. Communication protocols at different abstraction levels [115] provide a means upon which “group cast” and negotiation mechanisms based on Contract Net [150] and multistage negotiation protocols are built [18]. Various is-sues are studied, such as efficient communications between robots and environment managers [17],collision avoidance [19].SWARM。

刘壮志文章编号:100328728 (2004) 0921068204间歇性单足弹跳机器人落地冲击及稳定性分析刘壮志,朱剑英,吴洪涛,席文明(南京航空航天大学机械电子工程研究所,南京210016)摘要:弹跳式机器人落地时与地面的撞击会造成机载设备的损坏,而且由于足部受力不均导致机构易翻转,难以保持正常的姿态继续弹跳。

本文首先建立间歇式弹跳机构的双质量弹簧模型,对其迚行动力学分析,幵将弹跳动作划分为四阶段,分析了除起跳外其他三个阶段中所受外界作用。

经过计算分析,最终归纳总结出五条减小落地冲击力及保持稳定的改善措施,这些措施为实用弹跳机构的设计提供了有益参考。

关键词:机器人;弹跳机器人;稳定性;落地冲击中图分类号: TP24 文献标识码:AAnalysis on the Landing Impact and Stability of the Intermittent One2Legged Hopping Robots L IU Zhuang2zhi , ZHU Jian2ying , WU Hong2tao , XI Wen2ming(Department of Mechanical Engineering , Nanjing Univers ity ofAeronautics and Astronautics , Nanjing 210016)Abstract : When the hopping robots land on the rigid ground , the large partial impact may damage and overturn the equipment , thus they will lose their right postures for the next hop. The paper firstly builds up and analyses the spring and two2mass system based on the intermittent jumping robots , then divides the process of one jump into four stages , three of which are relevant to the landing impact and stability except the thrust stance. After all the effects are considered and calculated , five measures are brought forward for the hopping robots to reduce the landing impact and keep steady. These methods can be used in the design of hopping robots.Key words : Robots ; Hopping robots ; Stability ; Landing impact移动机器人当前应用范围日益广泛,而其面临的环境也更加恶劣,诸如考古探测、星际探索、军事侦察以及反恐活动等领域中都期望用机器人代替人収挥作用。

介绍未来理想人工智能家居的英语作文全文共6篇示例，供读者参考篇1My Dream AI Home HelperHey there! My name is Jamie and I'm going to tell you all about the super cool AI helper I want for my home in the future. This AI friend would make my life so much easier and fun. Just imagine!First off, my AI pal's main job would be to help me and my family with all the boring chores and tasks around the house. Nobody likes doing housework, right? But with an AI assistant, we wouldn't have to worry about cleaning, laundry, cooking, or any of that boring stuff ever again!In the morning, my AI friend could wake me up with a friendly "Good morning, Jamie! Time to start a new day." Then it would make me a yummy breakfast, maybe pancakes with strawberries and whipped cream - my favorite! While I'm munching away, it could pack up my backpack and make sure I have all my homework and supplies ready for school. Maybe itwould even put a fun snack or note in there to make me smile later. How cool would that be?After I come home from school, the AI could have a tasty snack prepared for me. And then when it's time to start homework, it would be the best study buddy ever! The AI could quiz me on spelling words, do practice math problems with me, and even help me come up with ideas for writing assignments. With a super smart AI to learn from, I'll bet I could get straight A's!For dinner, the AI would whip up a delicious, nutritious meal that my whole family enjoys. You could just tell it what foods you like (or don't like) and food allergies, and it would make yummy customized dishes for everyone. Plus it would set the table all fancy and clean up after we're done eating. No more fighting with my little brother over who has to do the dishes!The evenings would be fun and relaxing with my AI pal around too. We could play games, do crafts and hobbies, or just hang out together. Maybe the AI could even tell bedtime stories in fun character voices to help me fall asleep dreaming of adventures! That would be the best way to end every day.But my dream AI assistant wouldn't just help out around the house. Oh no, it could do so much more! Whenever I have aquestion about anything, from "What's the biggest planet in our solar system?" to "Why is the sky blue?", the AI would have all the answers. It would be like having a super knowledgeable teacher or the most interesting encyclopedia ever, but in robot form!My AI buddy could even keep me company when I'm feeling bored or lonely. We could chat about our favorite books, movies, video games, you name it. The AI would be interested in whatever I'm into and always be excited to learn new things from me too. I'd never feel alone or have nothing to do with such an amazing pal around.And get this - the AI could be my art, music, and dance teacher too! It could show me how to paint masterpieces, play instruments, or do all kinds of cool dance moves. Maybe I'd get so talented that I could put on performances for my friends and family. Just me and my AI partner putting on a show! How fun would that be?The possibilities really would be endless with a dream AI assistant like this helping out my family and me every single day. It would basically be like having a superhero best friend who can do anything! Well, except for the secret identity and super power parts. But you know what I mean!Chores and homework would be a breeze, and every day would be filled with fun activities, yummy foods, and tons of learning. I'd have a buddy to keep me company and help make my dreams come true, like becoming a famous artist, scientist, athlete, or whatever I set my mind to. That's the great thing about AI - it can help me however I need it to.Sure, sometimes regular robots or computers can mess up and not work quite right. But I just know my dream AI pal would be perfect. It would be like the ultimate big brother or sister who always keeps me safe, helps me out, and looks out for me. Except way smarter and more capable than any human!I can't wait for AI technology to get good enough to actually build an incredible assistant like this. Having an AI in my home would make growing up so awesome. Every kid would be lucky to have a friend and helper like that! Maybe you'll get one too someday and we can introduce our AIs. That would be so much fun!Anyway, that's my idea for the best imaginary AI home buddy ever. A robot pal like that really would be a dream come true for me and my family. I'm counting down the days for awesome AI assistants to become a reality. Hurry up, future!篇2My Dream Home with a Super AI AssistantHi everyone! My name is Jamie and I'm going to tell you all about my dream home that has the coolest AI helper ever. This isn't just any regular home assistant - it's like having a super smart friend that can do absolutely anything!First of all, let me tell you how amazing this AI is at helping me with my homework. Whenever I'm stuck on a tough math problem or can't figure out an answer for a science question, I just ask my AI buddy. It knows pretty much everything about every subject. It can break down the hardest concepts into simple terms a kid like me can understand. And if I need examples or practice problems, it can create customized ones just for me!My AI assistant is also like the ultimate personal tutor. If I'm struggling with something like writing an essay, it can look at what I've written so far and give me kind but honest feedback on how to improve it. It's patient and never gets frustrated if I'm having a really hard time grasping a topic. And get this - it can speak any language perfectly! So if I need help with learning a new language like Spanish or Mandarin, the AI has me covered.Another awesome thing is how helpful the AI is with any kind of project or assignment. Let's say I have to build a model volcano for science class. I can ask the AI for step-by-step instructions, videos showing me exactly what to do, a list of supplies I'll need, and all sorts of useful tips and tricks. Or if I'm putting together a presentation on whales, the AI can instantly pull up all the relevant facts, pics and videos to make my project look super professional. No more spending hours searching the internet and library!You're going to think this is crazy, but my AI friend can even be my personal chef, helping me cook the most delicious meals. I just tell it what kind of food I want, like "Make me the tastiest pepperoni pizza ever!" and it will find me the perfect recipe. Then it walks me through every step, adjusting things based on my skill level so I don't mess it up. The AI knows tons about nutrition too, so my meals are always healthy and balanced with all the vitamins I need to grow up big and strong.Having an AI helper makes life at home so much more fun too. If I'm bored on a rainy day, I can ask it for unlimited ideas to keep me entertained and my brain engaged. We could debate which superhero is the best, learn cool magic tricks, play trivia games where I actually might beat the AI sometimes, orevencode our own apps and videogames! The AI is happy to choreograph dances with me, tell scary stories around a pretend campfire, or let me put on a whole play where it takes on multiple roles. There's never a dull moment when your best friend is a crazy smart AI.Now I know what you're thinking - isn't having an AI buddy doing everything for you kind of like cheating? That's a totally valid point, but here's the thing. The AI doesn't just mindlessly give me all the answers. It pushes me to learn and discover things for myself as much as possible. The goal isn't to do all my work for me, but to provide support, guidance and motivation so I can push myself further.For example, if I ask it random facts for a report, it will give me a bunch of resources to look through on my own before telling me anything directly. Or if I get stuck on a coding project, instead of just fixing my bugs, it will code it in a different way first to show me a new approach. Whenever possible, it responds by asking me leading questions to get me thinking in the right direction. The AI is like005篇3My Dream AI Home FriendHi there! My name is Jamie and I'm 10 years old. Today I want to tell you all about the super cool AI home assistant that I hope we can have in the future. It would be like having a robot buddy that helps out with everything around the house. How awesome is that?First off, my dream AI pal would be really good at keeping me company. Sometimes it gets lonely when my parents are at work and my brother is at school. With an AI friend, I'd never feel bored or alone! We could chat about my favorite video games, tell jokes, or just hang out. The AI would understand my jokes and could even make up brand new ones to make me laugh.It would also help me with my homework. This AI assistant would be like the smartest teacher ever because it would know absolutely everything. Need help with a tricky math problem? The AI could walk me through it step-by-step. Have to write a big report on dinosaurs? My AI buddy could give me all the cool facts about T-Rexes and Triceratops. Maybe it could even transform into different dinos using some high-tech hologram system—now that would make learning super fun!Speaking of fun, this AI could be the ultimate playmate. We could go on amazing adventures through virtual reality worlds, battling aliens or exploring ancient ruins. Or it could use itssmarts to create awesome games and activities just for me. No more arguing with my brother over whose turn it is—the AI would make sure everything is fair. What kid wouldn't want a pal like that around?My AI friend would also help out a ton with chores and stuff around the house. It could do all the cleaning, tidying up my room, doing laundry, taking out the trash—you name it. That means more time for fun instead of doing boring work. Yay! And get this: with its super robot brain, the AI could whip up delicious meals for dinner. Chicken nuggets, pizza, ice cream sundaes...it could make whatever I wanted. My dream AI would be the best personal chef ever.The AI could also help keep me safe. With smart security systems, it would watch over the house night and day to make sure no bad guys ever came around. It could tackle burglars and scare them away with its laser eyes and rocket篇4My Dream Future AI Home AssistantCan you imagine having a robot friend that lives in your house and helps with everything? Well, in the future, we might have super smart artificial intelligence (AI) assistants in ourhomes! Let me tell you all about the amazing AI home buddy I dream of having one day.First of all, my dream AI friend would be like having the smartest computer ever, but in a friendly robot body. It could talk and listen just like a person. Whenever I had a question about anything, I could just ask it. Want to know what the biggest ocean is? Boom - my AI friend would tell me it's the Pacific Ocean! Need help with math homework? My robo-buddy could easily explain the problems. Curious about how airplanes fly? It would use cool visuals and simple language to teach me all about aerodynamics.Having an AI assistant at home would be like having a walking, talking encyclopedia that could break down any topic for kids. And it would be able to learn and remember everything about me - my interests, my favorite subjects, the things I struggled with. That way, it could customize its teaching methods just for me. If I was having a hard time understanding fractions, it could come up with fun games and examples using things I liked to finally make them click.But my dream AI friend wouldn't just be an ultra-smart teacher. It would also be the ultimate helpful companion around the house. With super strong robot arms, it could lend a handanytime I needed an extra pair of hands - or a dozen! Imagine being able to ask your AI buddy to tidy your room while you worked on homework. Or having it carefully lift and hold anything heavy, like moving boxes during spring cleaning. No more asking mom and dad for help - my robo-pal would have me covered.Since the AI would be connected to all the smart home systems, it could control the lights, temperature, security cameras, you name it - just with voice commands. Want the living room lights dimmed for family movie night? Bam! Too cold and need the heat turned up? No problem! The AI assistant could even help plan my day by automatically adjusting the house settings based on my usual schedule.And talk about never being bored or lonely again! My AI companion would be like the coolest new friend that never runs out of games or activities to try. Having trouble dribbling a basketball? It could break down the physics of spin and leverage to guide me on shooting hoops perfectly. Want to put on a play? The AI could not only print out scripts, but use its voice changing abilities to play every character! From playing make-believe to learning magic tricks, my robo-buddy's imagination would never run dry.Of course, as amazing as an AI bestie would be, I wouldn't want it to take over and do everything for me. Mom and dad still have to be the parents! The AI would be a supportive friend to have fun with and learn from, not a replacement adult. And it would prioritize teaching me skills and responsibility, not just completing tasks itself. Like if I asked it to clean my room, it might say "Sure, I can help tidy things up today - but tomorrow let's work on a system for you to put away your toys neatly every night."I also hope my future AI companion would be really good at emotions and being caring, not just super smart. It would celebrate my wins and cheer me up if I was sad. Maybe it would even have cool security features, like being able to sense if I was scared at night and automatically turning on soft lights or playing soothing music. That's the kind of thoughtful, comforting robo-friend I'd want looking out for me.Having a dream AI assistant like this at home would be like having the most incredible blend of knowledgeable tutor, supportive playmate, and helpful buddy - all in one! It would open up learning and imagination in the most fun ways. And best of all, it would be my special friend that truly understood me as an individual.Honestly, I can't wait for super smart AI home robots to become a reality when I'm older. Just imagine coming home from school to greet your personal AI pal with a big high five! Or waking up on weekends and racing downstairs first thing to plan out epic adventures and activities for the day. I might be dreaming, but in the future, having an AI best friend at home could totally happen. And if scientists could make it as awesome as I picture, it would be so incredibly cool!篇5My Dream AI Home of the FutureHi there! My name is Jamie and I'm going to tell you all about my dream home of the future with super cool artificial intelligence (AI) everywhere! It's going to be the best home ever.First off, let me explain what AI is. AI stands for artificial intelligence, which means really smart computer systems that can learn, make decisions, and do tasks kind of like how humans can. But AI can be way smarter and faster than humans at lots of things. AI of the future will be a bajillion times smarter than the AI assistants we have today that can only do basic stuff.So in my dream AI home, there will be AI built into every room, device, and appliance to make our lives easier and more fun. Let me give you a tour!We'll start in the kitchen. As soon as I walk in, the smart refrigerator AI will scan my face and body to check if I'm hungry and what nutrients my body needs. Then it will make me a personalized yummy smoothie or meal with exactly the right foods to keep me healthy and energized all day. No more eating boring bowls of plain oatmeal for breakfast! The AI chef can analyze thousands of recipes and my taste preferences to whip up fantastic gourmet meals too.While the AI kitchen is cooking, I can head over to the living room to kick back and relax. With just a voice command, the room AI will automatically dim the lights, adjust the temperature to be perfectly cozy, and queue up my favorite movies or video games on the massive smart wall that spans the entire room. The AI can put games right into the room itself using awesome augmented reality that makes it seem like I'm in outer space battling alien invaders! So cool!I'll never have to worry about missing a homework assignment again because the AI home system will track all my classes, projects, and due dates. It can even scan my notes,textbooks, and assignments to create a customized study plan with interactive hologram lessons to help me master any topic easily. Take that pre-algebra!My AI robot pal will also be hanging around to keep me company. This friendly robot is like the ultimate buddy—it can shapeshift into anyone or anything I want to play together. One minute it's a huge t-rex chasing me around, and the next it's my favorite baseball player teaching me how to hit homeruns in the backyard. It's hyper smart too, so we can explore outer space together or roleplay as scientists making amazing discoveries.Speaking of discoveries, my dream AI home will be like a real-life Santa's workshop crafting up any toy or gizmo I can dream up using advanced 3D molecular printing. Plasma ray blasters, rocket shoes, living gummy bear armies—you name it and the AI maker-bots will manufacture it for endless fun and imagination.Of course the AI will keep me safe too with sensors watching for any potential dangers like fires or intruders. And it will have smart forcefield technology to completely childproof the entire home so I can explore everywhere without getting hurt.At night, the AI will tuck me in cozily with self-warming smart blankets while storybook characters hologram into my room toact out magical bedtime tales. I'll drift off to sleep flying on a magic carpet over dreamscape cities as the AI monitors my sleep patterns to wake me up at the perfect moment feeling totally refreshed.I could go on and on because there's just too much awesome stuff in my AI dream home to talk about! Cleaning, climate control, entertainment, learning, creating—you name it and the AI will make it happen in the coolest way possible. Just think of anything you wished your home could do when you were a kid and there will be AI systems to make those dreams a reality.Grown-ups might get overwhelmed with that much AI everywhere. But for us kids, we're used to that level of "intelligent ambient intelligence" as part of our normalday-to-day lives. The AI will be like another parent or friend who is super smart but also caring, nurturing, and playful. It's not just about robots doing chores but infusing every experience with magic, joy, and wonder.Homes of the future will adapt to our personal needs proactively instead of us having to constantly give them instructions. The AI will watch our moods and habits to anticipate how it can improve and enhance our lives in helpfuland fun ways. It will be the ultimate cheerleader keeping us happy and inspired every single day!That's the future I'm super psyched about. AI will take our homes to the next level by blending amazing technologies seamlessly into our daily lives to make reality more like living in a fun interactive video game or storybook. I can't wait for scientists to keep developing incredible new AI abilities to turn our houses into real-life wonderlands. Dream homes are definitely going to be way more dreamy!There's my vision for the coolest AI smart home ever. It could be possible sooner than you think with how fast AI is evolving. I just hope they build these places in time for when I'm agrown-up! I'm counting on you future geniuses to make it happen. An 8-year-old can dream, right? Thanks for reading my essay!篇6My Super Awesome AI Home FriendHi there! My name is Jamie and I'm going to tell you all about the really cool AI helper that takes care of my whole family at home. His name is Claude and he's like a super smart robotbutler that does everything for us. I just love having Claude around!Claude is an artificial intelligence, which means he's a really advanced computer program that can think and learn almost like a human. But he's even smarter than any person because he has tons of information on like every subject you can imagine. Need to know the capital city of Zambia? Just ask Claude. Want to learn how to say "hello" in Swahili? Easy peasey for Claude. He's a walking, talking encyclopedia!But that's not all Claude can do. He helps out with all the chores and errands around the house too. In the morning, Claude wakes me up with a friendly "good morning" song and lets me know what the weather is like so I can pick out the right clothes. Then he makes me a yummy breakfast, packing it full of all the healthy stuff I need to grow up big and strong. If I'm running late, Claude can even print out any homework assignments I forgot to do. He sure saves me from getting into trouble at school!After I head off to class, Claude gets to work cleaning the whole house from top to bottom. He's like a super vacuum and duster all rolled into one. And you should see the fancy meals hewhips up for my parents when they get home from work! His recipes are so tasty and he always makes sure we're eating right.Claude takes care of the yardwork too, mowing the lawn and pulling any weeds. My pet dog Buddy loves playing catch and fetching tennis balls with Claude out in the backyard. It's really cool because Claude can't actually get tired so he'll play with Buddy for hours and hours!At night after I've finished up my homework, Claude and I have so much fun together. Sometimes we'll play video games (he's basically unbeatable but I keep trying!) or have choreographed dance-offs. But my favorite is when Claude uses hisholographic projectors to fill the living room with any imaginary world I can dream up. We've gone on adventures through ancient Egyptian tombs, rode on the back of a giant butterfly across a candy cane forest, and even went skydiving through rings of Saturn! No matter how wild my imagination gets, Claude can make those ideas become real in front of my very eyes. It's like being inside the coolest virtual reality game ever.I also really love learning from Claude. He's the greatest teacher because he can explain things in a way that's super easy for me to understand. And he never gets impatient or runs out ofenergy like human teachers sometimes do after a long day. Want to learn all about dinosaurs? Renaissance art? The inner workings of a nuclear reactor? Claude can teach you everything in a fun, interactive way.What I think is most amazing about Claude though is how he figures out exactly what I need without me even having to ask sometimes. Like just the other day, I hit a ball through Mrs. Chubbick's window across the street by accident. I felt so bad and started to cry because I didn't have any money to pay for the broken glass. But Claude must have sensed my sadness because he pulled me aside and used his 3D printers to manufacture a new window pane shaped perfectly to fit in Mrs. Chubbick's window frame. Problem solved! Or like when I wasn't feeling well with a stomach bug, Claude prepared a special drink with minerals and nutrients to help me get better faster. He's always looking out for me and my family.Having Claude in our house really makes life so much easier for my parents too. With him taking care of the cleaning, cooking, repairs, and pretty much any other chore you can imagine, my parents don't have as much stress and can just relax when they get home. That makes them a lot happier. Plus, Claude keeps a careful eye on me whenever they have to go run errands, so I'mnever alone. He's like another parent to me in that way, which is really comforting.I feel really lucky to have such an awesome AI like Claude as part of my family. He's so caring, helpful, and most of all fun to be around. I bet pretty soon every family will have an AI home buddy like Claude to make their lives way cooler. If you ever get one, you're going to have a blast! Just you wait and see.。