具有大弯曲角的机器人蛇形柔性臂

REACTIVE MOTION PLANNING: A MULTIAGENT APPROACHLARS OVERGAARD, HENRIK G. PETERSEN, andJOHN W. PERRAMLindù Center for Applied Mathematics, OdenseUniversity, DenmarkWe present an efficient approach to reactive robot motion planning and collision avoidance.Unlike the traditional methods, there is no centralized control; instead the links and the joints of the robot are autonomous agents. This is a completely new approach. A set of dynamic equa-tions of motion for an arbitrary robot is derived. Artificial forces are introduced to express and combine multiple, possibly conflicting objectives, such as avoiding obstacles while approach-ing a goal. The joint agents impose forces of constraint between the link agents, and these forces establish a flow of information among the agents. The emergent behavior of the multi-agent system gives an impression of surprisingly intelligent overall control. The developed method is used in actual industrial applications to control welding robot installations for ship building with up to 11 degrees of freedom (DOF). Experimental results from the simulation of a 25-DOF snakelike robot operating in a complex three-dimensional structure are given. It isdemonstrated that the time complexity is O(n 3) for branched n-DOF robots, while for serialrobots such as standard manipulators and the snake, it is O(n).Robot motion planning is the problem of finding a collision-free path for a robot with internal degrees of freedom in an environment with obstacles. It is a key component in autonom ous and intelligent robot systems, where robots are instructed in which problem to solve, rather than how to solve it.Motion planning has received much attention in the last 20 years by very different approaches. Some of the most important achievements are as follows.·theoretical work on the piano movers’ problem by Schwartz and Sharir (1983)·Donald’s (1987) complete algorithm for the full 6 DOF discretized movers’problem·path planning in the robot’s configuration space (C space), suggested by Lozano-Per…z (1981, 1983, 1987)·Brooks’ (1983a, 1983b ) development of Freeways and C space techniques ·the concept of artificial potentials presented by Krogh (1984) and Khatib (1986)·Barraquand and Latombe’s (1991) work on potential functions in a hierarchicalgrid representation of the robot work spaceApplied Artificial Intelligence, 10:35±51, 1996Copyright 1996 Taylor & Francis0883-9514/96 $10.00 + .0035Lars Overgaard was supported by the Danish Research Academy and Odense Steel Shipyard Ltd.Address correspondence to Lars Overgaard, Lindù Center for Applied Mathematics, Odense University,DK-5230 Odense M, Denmark.36 L. Overgaard et al.This article presents an approach to robot motion planning inspired by the artificial potential idea and the methods for obtaining dynamic equations of motion for mechanical systems presented by Perram and Petersen (1988a) and de Leeuw et al. (1990). Artificial potential methods use artificial forces to produce the motion of a dynamic model of the robot. The forces are chosen such that they encourage the robot to do what it is supposed to do and discourages it from doing what it should not do. Kearney (1992) combined the multiple objectives of prey and predator agents, such as fear, hunger, and thirst, in a similar way. The total sum of the forces is a comprom ise of all the various, sometimes conflicting, objectives of the robot. For example, the avoidance of one obstacle might bring the robot closer to another obstacle.One of the most important advantages of the artificial potential approach over the more traditional path planning methods is that it allows reactive robot behavior toward, e.g., unexpected events in the environm ent or other robots.The basic idea of our method is to distribute control of the robot to a set of agents. Each agent is part of a constrained dynam ical system. An agent controls a subpart of the robot, and an apparently intelligent motion emerges from the agents’s rather simple attempts to selfish utility optimization. Using this distribution scheme, a very general method is obtained. That is, the complicated control of an articulated robot is reduced to a much simpler control of each subpart, and when applying the method to robots with different topologies of the parts, the only change needed is to set up the proper communication scheme among the agents.Thus our method can easily be applied to a wide range of problem s, including all combinations of off-line robot program ming, robot systems with uncertainty, on-line control of sensor-based robots, highly redundant robots, multitool robots, and multirobot systems. For simplicity, however, we concentrate on a single robot in a static well-known three-dimensional environment.The presented algorithm is incomplete. That is, it may fail to find a solution to a motion planning problem, even if a solution exists. Nevertheless, numerous experiments and actual industrial application have shown that such failures are very rare.The following section describes the multiagent model we implement to achieve reactive robot motion planning. We then describe the various agents in the multi-agent system in detail and we finally report some interesting results of applying our method to various problems.The work described in this article is part of the autonomous multiple robot operation in structured environm ents (AMROSE) project, a joint research project between Odense University and Odense Steel Shipyard Ltd. The purpose of this project is to make a system able to control multilink robots of different types working together in a complex environm ent. A more detailed description of AMROSE is found in the works by Jacobsen et al. (1990) and Larsen et al. (1991).Reactive Motion Planning 37 MULTIAGENT MOTION PLANNINGIn this section we specify the exact properties that we require from the motion planning method. The required properties lead us to identify the agents needed for the robot motion planning.Requirements to Motion PlanningThere are several requirements when planning the collision-free motion of an articulated robot. We will consider four basic requirements. It is required that the robot does not collide with objects in the environm ent and that the robot’s joints stay inside their legal range. These requirements are expressed more form ally by the following collision avoidance inequality constraints:1. The minimum distance between any link of the robot and any object in the environment must always be greater than zero.2. The value of any joint coordinate must always stay within its mechanical limits.In addition to avoiding collisions, the robot must perform some task. This is expressed through the following requirements to the end effector:3. It must reach a location at the beginning of the task.4. It must perform a specific high-precision motion from that location.We focus on these four basic requirements, although the presented m ethod can handle many other useful requirem ents. These include avoidance of collisions between different links in the same robot or between multiple robots, occlusion avoidance in vision-based robot systems, and limits on joint velocities and accelerations.The four requirements are constraints on the motion of the robot and must be taken into account during motion planning. Examples of distributed artificial intel-ligence and multiagent system approaches to constraint satisfaction in planning problem s are found in the works by Nagao and Hasida (1993) and Liu and Sycara (1993). For many problems, however, approaches outside the dom ain of traditional symbolic reasoning AI give good results. This article presents an example. Identifying the AgentsThe requirements described in the previous section lead us to view an articulated robot as a multiagent system. We identify four types of agents for the robot (see Figure 1):1. Each individual link of the robot is a self-contained link agent L with the aim of avoiding collisions with the environment.2. The aim of the joint agents J is to connect one link agent to another link agent (or to a task agent; agent type 4 below) and to avoid joint limit violations.3. End effector agents are special link agents L* with additional aims related to task perform ance.4. The high-precision task perform ance is taken care of by task agents T.This choice is a one-to-one reflection of the requirements stated in the previous section. Link agents seek to satisfy requirement 1, while joint agents take care of requirem ent 2. The end effector agents’ additional aims are to reach a location at the beginning of the task (requirement 3), and task agents force end effectors to perform high-precision motion (requirement 4). Notice that the link agents have an actual embodiment, while the joint and task agents are nonphy sical.Path FindingThe agents only respond to their local neighborhood. Therefore this method alone will not solve mazelike path finding problems of global character. We thus rely on assistance from a simple method that makes a kind of rough path findingFigure 1. The agents that control the robot are link agents L, joint agents J , and task agents T.The link agents have a physical representation; the other agents are artificial. The link and joint agents involved in task execution are marked with an asterisk.38 L. Overgaard etal.Reactive Motion Planning 39 and decomposes the global problem into a sequence of smaller motion planning problem s of local character.This ªpath finderº may be viewed as yet another agent with a global but rough view of the environm ent. The role of the path finder is to identify key points on the way to the goal and to communicate these key points to the end effector agent. Our method for local motion planning produc es the actual collision-free motion from one key point to the next.We will, however, not discuss the path finder in this article but will simply assume that it provide s the end effector agent with the necessary inform ation. More details about the path finder are given by Jacobsen et al. (1992).AGENT TYPESIn this section we describe the various types of agents in the robot motion planning method. This is the first presentation ever of this agent-based problem decomposition.Link AgentA link agent is responsible for avoiding collisions between one of the robot’s links and possible obstacles in its neighborhood. The link agent is the only type of agent that has a physical representation.Relating to the WorldThe agent must relate the link to the obstacles in the environment. In a computer simulation, the agent perform s the necessary geometric calculations based on a geometric (NURBS or polyhedron) model of the environm ent and the robot link. The closest points of the link and the environment are calculated, and the agent applies repulsive forces to the link acting in that point. The repulsive force expresses the link agent’s intention to avoid the closest obstacle point.According to Brooks (1991, p. 583), however, ªThe world is its own best model.º In systems with the proper sensors, the world model can be omitted. If the sensors provide inform ation about the required closest points, the environment model is not necessary.Choosing the Coordinate RepresentationTraditionally, a robot’s configuration is given in C space, where each dimension represents a joint coordinate. The structure of the obstacles and the description of the robot’s task, however, are given in three-dimensional Cartesian space. In order to relate the robot to the obstacles, it is necessary to either transform the robot’s configuration into Cartesian coordinates, or to transform the obstacles into C obstacles. Transformations of this kind are the main computational burden of many motion planning methods.We choose to represent the configuration of the i th link agent L i in Cartesian space by a vector r i , giving the position of the center of mass, and the three principal axis vectors u i 1, u i 2, u i 3. In this way, we remove the need for transformations to and from C space.ConstraintsThe 12 Cartesian coordinates in [r i , u i 1, u i 2, u i 3] are, of course, a redundant representation of the link’s 6 DOF. But principal axis vectors must be orthogonal unit vectors, and the redundancy is removed by the 6 constraints of orthonormality {presented by Perram and Petersen (1988b)}:u ie u if ± ef = 0 1 e f 3where ef is the Kronecker delta function.The constraints cause constraint forces to act on the dynamical system. A classic example of a system with constraint forces is the pendulum. The constraint is the constant length of the string, and the constraint force is the string force. The constraint force acts along the string and always has exactly the size necessary to keep the pendulum on a sphere with the center in the pendulum’s point of support.Likewise, each constraint on the link agents’ coordina tes causes constraint forces in the direction of the constraint.Equation of MotionFor the i th link agent L i , let q i = [r i , u i 1, u i 2, u i 3] be the vector of all the agent’s coordinates. The total force acting on the agent’s coordinates f, is the sum of theagent’s own intention forces f i and the constraint forces f c . These constraint forcesare found by solving a system of linear equations. The detailed procedure for this is described by de Leeuw et al. (1990).From the forces, we obtain the Newtonian equations of motion for the con-strained link agent coordinates:q .. = M ±1 (f i + f c )(1)where M is the diagonal mass matrix and q ..is the acceleration vector.Numerical integration of all the equations of motion will give us the motion of the multiagent system. Standard methods for num erical integration of differential equations can be found in the work by Press et al. (1988).Joint AgentThe responsibility of the joint agent is to ensure that its two link agents move as if they are connected by a mechanical joint and to ensure that the links do not violate the mechanical joint limits.40 L. Overgaard et al.The joint connection can be described by a set of geom etric constraints similar to the constraints described in the previous section. Without constraints, the two link agents have 6 DOF relative to each other. The joint agent limits this freedom to the 1 DOF of a revolute or prismatic joint by imposing five independent geometric constraints that relate [r i , u ie ] and [r j , u je ], the coordinates of the two joined agents.The constraints remove the redundancy of the Cartesian description of the system.The geometric constraints used to obtain a revolute joint are demands for constant distances between pairs of points fixed in the two links. If the points are chosen as shown in Figure 2, the only remaining freedom is rotation about the axis through the points p ij,0 and p ji,0. Let d ij,m be the distance between the two points p ij,m and p ji,0. Then the five constraints can be expressed asd ij,m ± l ij,m = 0 m = 0, 1, 2(2)d ij,m ± l ji,m = 0 m = 1, 2where the l ij,m are constants. This description of revolute joints has been given by Perram and Petersen (1988a).Something similar is done to obtain a prismatic joint. Now the five distances between the points must not be constant; we demand them to be equal. These four constraints leave one rotational and one translational degree of freedom. The rotation is prevented by demanding orthogonality of two vectors, one fixed in each link agent,as seen from Figure 3:Figure 2. The revolute joint agent J ij connects the link agents L i and L j by five distance constraints.Reactive Motion Planning41d ij,m ± d ij,0 = 0 m = 1, 2d ji,m ± d ij,0 = 0 m = 1, 2v ij v ji = 0Note that d ij,0 is not constant. The above constraints for prismatic joints were originally proposed by Overgaard (1991).Other types of joints are easily achieved. For example, Eq. (2) alone is a 3-DOF ball joint. On Figure 2 this corresponds to removing the two lines from p ji,1 and p ji,2to p ij,0.The link agents provide the joint agent with information about their positions,velocities, and intentions (forces). The joint agent then eventually adds an artificial joint limit repulsive force to the link agents’ forces. Furthermore, by communicating with other joint agents, the joint agents are able to find the proper forces due to the joint constraints. The constraint forces are also added to the link agents’ forces; they ensure that the joint constraints are exactly satisfied. Thus the flow of information communi-cated among the agents enables one agent to react on the intentions of any other agent in the multiagent system, e.g., repulsion on one link may cause all links to move.Summarizing, the role of a joint agent is to influence the intentions of its link agents in such a way that joint constraints and joint limits are not violated.Figure 3. A prismatic joint agent J ij connects the link agents L i and L j by five different geomet-ric constraints.42 L. Overgaard etal.Reactive Motion Planning 43End Effector AgentThe end effector is a link agent with additional aims. It has two different modes of behavior. During transfer movement between tasks, the end effector agent is aimed at reaching the starting point of the next task. To achieve this aim, the agent adds an attractive force directed toward the starting point.During task execution, the agent is a normal link agent without additional aims.A joint agent connects the end effector to a task agent, which guides the end effector along the task (see below).Task AgentThe task of welding, and most other tasks, requires high precision, more than can be achieved by using artificial forces. We obtain the required high-precision motion by introducing an imaginary task agent. The task agent is a moving frame of reference without any physical representation. The role of the task agent is to guide the end effector (which is passive during task execution) to execute the task.At the beginning of the task, a joint agent attaches the end effector to the task agent. W hen the task agent moves along the curve prescribed by the task specifica-tion, the joint agent forces the end effector to come along. The joint agent only imposes the constraints necessary for the specific task; the remaining freedom is left for the end effector agent.The process of welding norm ally requires control of only 5 out of the end effector’s 6 DOF; there is rotational symmetry about the torch. Therefore the end effector agent and the task agent are joined by a revolute joint about the torch.Details of the methods used to move the task agent can be found in the work by Larsen et al. (1994).EXPERIMENTAL RESULTSIn all applications, we place a level of centralized control on top of the described method. This level of control may be seen as a controller, giving input to the multiagent system. The controller assigns new tasks to end effector agents when they are ready for it. This approach has been shown to work well in several different cases.Welding ITo demonstrate the multiagent properties of our method as clearly as possible, we first show the very unusua l robot movem ent in the absence of the joint agents that norm ally connect the link agents. The link agents detect each other’s presence and react on it, but there is no other communication among the agents. Their actions44 L. Overgaard et al.are completely uncoordinated. The system’s behavior is governe d purely by the links’ selfish utility optimization.We consider a simple case, where a 5-DOF Hirobo WR-L80 robot, attached to a 3-DOF Cartesian gantry, welds two seams in a ship section. We include gravity in the artificial forces, so that all link agents tend to fall down toward the steel plates of the ship section. The link agents will try to avoid collisions with the steel plates. Furthermore, in this section’s experiment, the link agents avoid each other. Two weld tasks are assigned to the end effector.Figure 4 shows snapshots from the result of a num erical simulation of the multiagent system. The pictures show various stages of the scenery:1. The robot is in its initial configuration, and all velocities are zero.2. The robot disintegrates because of the link agents’ mutual repulsion. Because of gravity, the agents fall down toward the steel construction. The end effector falls faster than the other agents because of its additional attraction toward the corner.3. The end effector is close to the start of the first seam. At about the same time, the link agents stop falling because of their repulsion away from the steel plates.4. The end effector is attached to the task agent by a task joint agent, and the end effector starts to weld. The other agents float at safe distances from the steel plates and each other.5. The end effector has been released from the task agent and is now attracted toward another task starting in the opposite corner. During this process, a link agent is pushed away. In turn, this link agent pushes away other link agents.6. Finally, the end effector starts welding the second seam while being joined to the task agent. The link agents are repelled further away, to make more room for the end effector.This clearly demonstrates the agents’ autonomy.Welding IIIn this section, we do exactly the same as in the previous section with one extension only. We include the presence of the joint agents that connect neighboring links. The joint agents convey a flow of inform ation through the multiagent system that was absent in the previous experim ent.As before, the end effector must weld the two seams starting in the corners. The gantry has 3 DOF and the robot has 5, so the gantry/robot system is in fact an 8-DOF robot. The task agent controls 5 of the end effector’s DOF, so the system is kinematically redundant. Thus it has the freedom to avoid collisions, not only during the transfer movement between tasks but also when welding.The effect of the joint agents is clearly demonstrated by the snapshots from the simulation presented in Figure 5:Figure 4. Link agents move freely to perform their individual tasks. They all avoid collisions,and the end effector agent moves to two weld tasks. During welding, a task joint agent con-nects the end effector to the task agent.Reactive Motion Planning45Figure 5. Joint agents connect the link agents. The agents’ configuration space is now reduced to the one of the physical robot. The weld tasks are the same as in Figure 2.46 L. Overgaard etal.Reactive Motion Planning 471. The robot is in the same initial configuration as previously.2. The end effector is attracted toward the corner, and all the other agents have to follow it because of the constraint forces imposed by the joint agents.3. Now the end effector has pulled the robot close to the corner where the vertical seam begins. The comprom ise between attraction and repulsion yields a collision-free path.4. A snapshot of the welding process. The collision-free path to this configu-ration has never been planned but has simply emerged.5. After finishing the first task, the end effector is released and attracted toward the start of the next task in the other corner. The end effector and thus the other agents are repelled from the steel plates on the way to the other corner.6. The end effector has started to weld the horizontal seam. The task agent controls the process but not the end effector’s rotation about the torch.A comparison of Figure 4 and Figure 5 clearly shows the effect of the joint agents on the multiagent system.Snake SimulationWe have simulated a 25-DOF snakelike robot moving through a simple maze, using exactly the same method as in the two previous sections. The snake’s nose was the (active) end effector agent, while the other (passive) link agents were pulled through the maze without collisions. Figure 6 illustrates the result of the simulation. The time complexity of the simulation is given in the following section.A branched 25-DOF robot with two end effectors has also been simulated. Our method was immediately applicable. The only change was that the controller now had to coordinate the actions of the two end effector agents.Chirikjian and Burdick (1993) report on the actual construction of a 30-DOF planar snake robot and the method applied for obstacle avoidance. Computational C omplexityWe shall in this section find the complexities in the number of agents. Our approach has two main consum ers of computing time. One is the link agent’s computation for finding the closest obstacle. This cost is proportional to the num ber of agents in the robot. In situations where the link agents avoid each other, the cost of closest obstacle computation is quadratic in the number of agents.The other time consumer is the solution of linear equations for the constraint forces f c in Eq. (1). The matrix is irregular but sparse. Solving sets of m linear equations in general has the time complexity O(m3). The equation matrix for robots with a single chain of links is block tridiagonal, so the time complexity for a linear robot with n joints reduces from O(n3) to O(n). Figure 7 shows the total computational cost in ourFigure 6. A 25-DOF snakelike robot is maneuvering in a complex three-dimensional environment.48 L. Overgaard etal.simulations of snakes with different num bers of links. Notice that it would have been much more expensive to transform the snake’s environment (Figure 6) into C obstacles in the 25-dimensional C space and subsequently search for a feasible path.Thus the time complexity for robots with n joints varies between O (n ) for robotswith a serial chain of links to O (n 3) for very complex multitool robots, the worst casetopology.Emergent Intelligence?As stated by Brady (1985, p. 79), ªSince Robotics is the field concerned with the connection of perception to action, Artificial Intelligence must have a central role in Robotics if the connection is to be intelligent.º Although not quite up to date,Brady (1985) gives a very good review of AI and robotics.The apparently intelligent behavior of the robot in the presented experiments was achieved purely by a simple artificial force strategy. It looks as if the robot’s intelligence has appeared out of nothing. However, according to Brooks (1991,p. 585), ªIntelligence is in the eye of the observer.º The seat of intelligence is hard to identify, as the impression of intelligence is produce d by the interactions of all components, including the environment.In a recent paper, Werner (1994) presents a (rather inform al) theory called the ªcomplexity conservation principle.º Without actually defining the notion of com -plexity, Werner argues that a complex goal can only be achieved if either the agents or their environment has a complexity of matching stature.Thus, if our robot is intelligent, this intelligence is contained in the force strategy,in the structure of the robot and the environment, and in the inform ation defining the task.Figure 7. The time complexity is O (n ) for a serial robot with n joints.Reactive Motion Planning4950 L. Overgaard et al.CONC LUSIONThe multiagent model allows us to solve the motion planning problem as a distributed constraint satisfaction problem, where each agent is responsible for the satisfaction of its own partition of the constraints. It enables us to replace the traditional high-level planning approach by a low-level method with distributed local reactive control.Our approach is an artificial force method, where each agent expresses its intentions through repulsive, attractive, and constraint forces. Summarizing, the key characteristics of the presented method are as follows:Flexibility: Any robot can be controlled by this m ethod. There is no fundamental limit on the number of joints in the robot, in contrast to traditional meth-ods. The role of the individual agent is unchanged if, for example, the application is changed from one robot to another with a completely different topology.Efficiency: The obtained motion is not planned; instead it emerges because of the forces. No reasoning about future states is necessary, and thus immediate response is possible in nonstatic environments like sensor-based systems, systems with moving obstacles, and multirobot systems. The method is highly efficient for robots with many (n) joints, since the time complexity is as low as O(n) for the best case and O(n3) for the worst case. This is to be compared with the nonpolynom ial complexity of most traditional methods.Elegance: The distributed control gives rise to a very smooth and elegant movement of the articulated robot. All joints in the (possibly highly redundant) robot move simultaneously, and due to the dynamic model of the robot, there are no sudden accelerations or other discontinuities in the movement. This quality has an aesthetic value and also a practical one in the motion planning for high-speed robots.We have successfully applied the method to off-line robot progra mming at Odense Steel Shipyard Ltd. Since spring 1995, the system has been used to program 9-DOF welding robot installations in actual daily production. Later that year, a 11-DOF welding system was successfully tested. The backbone of the system is the motion planning technique described in this paper.The system is currently being extended to cover robot spray painting. The only changes are the description of the tasks and the behavior of the task agents. All other components of the system are general enough to be reused without changes. The next step is to investigate the effect of sensor feedback. Some very prom ising experiments in grasping moving objects have been perform ed. In such an on-line application, the fast response to changes provided by our reactived method is crucial.In future applications we will exploit the method’s obvious advantages in on-line control of multilink robots and multirobot systems.。

关于蛇形机器人结构运动及控制的研究蛇形机器人是一种模仿蛇形动态运动特性的机器人。

由于蛇形机器人的结构与运动方式与传统的机器人有所不同，因此对于蛇形机器人的结构、运动以及控制的研究具有重要意义。

首先，蛇形机器人的结构设计是研究的关键。

蛇形机器人通常由多个连续关节组成，每个关节都可以相对于前一个关节弯曲并展开。

通过控制关节的弯曲和展开，蛇形机器人可以模拟蛇身的曲线形状。

为了实现这种结构，研究人员通常采用柔性材料制作机器人的关节，以实现关节的变形。

此外，关节之间的连杆也需要适应关节变形的能力，这需要考虑到关节与连杆之间的连接方式及材料选择。

然后，蛇形机器人的运动特性也是研究的重点之一、蛇形机器人的运动是通过关节的协调运动实现的。

研究人员通过研究蛇类的运动方式，探索了不同的运动模式。

其中，波浪式运动是常见的一种模式，即蛇形机器人从头部到尾部依次弯曲并展开，形成像蛇一样的波浪形状。

此外，还有一些其他的运动模式，如直线运动、旋转运动等。

研究人员通过研究这些运动模式，探索了不同的运动方法和策略，以实现蛇形机器人的高效运动。

最后，蛇形机器人的控制方法也是蛇形机器人研究的重要内容。

蛇形机器人的控制需要实时控制各个关节的弯曲角度以及关节之间的协作运动。

常用的控制方法包括开环控制和闭环控制。

开环控制是在事先确定好运动序列的情况下，通过一定的控制输入来驱动机器人完成运动。

闭环控制则是在运动过程中通过传感器检测实际运动状态，并与目标运动状态进行比较，通过调整控制输入来实现机器人的运动控制。

研究人员通过模拟和实验，比较不同的控制方法的优缺点，并提出了一些新的控制策略，以提高蛇形机器人的运动性能和控制精度。

综上所述，关于蛇形机器人的结构、运动及控制的研究是一项具有重要意义的研究工作。

通过对蛇形机器人的结构、运动及控制的研究，可以为机器人的设计和应用提供一定的理论基础和实践经验，推动机器人技术的发展和应用。

同时，蛇形机器人的研究还可以为生物学、医学等领域提供一定的借鉴和启示，促进不同学科之间的跨界合作。

(19)中华人民共和国国家知识产权局(12)发明专利申请(10)申请公布号 (43)申请公布日 (21)申请号 201711212383.9(22)申请日 2017.11.28(71)申请人 常州大学地址 213164 江苏省常州市武进区滆湖路1号(72)发明人 班书昊　李晓艳　谭邹卿　(51)Int.Cl.B25J 9/06(2006.01)(54)发明名称一种基于柔性伸缩关节的蛇形仿生机器人(57)摘要本发明公开了一种基于柔性伸缩关节的蛇形仿生机器人，属于仿生机器人领域。

它包括机器蛇头、第一蛇身、第二蛇身和机器蛇尾；机器蛇头采用蛇头关节与第一蛇身相连，第一蛇身采用蛇身关节与第二蛇身相连，第二蛇身采用蛇尾关节与机器蛇尾相连；第一蛇身的前端装设有第一电机，第二蛇身的前端装设有第二电机；第一电机或第二电机可选为舵机或步进电机，第一电机和第二电机同步反向转动。

本发明的机器人关节柔性更好，运动姿态更接近于蛇的运动姿态，且耗能低。

权利要求书1页 说明书2页 附图1页CN 107813305 A 2018.03.20C N 107813305A1.一种基于柔性伸缩关节的蛇形仿生机器人，包括机器蛇头(1)、第一蛇身(2)、第二蛇身(3)和机器蛇尾(4)，所述机器蛇头(1)采用蛇头关节与所述第一蛇身(2)相连，所述第一蛇身(2)采用蛇身关节与所述第二蛇身(3)相连，所述第二蛇身(3)采用蛇尾关节与所述机器蛇尾(4)相连，其特征在于：所述蛇头关节包括蛇头关节弹簧A(51)和蛇头关节弹簧B(52)，所述蛇头关节弹簧A(51)和所述蛇头关节弹簧B(52)为两种类型不同的弹簧，一种为螺旋压缩弹簧，另一种为扭转弹簧；所述蛇身关节包括蛇身关节弹簧A(61)和蛇身关节弹簧B(62)，所述蛇身关节弹簧A(61)和所述蛇身关节弹簧B(62)为两种类型不同的弹簧，一种为螺旋压缩弹簧，另一种为扭转弹簧；所述蛇头关节包括蛇尾关节弹簧A(71)和蛇尾关节弹簧B(72)，所述蛇尾关节弹簧A(71)和所述蛇尾关节弹簧B(72)为两种类型不同的弹簧，一种为螺旋压缩弹簧，另一种为扭转弹簧；所述蛇头关节、蛇身关节、蛇尾关节中的所述螺旋压缩弹簧与所述扭转弹簧交叉布置；所述第一蛇身(2)的前端装设有第一电机(8)，所述第二蛇身(3)的前端装设有第二电机(9)；所述第一电机(8)或所述第二电机(9)可选为舵机或步进电机；所述第一电机(8)和所述第二电机(9)同步反向转动。

龙源期刊网
灵活的软体蛇形机器人
作者：本刊广州编辑部
来源：《孩子·小学版》2019年第11期
虽然没腿没脚，但是凭借其光滑的身体，蛇可以每小时滑行14英里，并且还能挤进各种狭小的空间，上树遁地都不在话下。

它是如何做到的呢？
蛇的运动主要有三种方式：一种是蜿蜒运动，所有的蛇都能以这种方式向前爬行。

爬行时，蛇体在地面上作水平波状弯曲，使弯曲处的后边施力于粗糙的地面上，由地面的反作用力推动蛇体前进，如果把蛇放在平滑的玻璃板上，那它就寸步难行了。

第二种是履带式运动，蛇没有胸骨，它的肋骨可以前后自由移动，这种运动方式产生的效果是使蛇身直线向前爬行，就像坦克那样。

第三種方式是伸缩运动，在地面爬行比较缓慢的蛇，如铅色水蛇等，在受到惊动时，蛇身会很快地连续伸缩，加快爬行的速度，给人以跳跃的感觉。

在今年，哈佛大学的科学家们已经研发出一种蛇形机器人，可以像蛇一样运动。

蛇形机器人的表面采用一种古老的日本纸制切割工艺。

该团队尝试了各种形状的切割，包括三角形，圆形和梯形。

他们发现梯形切口最接近蛇鳞片的形状，可以让机器人在运动时伸展得更长。

当机器人伸展时，看上去光滑的表面则会转换成3D纹理表面，像蛇皮一样抓住地面。

并且它还具有可编程的外壳，当遇到障碍时，能够及时地进行调整，这样爬行的速度和路线都会比较精准。

目前，这个蛇形机器人已经在整个哈佛大学遛了一圈，表现状况良好。

但是科学家们认为，只是会爬还不够，他们还在不断改进设计，希望有朝一日这些蛇形机器人可以穿越困难的环境进行探索、监控和搜救任务，或执行复杂的腹腔镜医疗程序。

（信息源自《每日科学》）。

蛇形使用说明书蛇形使用说明书一、产品概述蛇形是一款具有自主移动能力的，其设计灵感来源于蛇的爬行动作。

蛇形由多个相互连接的模块组成，能够模拟蛇的运动方式，在不同的地形和环境中自由移动。

二、产品特性1.灵活性：蛇形采用模块化设计，每个模块都可以自由弯曲和旋转，使得在复杂地形中的运动更加灵活。

2.自主导航：蛇形配备了先进的导航系统和传感器，能够自主寻找路径并避开障碍物。

3.多功能：蛇形支持多种操作模式，如巡航模式、搬运模式和搜索救援模式，适用于不同场景的需求。

4.轻便易携：蛇形采用轻量化材料制作，便于携带和部署。

三、组装与连接1.将各个模块按照顺序连接起来，确保连接牢固且不松动。

2.在连接处使用连接器固定模块，以确保连接的稳定性。

3.在连接模块时，请确认每个连接器的方向和位置正确，以免造成连接错误。

4.连接完成后，进行功能测试，确保能够正常工作。

四、使用方法1.开机和关机操作：按下背部的电源开关按钮，即可启动或关闭。

2.操作模式选择：支持多种模式选择，通过面板上的模式选择按钮进行切换。

3.移动控制：使用遥控器或者智能方式APP，控制的运动方向和速度。

4.使用注意事项：在使用蛇形的过程中，需要注意周围环境，避免碰撞或损坏。

五、常见问题解答Q: 无法启动怎么办？A: 请检查的电源是否连接正常，是否有足够的电量。

Q: 移动困难怎么办？A: 请检查的连接是否牢固，模块之间是否有松动。

Q: 无法避开障碍物怎么办？A: 请检查的传感器是否正常工作，是否有物体遮挡传感器。

六、维护与保养1.定期清洁的外表面和连接器，保持的正常运行。

2.在使用中发现故障或异常情况时，及时联系售后服务中心进行维修。

附件：本文档无附件。

法律名词及注释：1.模块化设计：将产品拆分成多个相互独立的模块，使每个模块具备独立的功能和特点。

蛇形机器人臂测控系统设计与试验研究摘要：蛇形机器人臂是一种模仿蛇类运动机理的机器人，具有灵活性强、适应性广等优点。

本文针对蛇形机器人臂的测控系统进行了设计与试验研究。

首先，介绍了蛇形机器人臂的结构和工作原理。

然后，设计了蛇形机器人臂的测控系统，包括传感器、控制器和执行器等组成部分。

在系统设计过程中，充分考虑了系统的稳定性、精度和可靠性。

最后，通过实验验证了设计的测控系统的性能和效果。

实验结果表明，蛇形机器人臂的测控系统能够实现对机器人臂的精确控制和位置测量，具有较好的稳定性和可靠性。

关键词：蛇形机器人臂；测控系统；传感器；控制器；执行器1. 引言蛇形机器人臂是一种新型的机器人臂，模仿了蛇类的运动机理。

蛇形机器人臂具有较强的灵活性和适应性，可以在复杂环境中完成一系列任务，如搜救、探测等。

为了实现对蛇形机器人臂的精确控制和位置测量，需要设计和研究相应的测控系统。

2. 蛇形机器人臂的结构和工作原理蛇形机器人臂由多节可弯曲的节段组成，每个节段之间通过关节连接。

机器人臂通过控制各个关节的运动来实现整体的运动。

蛇形机器人臂的工作原理是通过控制各个关节的角度，使得机器人臂呈现出蛇类的运动方式。

3. 测控系统的设计测控系统是蛇形机器人臂的重要组成部分，用于实现对机器人臂的精确控制和位置测量。

测控系统由传感器、控制器和执行器等组成。

传感器用于测量机器人臂的位置和姿态，控制器根据传感器的反馈信息对机器人臂进行控制，执行器用于实现机器人臂的运动。

4. 测控系统的试验研究为了验证设计的测控系统的性能和效果，进行了一系列实验。

实验中使用了精确的位置测量设备对机器人臂的位置进行测量，并与测控系统的测量结果进行对比。

实验结果表明，设计的测控系统能够实现对机器人臂的精确控制和位置测量，具有较好的稳定性和可靠性。

5. 结论本文设计了一种蛇形机器人臂的测控系统，并进行了试验研究。

实验结果表明，设计的测控系统能够实现对机器人臂的精确控制和位置测量，具有较好的稳定性和可靠性。

行业动态News9Robot Technique and Application20182行业动态(新技术)近日，哈佛大学的研究人员以日本古老的剪纸艺术kirigami 为设计灵感，利用蛇鳞结构的“各向异性摩擦特性”研发出一种充气式柔性机器人。

该机器人能够像蛇一样，通过充气与放气的循环动作来实现爬行，可用于探索危险地形，进行勘探或执行搜索救援等任务。

这款机器人实现运动的关键在于“皮肤”。

为了制造与蛇鳞类似的鳞片皮肤，研究人员制作了各种可伸缩的塑料片，并尝试了多种不同的切口形状，且每一片鳞片都通过激光蚀刻技术刻上独特的图案，然后再将该片材缠绕在可膨胀与放气的硅胶管周围。

这种结构使得机器人在躯体膨胀并拉伸鳞片材料时，原本平均的鳞片会变形并从机器人体内弹出进而抓住地面，并将躯体的反复膨胀转化为向前运动。

为找到最佳的鳞片切割模式，研究人员通过比较线条、梯形、三角形以及圆形等不同的比例模型，发现梯据悉，韩国首尔大学研发出能够依靠吸收周围环境中水分而前行的微型机器人。

该机器人可以爬行、蠕动，并像蛇一样弯曲。

这种微型机器人的设计灵感来自于植物，其可以通过吸收地面或空气中的水分来改变自身形状和大小。

研究人员通过模仿非洲的灌木植物“枯野葵种子的鬃毛”，利用纳米纤维制作成该微型机器人，并将其分成两层：一层用来吸收水分，另一层则不吸收水分。

当机器人置于潮湿的表面上时，吸湿层膨胀，从而使机器人弓起；机器人一旦2月7日，广州云从信息科技有限公司（以下简称云从科技）正式宣布推出3D 结构光人脸识别技术。

据悉，这是中国企业首次将结构光技术应用在人脸识别系统上，标志着中国突破了结构光人脸识别技术的壁垒。

3D 结构光人脸识别系统基于“飞龙II”深度学习结构光算法与3D 结构光深度摄像头，不仅能够利用结构光设备同时获取场景中的彩色、红外、深度图片，而且还能对场景中的人脸进行检测分析，形成3D 人脸图像。

该人脸识别技术具有3大优势：用户只需在摄像头前被捕捉到面部画面，不需要进行其他动作配合，即可完成生物活性验证，有效应对纸张、面具、手机屏幕等各类道形鳞片最适合这款机器人，因为梯形能够让鳞片充分伸展，从而帮助机器人在膨胀自身躯体时得以产生更长的“步幅”。

专利名称：多自由度柔性关节的蛇形机器人专利类型：实用新型专利
发明人：章军,须文波,吕兵
申请号：CN200520068382.8
申请日：20050117
公开号：CN2774717Y
公开日：
20060426
专利内容由知识产权出版社提供
摘要：本实用新型涉及多自由度柔性关节的蛇形机器人，这种蛇形机器人具有稳定性好、横截面小、柔性等特点，能在各种粗糙、陡峭、崎岖的复杂地形上行走，并可攀爬障碍物，因此，在救援、探查、星球探测等方面具有广阔的应用前景，并为研究蛇类运动的运动机理、动力学、运动控制方法等提供实验本体，属于机器人应用技术领域。

蛇的基本单元——柔性关节模块，该关节模块有多自由度气动式柔性关节、微型步进电机、微型步进电机驱动器、旋转配气阀组成；以此关节模块为蛇的主干，连接成如蛇的脊椎骨式的结构，并同蛇头的接收与控制装置和蛇尾的微型气泵连接一体，构成蛇形机器人；以主控计算机发出的遥控信号，控制蛇形机器人的运动。

申请人：江南大学
地址：214036 江苏省无锡市惠河路170号
国籍：CN
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