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Mobile Robotics

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Mobile Robotics

Mobile robotics is an exciting field that has been gaining increasing

attention in recent years. It involves the use of robots that can move

autonomously in various environments, such as factories, warehouses, hospitals,

and even homes. These robots are equipped with sensors, cameras, and other

technologies that enable them to perceive their surroundings and make decisions

based on that information. In this response, I will discuss the requirements,

challenges, and opportunities associated with mobile robotics, as well as the

impact that this technology can have on society.

One of the main requirements for mobile robotics is the ability to navigate in

complex environments. This involves not only the ability to move around obstacles

but also the ability to plan paths that optimize various criteria, such as safety,

efficiency, and energy consumption. To achieve this, mobile robots need to be

equipped with advanced sensors and algorithms that enable them to perceive their

surroundings and make decisions in real-time. This is a challenging task that

requires a deep understanding of computer vision, machine learning, and robotics.

Another requirement for mobile robotics is the ability to interact with humans

in a safe and efficient manner. This is particularly important in applications

such as healthcare, where robots need to work alongside human caregivers to

provide assistance to patients. In these scenarios, robots need to be able to

understand natural language, recognize gestures, and respond appropriately to

human requests. This requires the development of advanced natural language

processing and human-robot interaction technologies.

One of the main challenges associated with mobile robotics is the need for

robustness and reliability. Mobile robots are often deployed in harsh and

unpredictable environments, where they may encounter unexpected obstacles,

lighting conditions, or other sources of noise. To ensure that these robots can

operate safely and effectively, they need to be designed with redundancy and

fault-tolerance in mind. This requires the development of robust control

algorithms, as well as the use of redundant sensors and actuators.

Another challenge associated with mobile robotics is the need for energy

efficiency. Mobile robots often operate on battery power, which limits their endurance and range. To overcome this limitation, researchers are exploring

various strategies for reducing the energy consumption of mobile robots, such as

optimizing their motion planning algorithms, using energy-efficient sensors and

actuators, and employing advanced power management techniques.

Despite these challenges, mobile robotics offers many exciting opportunities

for innovation and impact. One of the most promising applications of mobile

robotics is in healthcare, where robots can be used to provide assistance to

patients, such as helping them with mobility, monitoring their vital signs, and

even providing companionship. Another promising application is in logistics, where

robots can be used to automate tasks such as picking and packing in warehouses,

reducing the need for human labor and improving efficiency.

Mobile robotics also has the potential to transform the way we interact with

our environment. For example, robots can be used to monitor and maintain critical

infrastructure, such as bridges and pipelines, improving safety and reducing

maintenance costs. They can also be used to explore hazardous environments, such

as disaster zones or deep-sea environments, where human access is limited or

impossible.

In conclusion, mobile robotics is a rapidly evolving field that offers many

exciting opportunities for innovation and impact. While there are many challenges

associated with this technology, such as the need for robustness, reliability, and

energy efficiency, researchers are making significant progress in addressing these

challenges. As mobile robots become more capable and more widely deployed, they

have the potential to transform many aspects of our lives, from healthcare to

logistics to environmental monitoring.

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移动机器人利用元胞自动机模型避免瞬间障碍(IJMECS-V5-N8-8)

移动机器人利用元胞自动机模型避免瞬间障碍(IJMECS-V5-N8-8)

I.J.Modern Education and Computer Science, 2013, 8, 73-80 Published Online October 2013 in MECS (/) DOI: 10.5815/ijmecs.2013.08.08

Copyright © 2013 MECS I.J. Modern Education and Computer Science, 2013, 8, 73-80 Mobile Robot Motion Using Cellular Automaton Model to Avoid Transient Obstacles Kohei Arai Faculty of Science and Engineering, SAGA University, Saga, 840-8502 Japan E-mail: arai@is.saga-u.ac.jp Steven Ray Sentinuwo Faculty of Engineering, Sam Ratulangi University, Manado, 95115 Indonesia E-mail: raysteven@ Abstract — The obstacle avoidance is currently treated by methods that fall into two broad categories: global and local approach. This paper considers the obstacles whose velocity and direction cannot be easily predicted. Such obstacle is called transient obstacle. To avoid such kind of obstacle, we introduce a local path planning method for a robotics by using cellular automaton approach. The cellular automaton was combined with Dijkstra shortest path algorithm as global path planning to obtain a path for mobile robot to be able to avoid transient obstacle along the path. Using the proposed method, a scene in a typical corridor has been created. Moreover, this paper also evaluated two kinds of obstacle avoidance motion. First, the robot uses the “stop and go” method, which is the robot decreases its speed while encounter a transient obstacle. The second one is detour method, in which the robot makes a detour motion to avoid a transient obstacle. To coupe the drawbacks of local path planning, this paper also propose the enhancement of detour method. The simulation results show that in dynamic environment with transient obstacles, the “stop and go” method produces minimal collision with shortest-distance path. While, using the detour method generates minimal collision with time-minimal navigation path. Index Terms— Mobile Robot Motion, Cellular Automaton Model, Transient Obstacle Avoidance I. Introduction Path planning plays an important role in various fields of application and research. Many algorithms developed to assist the navigation of mobile agent, such as, people, robot, and other motion vehicle, in choosing the best path to their destination. A major goal in current mobile robotics research is to bring robots into natural human environments and also can safely carry out missions in populated environment. Though, in the worst case, the high-density environment may leave no free space for the robot to move. This paper introduces path planning and obstacle avoidance motion for autonomously mobile robot. This simulation uses a typical corridor layout as a simulation environment and the pedestrian along the corridor was considered as the obstacles. Most of the current local path planning considers the obstacle in dynamic environment as moving obstacle. Moving obstacle means the velocity and direction of obstacle relatively constant and predictable [1] [2] [3]. However, this paper using different approach for the obstacle behavior. The pedestrian along the corridor was not threat as moving obstacle but as transient obstacle. Transient obstacle is obstacle with randomize velocity and arbitrary motion, hence, it can appears and disappears in the environment [4]. In the other word, the transient obstacle is time-independent obstacle. Therefore, it is difficult to predict the further location of the robot based on its velocity and direction. This paper presents the research and simulation result of local path planning using Cellular Automata model to avoid transient obstacle in dynamic environment. This paper also suggests the enhancement of local path planning with global path planning to eliminate the drawbacks of local path planning and to optimize the length of the path. This paper is organized as follows. Some studies and theories relating with robot path planning issues and obstacles avoidance are quick reviewed in Section II and III. Cellular Automata model in robot path planning is presented in the Section IV. In the Section V, the proposed path planning model is described. Section VI contains the simulation process and results. Finally, in Section VII, we present the conclusion and the future works of this research. II. Related Works Research about robotic path planning covers a wide area because it enhances robotic navigation systems in both static and dynamic environment. There are exists a large number of methods for solving the basic path planning issues. Several traditional control methods 74 Mobile Robot Motion Using Cellular Automaton Model to Avoid Transient Obstacles

机器人毕业设计参考文献

机器人毕业设计参考文献

机器人毕业设计参考文献

以下是一些关于机器人毕业设计的参考文献：

1. "Robot Operating System for Mobile Robotics Applications" by

Anis Koubaa

2. "Robotics: Modelling, Planning and Control" by Bruno Siciliano,

Lorenzo Sciavicco, Luigi Villani, Giuseppe Oriolo

3. "Robotics: State of the Art and Future Challenges" edited by

Jadran Lenarčič, Baochuan Li

4. "Introduction to Autonomous Robots: Kinematics, Perception,

Localization and Planning" by Nikolaus Correll

5. "Introduction to Robotics: Analysis, Systems, Applications" by

Saeed B. Niku

6. "Robotics, Vision and Control: Fundamental Algorithms in

MATLAB" by Peter Corke

7. "Principles of Robot Motion: Theory, Algorithms, and

Implementations" by Howie Choset, Kevin M. Lynch, Seth

Hutchinson, George Kantor, Wolfram Burgard, Lydia E. Kavraki,

Sebastian Thrun

8. "Robotics Automation and Control" edited by Abul Hasan

基于模糊人工势场法的移动机器人路径规划

基于模糊人工势场法的移动机器人路径规划

基于模糊人工势场法的移动机器人路径规划

潘洲;万衡;李嘉琦

【摘 要】针对复杂环境下移动机器人的路径规划问题，提出模糊逻辑和人工势场法相融合的路径规划方法。对传统人工势场法进行了改进，针对人工势场法中的局部最小问题，提出了设置虚拟障碍点的方法，仿真验证效果可行；把通过人工势场法得到的相关数据作为模糊控制器的输入，融合了二者的优点，该算法计算量小且实时性高，设计的模糊控制器负责对机器人的航向角和速度进行实时的调整修正，弥补了人工势场法忽略机器人自身几何信息缺陷的同时，保障了路径规划的可靠性和平滑性，提高了系统的稳定性和鲁棒性。MATlAb仿真结果表明，该算法具有可行性和高效性。

【期刊名称】《制造业自动化》

【年(卷),期】2015(000)013

【总页数】5页(P4-7,11)

【关键词】移动机器人;人工势场法;模糊逻辑;路径规划

【作 者】潘洲;万衡;李嘉琦

【作者单位】上海应用技术学院电气与电子工程学院，上海 201418;上海应用技术学院电气与电子工程学院，上海 201418;上海应用技术学院电气与电子工程学院，上海 201418

【正文语种】中 文

【中图分类】TP242

0 引言

路径规划是移动机器人领域的一个重要研究课题[1]。路径规划的方法很多，主要有可视图法、蚁群算法、粒子群算法，遗传算法等。可视图法没有考虑机器人本身的形状大小，容易造成与障碍物的碰撞，同时随着障碍物的增多，计算复杂性增加，搜索时间长。蚁群算法、粒子群算法、遗传算法等算法普遍存在算法耗时多、计算量大、存储空间占用多的缺点。随着人们对实时性要求的提高，同时复杂的环境也很难用精确的数学模型来描述，所以越来越多的人转向了结构简单的人工势场法和模糊逻辑算法。人工势场法规划出的可行路径一般比较平滑且安全，在实时避障和平滑轨迹控制等方面得到了广泛应用[2]。机器人运动环境复杂，障碍物位置信息很难用精确的数学模型来描述，因此采用模糊逻辑算法非常适合移动机器人的避障问题，模糊逻辑算法避开了传统算法对环境模型依赖性强等缺点[3]，对于复杂环境下路径规划，有较强的优越性和实时性。

Mobile Robotics

Mobile Robotics

Mobile Robotics

Mobile robotics has become an increasingly important field in today's

technological landscape. From autonomous vehicles to robotic surgery, the

applications of mobile robotics are vast and diverse. However, with the rapid

advancements in technology, there are also a number of challenges and concerns

that need to be addressed. One of the primary concerns with mobile robotics is

safety. As these robots become more autonomous and interact with humans in various

environments, ensuring their safety and the safety of those around them is crucial.

This includes developing robust collision avoidance systems, as well as ensuring

that the robots are able to make ethical decisions in complex situations.

Additionally, there is a need to establish clear regulations and standards for the

deployment of mobile robots in public spaces to minimize potential risks. Another