多移动机器人系统运动控制研究
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摘要
机器人技术的发展使机器人的能力不断提高,机器人应用的领域和范围不断扩大。深海作业、核工业故障处理、太空中操作等都迫切需要机器人进入角色。一方面,机器人作业任务的复杂性,迫切需要多机器人的协调与合作来完成。另一方面,通过多机器人间的协调与合作,可以提高机器人系统的工作效率,并使系统具有更强的适应能力和容错能力。在多机器人系统的研究中,多机器人系统运动的协调控制是一个热点研究主题,是该领域中的一个基础性研究方向。本文对多移动机器人系统的体系结构、路径规划、典型协作任务(编队控制与追捕)等议题开展研究,论文主要内容和取得研究结果如下:
论文在综述多机器人系统国内外研究现状的基础上,讨论了多机器人系统体系结构问题,定义了整个系统内的多机器人的相互关系和功能分配,确定了系统和各机器人之间的信息流通关系及其逻辑上的拓扑结构,给出了控制多机器人并使其可以协调合作的机制和计算结构。根据Pioneer 2-DXe型移动智能机器人的特点,本文提出一种基于开放式多智能体结构(open agent architecture,OAA)的多移动机器人协调控制系统,并且依照这一架构,采用多台Pioneer 2DX型机器人和OAA2.3.1软件平台,搭建了一个多机器人实物实验系统。该控制系统具有开放性、即插即用、分布式计算、多模式输入等优良特性,能满足多机器人系统适应未知复杂环境的需要。
本文提出一种基于粒子群优化算法(particle swarm optimization, PSO)的移动机器人全局路径规划方法:首先使用自由空间法构建机器人工作空间自由运动链接图,用图论方法获得链接图网络最短路径;后用PSO优化算法对已得路径进行二次寻优。针对PSO的局部极小问题,提出一种带变异算子的PSO算法,以提高算法搜索成功率。
多机器人路径规划不但要解决避障问题,还要解决避碰问题。本文根据多智能体系统理论,提出一种基于门限偶极子模型(gated dipole model)的分布式多机器人路径规划算法:每个机器人作为一个智能体,独立使用门限偶极子模型进行路径规划。机器人通过传感器以及与其他智能体的通信来获取环境的信息,采用设置动态优先级的方法实现各机器人之间的避碰。
编队控制是一个典型的多机器人协作问题。本文提出一种基于行为的多机器人任意队形控制算法。针对基于行为法只存在局部队形反馈,不能保证队形稳定性的缺陷,结合OAA结构特点,提出带整体队形反馈的编队控制策略,并用李雅普诺夫稳定性定理证明该控制策略是渐近稳定的。
强化学习是一种新的机器学习方法,在机器人领域中有广泛的应用。本文提出一种基于多智能体独立强化学习算法,实现多机器人围捕多入侵者的协作任务。建立了一个能包含大量机器人的多机器人系统仿真实验平台,分析了各种实验条件对机器人群体行为性能的影响;采用PSO算法寻找不同优化指标下的最佳实验配置。
With the development of robotics, the capabilities of robot have been improved ceaselessly and its application areas have been extended greatly. Robots are being expected to do more complicated tasks, such as exploration under deep ocean and even the treatment of nuclear industry fault, operation in outer space etc. On the one hand, when the tasks are too complex to be set up with a single robot, they can be accomplished through the coordination and collaboration of multiple robots. On the other hand, the coordination and collaboration among multiple robots can improve the efficiency of robots system and make the system be more adaptive and fault tolerant. The coordinate control of multiple mobile robots is always a hot and essential topic in multi-robot system domain. In this dissertation, the architecture, path planning and some typical coordinate tasks of multi-robot system, such as formation control and pursuit-evasion game, have been discussed thoroughly. The main contents and contributions of this dissertation are as the followings:
Based on the survey of the research status at home and abroad, one contribution is to establish the architecture for multi-robot system, which defines the relationship and function allocation among multiple robots in the system, specifies the information flow and the logical topological structure between the system and the robots, and presents the mechanism and algorithm structure which can control multiple robots and make them act cooperatively. According to the characteristics of Pioneer 2-Dxe intelligent mobile robot, a multi-robot coordination control system based on the open agent architecture (OAA) is presented, and a physical multi-robot experiment platform is constructed, using several Pioneer 2-Dxe mobile robots as hardware platform and OAA2.3.1 as software platform. This system has some advantages, such as openness, plug and play, distributed computation and multimodal, which can satisfy the requirement for multi-robot system working in unknown complex environment.
A novel global path planning method for mobile robot based on particle swarm optimization is proposed in the dissertation. First the free motion link graph is built for the working space of the mobile robot by using free space method, and the shortest path from the start point to the goal point in the graph is obtained by Dijkstra algorithm. Then PSO is adapted to optimal the path that already got. Aiming at the shortcoming of the PSO, which is, easily plunging into the local minimum, the dissertation puts forward an advanced PSO algorithm with the mutation operator.
Multi-robot path planning has to deal with not only the obstacle avoidance problem, but also the collision avoidance problem. According to multi-agent system theroy, a distributed multi-robot path planning algorithm based on gated dipole model is presented. Each robot as an agent uses the modal to plan its optimal path independently and obtain the environment information by sensor and the communication between other robots. The robots use dynamic priority method to avoid the collision with each other.
Formation control is a typical multi-robot coordinational problem. A behavior based arbitrary formation control algorithm is discussed. The behavior based method exist only local formation feekback, and cannot guarantee the stabilization of the formation. Considering the OAA character, the dissertation proposed a formation control strategy with global formation feedback and it is proved that this control strategy is asymptotical stable by Liapunov stability theory.
Reinforcement learning is a novel mechine learning method and has been widely used in robot domain. To carry out a coordination task that multiple robots surround multiple invaders, a multi-agent individual reinforcement learning algorithm is used. An experiment simulation platform consisting of many robots and targets is built. Several experimental conditions are designed and their influences on the robots’group behavior performance are discussed. PSO algorithm is used to find out the optimal experiment configuration under different optimal criterions.
论文在综述多机器人系统国内外研究现状的基础上,讨论了多机器人系统体系结构问题,定义了整个系统内的多机器人的相互关系和功能分配,确定了系统和各机器人之间的信息流通关系及其逻辑上的拓扑结构,给出了控制多机器人并使其可以协调合作的机制和计算结构。根据Pioneer 2-DXe型移动智能机器人的特点,本文提出一种基于开放式多智能体结构(open agent architecture,OAA)的多移动机器人协调控制系统,并且依照这一架构,采用多台Pioneer 2DX型机器人和OAA2.3.1软件平台,搭建了一个多机器人实物实验系统。该控制系统具有开放性、即插即用、分布式计算、多模式输入等优良特性,能满足多机器人系统适应未知复杂环境的需要。
本文提出一种基于粒子群优化算法(particle swarm optimization, PSO)的移动机器人全局路径规划方法:首先使用自由空间法构建机器人工作空间自由运动链接图,用图论方法获得链接图网络最短路径;后用PSO优化算法对已得路径进行二次寻优。针对PSO的局部极小问题,提出一种带变异算子的PSO算法,以提高算法搜索成功率。
多机器人路径规划不但要解决避障问题,还要解决避碰问题。本文根据多智能体系统理论,提出一种基于门限偶极子模型(gated dipole model)的分布式多机器人路径规划算法:每个机器人作为一个智能体,独立使用门限偶极子模型进行路径规划。机器人通过传感器以及与其他智能体的通信来获取环境的信息,采用设置动态优先级的方法实现各机器人之间的避碰。
编队控制是一个典型的多机器人协作问题。本文提出一种基于行为的多机器人任意队形控制算法。针对基于行为法只存在局部队形反馈,不能保证队形稳定性的缺陷,结合OAA结构特点,提出带整体队形反馈的编队控制策略,并用李雅普诺夫稳定性定理证明该控制策略是渐近稳定的。
强化学习是一种新的机器学习方法,在机器人领域中有广泛的应用。本文提出一种基于多智能体独立强化学习算法,实现多机器人围捕多入侵者的协作任务。建立了一个能包含大量机器人的多机器人系统仿真实验平台,分析了各种实验条件对机器人群体行为性能的影响;采用PSO算法寻找不同优化指标下的最佳实验配置。
With the development of robotics, the capabilities of robot have been improved ceaselessly and its application areas have been extended greatly. Robots are being expected to do more complicated tasks, such as exploration under deep ocean and even the treatment of nuclear industry fault, operation in outer space etc. On the one hand, when the tasks are too complex to be set up with a single robot, they can be accomplished through the coordination and collaboration of multiple robots. On the other hand, the coordination and collaboration among multiple robots can improve the efficiency of robots system and make the system be more adaptive and fault tolerant. The coordinate control of multiple mobile robots is always a hot and essential topic in multi-robot system domain. In this dissertation, the architecture, path planning and some typical coordinate tasks of multi-robot system, such as formation control and pursuit-evasion game, have been discussed thoroughly. The main contents and contributions of this dissertation are as the followings:
Based on the survey of the research status at home and abroad, one contribution is to establish the architecture for multi-robot system, which defines the relationship and function allocation among multiple robots in the system, specifies the information flow and the logical topological structure between the system and the robots, and presents the mechanism and algorithm structure which can control multiple robots and make them act cooperatively. According to the characteristics of Pioneer 2-Dxe intelligent mobile robot, a multi-robot coordination control system based on the open agent architecture (OAA) is presented, and a physical multi-robot experiment platform is constructed, using several Pioneer 2-Dxe mobile robots as hardware platform and OAA2.3.1 as software platform. This system has some advantages, such as openness, plug and play, distributed computation and multimodal, which can satisfy the requirement for multi-robot system working in unknown complex environment.
A novel global path planning method for mobile robot based on particle swarm optimization is proposed in the dissertation. First the free motion link graph is built for the working space of the mobile robot by using free space method, and the shortest path from the start point to the goal point in the graph is obtained by Dijkstra algorithm. Then PSO is adapted to optimal the path that already got. Aiming at the shortcoming of the PSO, which is, easily plunging into the local minimum, the dissertation puts forward an advanced PSO algorithm with the mutation operator.
Multi-robot path planning has to deal with not only the obstacle avoidance problem, but also the collision avoidance problem. According to multi-agent system theroy, a distributed multi-robot path planning algorithm based on gated dipole model is presented. Each robot as an agent uses the modal to plan its optimal path independently and obtain the environment information by sensor and the communication between other robots. The robots use dynamic priority method to avoid the collision with each other.
Formation control is a typical multi-robot coordinational problem. A behavior based arbitrary formation control algorithm is discussed. The behavior based method exist only local formation feekback, and cannot guarantee the stabilization of the formation. Considering the OAA character, the dissertation proposed a formation control strategy with global formation feedback and it is proved that this control strategy is asymptotical stable by Liapunov stability theory.
Reinforcement learning is a novel mechine learning method and has been widely used in robot domain. To carry out a coordination task that multiple robots surround multiple invaders, a multi-agent individual reinforcement learning algorithm is used. An experiment simulation platform consisting of many robots and targets is built. Several experimental conditions are designed and their influences on the robots’group behavior performance are discussed. PSO algorithm is used to find out the optimal experiment configuration under different optimal criterions.
引文
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