融合沿墙行为的多移动机器人有序化多模态群集运动控制

一类基于势场原理的群集控制理论正逐步应用于多agent(智能体)/机器人稳定协同运动中.针对群集运动系统在非规则障碍物环境中运行时易出现的局部极小问题,引入基于行为的机器人学理念,构成多移动机器人多模态群集控制系统.在此框架内,仿生的动物沿端行为与有序化群集运动控制策略相融合,实现了多移动机器人系统快速聚合行为与高效避障行为的统一.移动机器人仿真实验验证了该方法的有效性.     

融合路径跟踪模式的多移动机器人有序化群集运动控制

针对多移动机器人群集运动的实现及其避障问题，提出了一类融合路径跟踪模式的有序化群集运动控制算法．该算法将有leader的群集运动模式与队列运动模式相结合，实现了多机器人系统快速聚合行为与有效避障行为的统一．移动机器人群仿真实验验证了该方法的有效性．     

考虑运动受限的履带式移动机器人轨迹跟踪控制

针对履带式移动机器人的轨迹跟踪控制问题进行研究,首先,建立了履带式移动机器人的运动学模型和跟踪误差模型;其次,设计了转速有限时间控制和线速度滑模控制的轨迹跟踪控制律,并给出了考虑运动受限作用下的控制律修正表达式;最后,基于MATLAB对所提控制律进行仿真,对比分析了不考虑运动受限情况下跟踪控制效果;结果表明,设计的跟踪控制律能够实现履带式移动机器人对圆轨迹的有效跟踪,且考虑运动受限作用的控制律更加符合实际;文章研究分析了运动受限作用对于移动机器人轨迹跟踪控制的影响,分析结果对其他移动机器人的运动控制研究具有参考价值。     

基于SBC智能移动机器人控制系统与路径规划的研究

本文论述了以SBC为核心的智能移动机器人控制系统以及基于圆弧路径的策略研究.移动机器人的系统组成结构提高了机器人的开放性,基于"NC"嵌入"PC"的运动控制方式,提高了机器人的控制效率,路径控制策略的圆弧算法有利于提高移动机器人运动的运动性能.     

多机器人系统中的机器人运动控制

针对多机器人系统中机器人运动控制的要求和特点,提出了基于Server+IPC+PLC架构的移动机器人运动控制系统方案,解决了系统互连中存在的一些问题。建立移动机器人的运动学模型,设计基于分解运动速度控制的机器人运动轨迹跟踪算法,并通过仿真研究验证算法的有效性。     

基于多Agent的移动机器人导航进化控制的体系结构

将进化控制与多Agent技术相结合,兼顾几种经典体系结构的优点,提出了一种基于多Agent的移动机器人导航进化控制的体系结构.该体系结构特别适合于网络环境下的机器人系统,能充分发挥进化控制隐含并行性的优点.最后,结合其所在研究所实验室的条件,设计出以该体系结构为基础机器人系统实现方案.     

一种基于动力学模型的高速轮式移动机器人漂移运动控制方法

针对高速轮式移动机器人,提出了一种漂移运动控制方法.首先以统一轮胎模型为基础建立了移动机器人在复杂工况下的动力学模型;针对系统模型非线性、强耦合等特点,利用反馈线性化的方法设计了一种基于动力学模型的漂移运动控制器,该控制器实现了对目标姿态角及角速度的跟踪控制;针对现有视觉、GPS等测量手段实时性差、不能满足漂移运动任务的缺点,设计实现了位置速度测量单元.仿真及实验验证了该控制器的有效性.     

移动机器人Java Agent控制系统设计

针对移动机器人的任务和硬件组成,提出了基于Java 开发平台的Agent控制系统设计方法。以目前应用较广泛的JADE作为Agent开发平台,采用JNI方法实现了Agent与硬件系统的交互。在运动控制卡上设计了有实时性要求的轨迹生成、运动控制、位姿估计和安全控制等4个行为任务,将数据库和路径规划等管理性行为设计在车载PC104工业控制计算机上。人机交互界面可作为独立的Agent驻留在上位监控计算机上。这种方法结合了Java Agent开发平台的普遍性和工业控制的实时性,实验证明了该方法的可行性。     

多智能体系统及其协同控制研究进展

对多智能体系统及其协同控制理论研究和应用方面的发展现状进行了简要概述.首先给出 Agent 及多 Agent 系统的概念和特性等,介绍了研究多Agent系统协同控制时通常用到的代数图论;然后综述了近年来多Agent系统群集运动和协同控制一致性方面的研究状况,并讨论了其在军事、交通运输、智能机器人等方面的成功应用;最后,对多Agent系统未来的发展方向进行了探讨和分析,提出几个具有理论和实践意义的研究方向,以促使多Agent系统及其协同控制理论和应用的深入研究.     

智能移动机器人运动控制系统及算法的设计

本文应用系统工程的方法对户外智能移动机器人的运动控制系统进行了研究，从实用的角度提出了运动控制系统的实现方法。根据移动机器人户外工作的特点和要求，设计了简单实用的伺服运动控制器，并基于其动力学模型设计了稳定的控制算法。实验结果表明该户外移动机器人运动控制系统的结构设计和功能设计符合实用要求，具有一定的应用价值。     

连通性保持下的多机器人系统分布式群集控制

多机器人系统群集控制中的连通性保持对于系统的稳定性和状态收敛的快速性具有重要影响.本文在初始通信网络拓扑为强连通非平衡图的条件下,研究具有非完整约束运动学模型的多轮式移动机器人系统群集运动中的连通性保持控制问题.首先,构造了一类新颖的光滑有界的人工势场函数,该类函数可以同时满足连通性保持、碰撞规避和相对距离镇定等任务需求.进一步,将基于势函数梯度的控制策略与一致性控制机制有机结合,在系统中存在和不存在领航者的条件下,分别设计出一类具有连通性保持功能的光滑有界的分布式群集控制协议,不仅可有效避免不连续/非光滑控制器所固有的抖振现象以及执行器饱和问题,而且实现了将传统群集控制中的连通性保持算法从个体运动模型和系统通信拓扑类型两个方面同时加以拓展.最后,仿真结果和实验结果验证了本文所提出的光滑有界群集控制算法的有效性.     

Distributed leader‐follower flocking control

This paper presents a fuzzy based leader‐follower flocking system. To maintain the distance between robots, we use a fuzzy logic controller to design a “force function” which is related to the relative distance between neighbours. The “force function” is used to control velocity of robots. To prove stability of the flocking system, we build a Hamilton function which is kinetic energy of the flocking system. Utilizing the LaSalle's invariance principle, we prove that the system is stable. Specially, we develop a flocking controller in local form. By using the local controller, the robots in the flocking system only need to know local information (relative distances and relative angles between neighbours). To evaluate performance of the flocking system, we simulate the flocking system tracking trajectories with different shapes. The local flocking algorithm is tested with three Pioneer robots. We use the SICK laser scanner to measure the relative distances and relative angles between neighbours. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

考虑障碍环境下的多机器人编队控制研究

编队和避障控制是机器人路径规划设计中的典型问题,文中提出了将leader—following法和人工势场法相结合的方法,来更好地完成多机器人在未知环境下的编队和避障控制。之前的研究只将leader—following算法用于多机器人的编队控制,而文中提出此方法也可以用于多机器人系统的避障控制。基于leader—following法,多机器人能自动编队并保持队形;而结合人工势场法,多机器人可以保持队形行进,在遇到障碍物的情况下变换队形避障,在避障后恢复原队形,最终到达目标。通过仿真实验证明,该算法实现了多机器人在未知环境下的自动编队和避障,从而证明了leader—following算法可以用于机器人的避障控制。     

Robust formation control of electrically driven nonholonomic mobile robots via sliding mode technique

This paper proposes a sliding mode formation control method for electrically driven nonholonomic mobile robots in the presence of model uncertainties and disturbances. We use the kinematic model based on the leader-following approach for the formation control of multiple robots. Unlike many researches considering only the kinematic model, we also consider the dynamic model including actuator dynamics to obtain the voltage input because it is more realistic to use the voltage as input than the velocity. Then, the sliding mode control method is used to deal with model uncertainties and disturbances acting on the mobile robots. The stability of the proposed control system is proven using Lyapunov stability theory. Finally, we perform computer simulations to demonstrate the performance of the proposed control system.     

Fuzzy Policy Reinforcement Learning in Cooperative Multi-robot Systems

A multi-agent reinforcement learning algorithm with fuzzy policy is addressed in this paper. This algorithm is used to deal with some control problems in cooperative multi-robot systems. Specifically, a leader-follower robotic system and a flocking system are investigated. In the leader-follower robotic system, the leader robot tries to track a desired trajectory, while the follower robot tries to follow the reader to keep a formation. Two different fuzzy policies are developed for the leader and follower, respectively. In the flocking system, multiple robots adopt the same fuzzy policy to flock. Initial fuzzy policies are manually crafted for these cooperative behaviors. The proposed learning algorithm finely tunes the parameters of the fuzzy policies through the policy gradient approach to improve control performance. Our simulation results demonstrate that the control performance can be improved after the learning.     

基于博弈论的多移动机器人聚集任务路径规划

针对多移动机器人聚集任务的路径规划,建立系统多目标优化数学模型,研究基于博弈论的求解方法。将n个机器人视为博弈的n方,建立Nash均衡博弈模型和共谋合作博弈模型,结合进化策略,对多移动机器人的运动协调进行研究。仿真实验结果表明,博弈分析方法可有效解决多机器人的协调路径规划问题。     

Fault-tolerant flocking for a group of autonomous mobile robots

Consider a system composed of mobile robots that move on the plane, each of which independently executing its own instance of an algorithm. Given a desired geometric pattern, the flocking problem consists in ensuring that the robots form this pattern and maintain it while moving together on the plane. In this paper, we explore flocking in the presence of faulty robots, where the desired pattern is a regular polygon. We propose a distributed fault tolerant flocking algorithm assuming a semi-synchronous model with a k-bounded scheduler, in the sense that no robot is activated no more than k times between any two consecutive activations of any other robot.The algorithm is composed of three parts: failure detector, ranking assignment, and flocking algorithm. The role of the rank assignment is to provide a persistent and unique ranking for the robots. The failure detector identifies the set of currently correct robots in the system. Finally, the flocking algorithm handles the movement and reconfiguration of the flock, while maintaining the desired shape. The difficulty of the problem comes from the combination of the three parts, together with the necessity to prevent collisions and allow the rotation of the flock. We formally prove the correctness of our proposed solution.     

Robust multiperson detection and tracking for mobile service and social robots

This paper proposes an efficient system which integrates multiple vision models for robust multiperson detection and tracking for mobile service and social robots in public environments. The core technique is a novel maximum likelihood (ML)-based algorithm which combines the multimodel detections in mean-shift tracking. First, a likelihood probability which integrates detections and similarity to local appearance is defined. Then, an expectation-maximization (EM)-like mean-shift algorithm is derived under the ML framework. In each iteration, the E-step estimates the associations to the detections, and the M-step locates the new position according to the ML criterion. To be robust to the complex crowded scenarios for multiperson tracking, an improved sequential strategy to perform the mean-shift tracking is proposed. Under this strategy, human objects are tracked sequentially according to their priority order. To balance the efficiency and robustness for real-time performance, at each stage, the first two objects from the list of the priority order are tested, and the one with the higher score is selected. The proposed method has been successfully implemented on real-world service and social robots. The vision system integrates stereo-based and histograms-of-oriented-gradients-based human detections, occlusion reasoning, and sequential mean-shift tracking. Various examples to show the advantages and robustness of the proposed system for multiperson tracking from mobile robots are presented. Quantitative evaluations on the performance of multiperson tracking are also performed. Experimental results indicate that significant improvements have been achieved by using the proposed method.