协作多机器人系统研究进展综述

协作多机器人系统是近年来机器人研究的热点,具有良好的应用前景.针对开展相关研究的迫切需要,总结了协作多机器人系统研究的国内外发展现状,并分别对无意识协作的仿生多机器人系统和有意识协作的异构多机器人系统研究进行论述.重点从系统构建和优化控制的宏观角度出发,对有意识协作异构多机器人系统的体系结构、环境感知以及优化控制3个方面的研究进展分别展开论述;对典型的多机器人验证任务和软硬件实验平台进行了总结;最后对协作多机器人系统的研究方向进行了展望.     

异构多机器人协作系统研究进展

异构多机器人系统可以发挥单一结构机器人在某个领域的优点而达到整体的最优配置,机器人的功能和接口协议对协作系统影响很大.IGRS协议是我国在信息设备协作领域中惟一的国际标准,为异构多机器人协作提供了有效的支持.对国内外相关研究进行系统地归纳和总结,找出需要解决的问题,并在课题组研制的多种机器人平台上,从3个方面阐述了基于IGRS协议的异构多机器人协作系统:异构机器人的定位、通信以及感知方案;异构机器人协商策略和分组方案;机器人的功能分类和规划,提出了细粒度可控的任务委托分配方案.     

基于人工免疫系统的异构多机器人联盟形成

对动态环境下多机器人联盟形成问题进行了研究,提出了一种基于人工免疫系统的异构多机器人联盟形成方法.该方法在对比人工免疫系统与多机器人系统相似关系的基础上,利用人工免疫系统的隐喻机制为面向动态感知任务的异构多机器人联盟形成问题提供了一种新的思路和解决方法.进行了未知非结构化环境下的多机器人协作搜集仿真实验,仿真结果表明所提方法可以使多机器人系统自主地形成机器人联盟以完成动态感知任务,提高了多机器人系统执行任务的效率.     

路网约束下异构机器人系统路径规划方法

由无人机(Unmanned aerial vehicles, UAV)和地面移动机器人组成的异构机器人系统在协作执行任务时,可以充分发挥两类机器人各自的优势.无人机运动灵活,但通常续航能力有限;地面机器人载荷多,适合作为无人机的着陆平台和移动补给站,但运动受路网约束.本文研究这类异构机器人系统协作路径规划问题.为了降低完成任务的时间代价,提出一种由蚁群算法(Ant colony optimization, ACO)和遗传算法(Genetic algorithm, GA)相结合的两步法对地面机器人和无人机的路线进行解耦,同时规划地面机器人和无人机的路线.第1步使用蚁群算法为地面机器人搜索可行路线.第2步对无人机的最优路径建模,采用遗传算法求解并将无人机路径长度返回至第1步中,用于更新路网的信息素参数,从而实现异构协作系统路径的整体优化.另外,为了进一步降低无人机的飞行时间代价,研究了无人机在其续航能力内连续完成多任务的协作路径规划问题.最后,通过大量仿真实验验证了所提方法的有效性.     

一种异构多机器人系统交互协议

随着机器人在各领域的广泛应用和任务种类多样化,单机器人执行任务受到任务规模和复杂程度的限制,使用多个机器人协作完成任务是解决问题的有效方法之一;在多机器人控制系统中,不同的任务往往需要不同结构和功能的机器人去执行,而且工业化的机器人各自遵循私有协议;在操作多机器人系统时,系统中的通信协议因不同的机器人而异,从而增加了控制过程中的协作难度;设计和制定一个统一的交互协议,便于协作信息在系统中通信,共同完成给定的任务;针对不同的机器人私有控制协议,在一致化交互接口的基础上,设计并实现了不同工业机器人之间的交互;通过工程实践,验证了一致性交互协议对于异构机器人系统的有效交互,对完成复杂问题作业具有借鉴作用。     

基于平行控制的异构多Agent机器人系统控制方法

复杂系统控制过程往往需要人综合控制系统、仿真系统,以及人在回路综合实现。构建基于多agent机器人系统及其实现策略,是多机器人系统研究和解决现实问题的热点。本文基于多部件综合作业机器人项目,针对不同作业机器人,提出一种异构多Agent机器人系统控制方法,此方法引入ACP理论的思想,将平行控制方法应用于机器人综合作业平台,通过人工系统与实际系统的虚实互动,使电脑的计算能力与人脑的灵活能力有效结合,实现了一个复杂系统的合理管控,为多机器人协作完成实际复杂控制系统提供了一种保证系统可靠性基础上提高效率的可供借鉴的理论和实现方法。     

基于评价选择策略的Internet多机器人协作控制研究

针对Internet多机器人系统中存在的操作指令延迟、工作效率低、协作能力差等问题,提出了多机器人神经元群网络控制模型。在学习过程中,来自不同功能区域的多类型神经元连接形成动态神经元群集,来描述各机器人的运动行为与外部条件、内部状态之间复杂的映射关系,通过对内部权值连接的评价选择,以实现最佳的多机器人运动行为协调。以互联网足球机器人系统为实验平台,给出了学习算法描述。仿真结果表明,己方机器人成功实现了配合射门的任务要求,所提模型和方法提高了多机器人的协作能力,并满足系统稳定性和实时性要求。     

格斗机器人竞赛中的多机器人协作技术研究

本文旨在通过多机器人系统的敌我识别、任务分配等关键技术的研究,实现多机器人竞赛项目中的有效协作功能,以提高机器人竞赛能力,论文采用理论推导、软件仿真、硬件设计与改造等相结合的方式来开展研究,以解决机器人何时协作、如何协作等棘手问题.     

机器人群体协作与控制的研究

本文针对多机器人系统,从群体的角度探讨多机器人协作、运行及控制机理. 包括 机器人群体协作与机器人社会的概念、应用背景,以及机器人群体协作与机器人社会系统的 研究目的、研究内容等,以建立机器人群体协作及控制研究的框架．     

多个自主机器人的协作策略研究

1引言多Agent系统是DAI的一个重要分枝，它越来越引起人们的兴趣和关注．但由于缺乏合适的分析工具，目前所研制的多Agent系统大都是经验性的［”’］．即使有一些关于多Agent的理论工具，但因太过于一般而很难在实际中发挥作用‘’］．多个自主机器人的协作系统是一种典型的分布式多Agent系统，其协作为DAI提供了很好的实验床［‘］．此问题很复杂，为了简化研究，本文仪研究两个自主机器人的协作问题．2机器人的协作21机器人协作的几个阶段协作的多个机器人作为整体要完成预定的任务，就必须具备协作决策的能力，而协作以结构方式进行…     

Towards cooperation of heterogeneous,autonomous robots: A case study of humanoid and wheeled robots

In this paper a case study of the cooperation of a strongly heterogeneous autonomous robot team, composed of a highly articulated humanoid robot and a wheeled robot with largely complementing and some redundant abilities is presented. By combining strongly heterogeneous robots the diversity of achievable tasks increases as the variety of sensing and motion abilities of the robot system is extended, compared to a usually considered team of homogeneous robots. A number of methodologies and technologies required in order to achieve the long-term goal of cooperation of heterogeneous autonomous robots are discussed, including modeling tasks and robot abilities, task assignment and redistribution, robot behavior modeling and programming, robot middleware and robot simulation. Example solutions and their application to the cooperation of autonomous wheeled and humanoid robots are presented in this case study. The scenario describes a tightly coupled cooperative task, where the humanoid robot and the wheeled robot track a moving ball, which is to be approached and kicked by the humanoid robot into a goal. The task can be fulfilled successfully by combining the abilities of both robots.     

Solving function distribution and behavior design problem for cooperative object handling by multiple mobile robots

This paper addresses the function distribution and behavior design problem for a multirobot system which incorporates a behavior-based dynamic cooperation strategy for object handling. The proposed multiple robot system is composed of a managing robot and homogeneous behavior-based robots. The cooperation strategy in this system is realized in two steps: designing the distributed robot's cooperative behavioral attributes according to the robot's abilities, and organizing these behavioral attributes so that team cooperation is realized. For indicating an incremental style of local behavior construction, an advanced design of cooperative behavior for coping with unknown disturbance is addressed. Additionally, two extended cooperation strategies designed for a path tracking task are described. These three strategies are based on the same concept on performing manipulation in coordination. Therefore, by considering the function distribution among the managing robot and worker robots, and considering behavior design of each worker robot, the proposed system is able to achieve the object handling task with different performances according to the task requirement, such as with or without path tracking and with or without contact with the environment. Experimental results demonstrate the applicability of the proposed system.     

An algorithm for cooperative task allocation in scalable,constrained multiple robot systems

The current trends in the robotics field have led to the development of large-scale multiple robot systems, and they are deployed for complex missions. The robots in the system can communicate and interact with each other for resource sharing and task processing. Many of such systems fail despite the availability of necessary resources. The major reason for this is their poor coordination mechanism. Task planning, which involves task decomposition and task allocation, is paramount in the design of coordination and cooperation strategies of multiple robot systems. Task allocation mechanism allocates the task in a mission to the robots by maximizing the overall expected performance, and thereby reducing the total allocation cost for the team. In this paper, we formulate a heuristic search-based task allocation algorithm for the task processing in heterogeneous multiple robot system, by maximizing the efficiency in terms of both communication and processing cost. We assume a set of decomposed tasks of a mission, which needs to be allocated to the robots. The near-optimal allocation schemes are found using the proposed peer structure algorithm for the given problem, where the number of the tasks is more than the robots present in the system. The cost function is the summation of static overhead cost of robots, assignment cost, and the communication cost between the dependent tasks, if they are assigned to different robots. Experiments are performed to verify the effectiveness of the algorithm by comparing it with the existing methods in terms of computational time and quality of solution. The experimental results show that the proposed algorithm performs the best under different problem scales. This proves that the algorithm can be scaled for larger system and it can work for dynamic multiple robot system.     

分布自主协作式的多机器人系统研究

本文作者在研究多机器人协调的基础上，将多机器人作为一个整体，从系统 角度研究多机器人系统的整体行为和组织结构，以人工智能的多自主体系统为理论基础，以网络通讯和分布数据库为技术基础，设计了多机器人分布自主协作系统的体系结构，提出了实现该系统需要研究的内容和解决的关键技术，介绍了我们在这方面的工作。     

基于积极团队情感基调的情感机器人协作任务分配拍卖算法

多机器人系统(Multi Robot System,MRS)通过引入机器人个体情感因素,可以有效提高个体的自主协作能力、决策能力以及多机器人系统的整体智能化水平。然而,以往研究主要集中于个体情感状态(情绪、个性等),缺乏从团队情感层面来探索积极团队情感基调(Positive Group Affective Tone,PGAT)对团队协作能力和团队有效性的影响。为了发挥PGAT在任务分配中的积极作用,降低因为团队成员情绪衰减而导致团队解散的风险,并增加团队协作能力和团队有效性,提出了基于PGAT的情感机器人协作任务分配拍卖算法。仿真追捕对比实验表明,相对于基于焦虑情感模型的改进合同网协议多机器人任务分配算法和基于自主意识的分布式情感机器人任务分配算法,基于PGAT的情感机器人协作任务分配拍卖算法的追捕成功率分别提高了269.3%和6.5%,任务分配成功率分别提高了138.7%和5.1%,平均追捕时间分别缩短了14.5%和26.3%,并且在150场追捕对比实验中,追捕时间小于对比算法的场次占比分别达到87.3%和90.7%。     

基于BDI模型的多机器人智能体系统设计

BDI模型是智能体设计的一种成熟结构，本文将BDI模型应用于多机器人智能体系统设计中．文章先从形式逻辑角度描述系统模型，然后讨论基于合同网的多机器人智能体的协作机制，最后给出基于BDI模型的多机器智能体的实现模型．     

Study on formation control system for underwater spherical multi-robot

Microsystem Technologies - Aiming at multi robot cooperation application requirements of our small-scaled underwater spherical robots, a cooperative formation control system with static and dynamic...     

协作多机器人系统的模块化设计与实现

本文从讨论基于分布式人工智能(DAI)中多智能体系统(MAS)理论的多机器人的合 作策略入手,先提出了适用于多机器人合作机制的系统控制结构,再针对具体任务背景,对 多机器人系统的结构和功能进行了模块化分解,设计了系统原型．最后简要地介绍一下利用 面向对象技术完成的仿真系统程序实现．     

Cooperative localization and evaluation of small-scaled spherical underwater robots

With the goal of supporting localization requirements of our spherical underwater robots, such as multi robot cooperation and intelligent biological surveillance, a cooperative localization system of multi robot was designed and implemented in this study. Given the restrictions presented by the underwater environment and the small-sized spherical robot, an time of flight camera and microelectro mechanical systems (MEMS) sensor information fusion algorithm using coordinate normalization transfer models were adopted to construct the proposed system. To handle the problem of short location distance, limited range under fixed view of camera in the underwater environment, a MEMS inertial sensor was used to obtain the attitude information of robot and expanding the range of underwater visual positioning, the transmission of positioning information could implement through the normalization of absolute coordinate, then the positioning distance increased and realized the localization of multi robot system. Given the environmental disturbances in practical underwater scenarios, the Kalman filter model was used to minimizing the systematic positioning error. Based on the theoretical analysis and calculation, we describe experiments in underwater to evaluate the performance of cooperative localization. The experimental results confirmed the validity of the multi robot cooperative localization system proposed in this paper, and the distance of cooperative localization system proposed in this paper is larger than the visual positioning system we have developed previously.