基于信息融合的多智能体协作方法的研究与应用

将信息融合技术运用到多智能体系统中,利用信息融合方法对智能体得到的在空间上分布的其他智能体感知的局部信息进行融合,得到较完整的态势评估,以此来规划和协调多智能体系统的协作行为,提出了一种基于信息融合的多智能体协作方法。将该方法应用在机器人救援仿真系统中,结果表明该方法能够实现全局上的任务分解策略,有效提高了智能体协作能力。     

一种多智能体系统中的协作机制

在多智能体系统MAS中,自治的agent在完成任务时常常会出现任务重叠的“正交互”现象,从而使得系统效率低下。为了解决这一问题,文章提出了一种agent协作机制,采用最短路径的方法来找出agent最优的合作对象,优化整个系统处理问题的能力。实验证明该方法是有效的。     

仿真机器人足球赛中团队协作方法的研究

1.引言团队协作(teamwork)的研究跨越商业管理和心理学等不同学科,成为许多领域研究的焦点。目前许多人工智能研究者正在致力于构建复杂、动态的多智能体系统,在这些多智能体系统中,比如交互式的训练和教育,机器人足球赛,虚拟剧院以及基于Internet的信息集成中,团队协作和团队训练有着举足轻重的地位。这些领域中不仅需要多个智能体的参加来完成任务,而且需要团队中的智能体共同协作来有效地完成任务。团队协作不单纯是一些同时发生活动的简单协调,Cohen对团队协作有一经典的形象说明:“普通交通协调虽然是同时发生、并且由交通信号自动协调,但仍然不能     

基于案例的异构多机器人系统的协作研究

目的是研究异构多机器人系统中机器人之间的协作过程.基于足球比赛案例,将异构多机器人系统的任务分为找球、跟随、踢球等几个作业.以人形机器人和轮式机器人作为研究对象,并赋予不同的功能,对机器人能力进行建模.讨论如何以优化的方案分配给执行任务的机器人,并建立了一种参考模型.最后,以流程图方式说明了机器人的行为控制.实践表明,由具有不同能力的机器人共同协作可以更加有效地完成任务.     

基于MAS机器人协作机制的研究与应用

本文是在通过分析IR-SDS智能机器人系统平台的基础上实现基于MAS理论的多机器人协作机制,并且在分析合同网和熟人协作机制的基础上,提出了实时响应较好的合同网能力协作机制。通过在IR-SDS上进行实验,并分析实验结果证明了该协作机制的实时性和优越性。     

基于串联协作的多方法协作优化方法

为更好地求解复杂优化问题的全局最优解,提出了基于串联协作的多方法协作优化方法。它采用串联方式组织各个优化方法之间的协作。各个优化方法在每次协作中进行若干次优化,每一优化方法在优化过程中及优化结果中包含的优化信息被用于其它优化方法中,以提高整体优化性能。选择了遗传算法、模式搜索法和Powell法等三个直接优化方法进行串联协作组成的多方法协作优化方法的设计,并对其优化特性进行了深入的理论分析。最后,用三个复杂多维函数对算法性能进行了测试。计算实例表明,基于串联协作的多方法协作优化方法取得了优于单个优化方法的全局最优特性。     

起重吊装多机器人系统协作避障规划研究

针对起重吊装多机器人系统实际作业情况,进行了该机器人系统协作避障规划研究.首先,基于栅格法提出了优化姿态转角--动态栅格法的路径规划改进算法;其次,设计了基于传感器技术的自适应协作系统.该协作避障方法具备了寻找全局最优路线的能力,同时实现了多机器人系统内部的稳定协调合作;最后,建立了Matlab与LabVIEW联合仿真系统,仿真结果表明该系统是可行且高效的.     

多机器人协作搬运路径规划研究

针对多机器人协作系统,提出了一种新的混合定点转动和遗传算法的方法,解决其协作路径规划问题。该方法利用遗传算法并行计算、不易陷入局部最优的优点,具备概率上寻找全局最优解的能力,同时结合了定点转动法易实现、有效减少单机器人路径浪费的优点。仿真实验结果表明,该规划方法运算速度较快,在得到有效规划路径的同时,也易于实现对单机器人的控制。     

基于D-H法的农业采摘机器人运动协作控制系统设计

为提升农业采摘机器人运动协作控制性能,降低机器人碰撞概率,利用D-H法优化设计机器人运动协作控制系统。改装位置、力矩以及碰撞传感器设备,优化运动协作控制器与驱动器,调整系统通信模块结构,完成硬件系统的优化。利用D-H法构建农业采摘机器人数学模型,在该模型下,利用传感器设备实现机器人实时位姿的量化描述,通过机器人采摘流程的模拟,分配机器人运动协作任务,从位置和姿态等多个方面,确定运动协作控制目标,经过受力分析求解机器人实际作用力,最终通过控制量的计算,实现农业采摘机器人的运动协作控制功能。通过系统测试实验得出结论：与传统控制系统相比,机器人位置、姿态角和作用力的控制误差分别降低了约40mm、0.2°和1.2N,在优化设计系统控制下,机器人的碰撞次数得到明显降低。     

RoboCup中基于动作序列模型的动作决策

RoboCup机器人足球比赛是当前人工智能领域的一个研究热点,其目的在于为多智能体系统提供一个标准的研究平台.为了让RoboCup仿真比赛中球员智能体实时地作出合理的动作决策,提出一种基于动作序列模型的决策机制,通过对球员智能体的动作空间分解、建立动作序列并对动作序列进行评价,而让智能体选择出当前最优的动作执行.仿真结果表明应用这种决策机制提高了智能体对环境的适应性.     

基于MAS的复杂系统分布式求解策略与推理研究

针对复杂的连续生产线系统监测与故障诊断的动态、分布、实时和不确定特性，将多Agent及相关智能技术引入复杂故障诊断领域，提出了一种嵌套式基于消息传递的多Agent组织模型，分析了基于MAS的分布式智能故障诊断方法和过程；研究了模型系统的动态适应性和稳定性；设计了实时诊断Agent工作状态的表达机制；讨论了Agent间的协调协作机制及融合方法；给出了多Agent诊断系统诊断决策的集成描述结构；提出了任务分解与分配调度算法，以充分发挥Agent的社会性和基于场景的特点，使得在复杂系统的问题求解中能提供更为可靠的诊断结果，而且还能节约资源提高诊断效率．将其应用于某安全监控系统中，取得了与专家相似的诊断结果，克服了以往监控诊断系统存在的弊端，提高了企业的安全运行效率．与传统诊断方法相比，体现了Agent技术在复杂分布式问题求解领域的特有优势和良好前景．     

A framework for facilitating cooperation in multi-agent systems

This work introduces a multi-agent framework that facilitates cooperation in multi-agent robotic systems. It uses a layered approach based on Coloured Petri Nets for modelling complex, concurrent conversations among agents. In this approach each agent employs a Coloured Petri Net model that allows agents to follow a plan specifying their interactions. It also allows programmers to plan for the concurrent feature of the conversation and make sure that all possible states of the problem space are considered. The framework assists the agents to identify and adapt different strategies for teammates and task selection dynamically. The agents can change their strategies in the course of dynamic environments to improve their performance. We have examined the performance of the agents in this framework by developing some task selection and teammate selection strategies for agents in a disaster scenario.     

Applying an Agent-Oriented Methodology to the Design of Artificial Organizations: A Case Study in Robotic Soccer

The multi-agent paradigm is widely used to provide solutions to a number of organizational problems related to the collective achievement of one or more tasks. All these problems share a common difficulty of design: how to proceed from the global specification of a collective task to the specification of the local behaviors to be provided to the agents? We have defined the Cassiopeia method whose specificity is to articulate the design of a multi-agent system around the notion of organization. This paper reports the use of this method for designing and implementing the organization of a soccer-playing robotic team. We show why we chose this application and how we designed it, and we discuss its interest and inherent difficulties in order to clearly express the needs for a design methodology dedicated to DAI.     

基于多Agent的高校教学评测系统模型研究

基于多Agent技术所构造的协同设计系统具有多Agent系统的分布性、协作性和智能性的特点。在分析目前高校教师教学评价系统的基础上,提出了一个基于多Agent系统(MAS)的高校教师教学评测系统模型(MATES),并着重讨论了该系统中各Agent的功能和Agent之间的协作关系。该模型为教师教学评价的自动化、智能化和高效率提供了一个良好的计算平台。     

A Constraint-Based Robotic Soccer Team

It is a challenging task for a team of multiple fast-moving robots to cooperate with each other and to compete with another team in a dynamic, real-time environment. For a robot team to play soccer successfully, various technologies have to be incorporated including robotic architecture, multi-agent collaboration and real-time reasoning. A robot is an integrated system, with a controller embedded in its plant. A robotic system is the coupling of a robot to its environment. Robotic systems are, in general, hybrid dynamic systems, consisting of continuous, discrete and event-driven components. Constraint Nets (CN) provide a semantic model for modeling hybrid dynamic systems. Controllers are embedded constraint solvers that solve constraints in real-time. A controller for our robot soccer team, UBC Dynamo98, has been modeled in CN, and implemented in Java, using the Java Beans architecture. A coach program using an evolutionary algorithm has also been designed and implemented to adjust the weights of the constraints and other parameters in the controller. The results demonstrate that the formal CN approach is a practical tool for designing and implementing controllers for robots in multi-agent real-time environments. They also demonstrate the effectiveness of applying the evolutionary algorithm to the CN-modeled controllers.     

ADE: STEPS TOWARD A DISTRIBUTED DEVELOPMENT AND RUNTIME ENVIRONMENT FOR COMPLEX ROBOTIC AGENT ARCHITECTURES

In this paper we present the agent architecture development environment (ADE), intended for the design, implementation, and testing of distributed robotic agent architectures. ADE is unique among robotic architecture development environments in that it is based on a universal agent architecture framework called APOC, which allows it to implement architectures in any design methodology, and in that it uses an underlying multi-agent system to allow for the the distribution of architectural components over multiple host computers. After a short exposition of the theory behind ADE, we present the multi-agent system setup and give an example of using ADE in a multi-robot setting. A general discussion then highlights some of the novel features of ADE and illustrates how ADE can be used for designing, implementing, testing, and running agent architectures.     

MAS Builder：一种基于计划的多Agent系统开发平台

多Agent系统便于建模和实现复杂、庞大的应用系统，是一种新的软件开发范型。本文简要介绍了一种基于计划的通用多Agent系统开发平台-MAS Builder，并对其体系结构、基于计划的多Agent合作和任务管理等问题进行了较为详细的阐述。     

基于学习者个性因素的多Agent协同学习系统

未解决当前的远程教育系统存在形式单一和被动教学等问题,该文提出了一个基于学习者个性因素的多Agent学习系统模型。该模型结合智能代理技术,通过分析学习者个性因素,给出了个体Agent能力描述语言,提出了新的个性化分组策略和学习任务分配策略,采用补偿机制鼓励agent合作,结合状态空间搜索理论使M AS系统具有更强的解题能力,并可满足学习者主动学习的要求,还能在一定程度上节约系统的通讯。     

基于多Agent的智能监测与诊断技术

由于Agent系统是一个具有自主性、反应性、主动性和社会性的基于硬件和软件的计算机系统,其具有强的环境适应性,能够提高故障诊断的准确率,文中把Agent技术引入智能故障诊断之中。本文以诊断Agent为基础,构造了基于多Agent的监测与诊断模型,并对协作诊断中的任务分解策略、通信机制和诊断结果的综合等关键技术进行了分析,同时对模型实现中的软件机制进行了研究,从而为智能监测诊断的实现提供了支持。     

Evaluation of a grey scale tactile array sensor pad for robotic applications

Since sensory feedback is an important part of robot control and the acquisition, manipulation, and recognition of objects, incorporating a sense of touch into a robotic system can greatly enhance the performance of that system. This article describes the evaluation of a recently developed low-resolution tactile array sensor pad system for use in robotic applications. Computer algorithms are developed which acquire data from the sensor pad and display the data on a CRT screen. Vision algorithms are implemented in order to extract the necessary information from the tactile data which will aid in the acquisition, manipulation, and recognition of objects. An object's pose is estimated by calculating its center of gravity (position) and principal axis (orientation). Recognizing an object and distinguishing between different objects is accomplished by implementing algorithms which estimate an object's perimeter (shape) and area (size). This work demonstrates that a low-resolution tactile array sensor is capable of providing the information that is required for many robotic applications in which objects must be acquired, manipulated, and recognized. Such a system provides a low-cost alternative to more conventional vision-based systems.