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基于多目标决策理论的多机器人协调方法 被引量:5

Coordination of to multi-robots using behavior-based multi-objective decision theory
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摘要 多机器人协调问题是当前机器人技术领域的研究热点和难点.对多机器人之间的协调理论进行研究具有重大的理论和实际意义.为此,提出了一种基于多目标决策理论的多机器人协调方法,给出室内环境的拓扑建模法.在环境的拓扑图中利用A 算法搜索出每个机器人的静态无碰路径,用拓扑图的节点保证机器人运动的大方向正确,在局部范围内根据多目标决策理论和避碰规则集协调机器人的运动.通过计算机仿真试验验证了此方法的有效性和可行性. For multirobot cooperation and coordination is of great academic and applied significance,an approach is proposed for multirobot coordination using behavior based multiobjective decision theory,and a topological model of indoor environment with a static collisionfree path for every robot is presented by using the heuristic A* star arithmetic, and key nodes are used to ensure a rough path for every robot. The behavior sets in local areas are coordinated by using multiobjective decision and collision avoid rules. It is shown that robots can adaptively reach their goals during the simulation experiment.
出处 《哈尔滨工程大学学报》 EI CAS CSCD 2003年第3期308-312,共5页 Journal of Harbin Engineering University
基金 黑龙江省博士后科研启动金资助项目(LRB-KY01014).
关键词 多目标决策 多机器人协调 环境建模 拓扑图 multi-objective decision multi-robot coordination environment modeling topological graph
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参考文献5

  • 1DAVID J, ALEXANDER Z. Integrating spatial and topological navigation in a behavior- based multi-robot application [ A ]. International Conference on Intelligent Robotsand Systems (IROS99) [ C ]. Kyongju, Korea, 1999.
  • 2PIRJANIAN P, CHRISTENSEN H I. Behavior coordination using multiple-objective decision making [ A ]. SPIE Conf on Intelli-gent Systems and Advanced Manufacturing[ C ]. Pittsburgh, USA, 1997.
  • 3PIRJANIAN P, MATARIC M. Multi-robot target acquisition using multiple objec-tive behavior coordination [ A ].IEEE International Conference on Robotics and Automation [ C ]. San Francisco,2000.
  • 4PIRJANIAN P. Satisficing Action Selection [ A ] SPIE Conference on Intelligent Systems and Advanced Manufacturing[ C ]. Boston , USA, 1998.
  • 5ARAI Y, FUJII T, ASAMA H, et al. Collision avoidance in multi - robot systems based on mulit-layered reinforce- ment learning [ J ]. Robotics and Autonomous Systems, 1999,29:21 - 32.

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