摘要
针对多机器人协商中缺少心智状态与实际任务执行能力、通信带宽拥挤导致的低实时性以及从协商历史中缺乏快速学习等问题,提出一种多移动机器人任务协商模型。首先,阐述了机器人基本运动形态;其次,为多机器人协商定义了基于π演算的心智状态(信念、目标、意图、知识更新等)与能力状态(协作、能力判断、任务分配等);再次,构建了多机器人协商模型,研究了协商周期、协商任务、协商效用估计、协商分配协议、学习机制等。最后在机器人足球比赛平台上验证了方法的有效性。
Concerning the problems of lacking the mind states and task handling capability, low real-time caused by congested bandwidth and slow learning from negotiation history, a task negotiation model for multiple mobile-robots was proposed. Firstly, the basic moving state of robot was shown. Secondly, the states of mind （belief, goal, intention, knowledge update, etc. ） and ability （ cooperation, capability judgment, task allocation, etc. ） based on π calculus for the negotiation of multiple mobile-robots were defined. Thirdly, the negotiation model of multiple mobile-robots was constructed, in which the negotiation period, negotiation task, utility estimation, negotiation allocation protocol, learning mechanism were studied. Finally, the validity of model was proved through experiments of robot soccer.
出处
《计算机应用》
CSCD
北大核心
2013年第2期346-349,共4页
journal of Computer Applications
基金
国家自然科学基金资助项目(61075076)
广东省高等学校科技创新项目
广东高校石化装备故障诊断与信息化控制工程中心项目(512009)
广东省自然科学基金资助项目(8152500002000003)
关键词
多移动机器人
Π演算
Q学习
任务分配
效用估计
muhiple mobile-robots
w calculus
Q learning
task allocation
utility estimation
