RoboCup机器人救援仿真中基于拍卖的任务分配算法

Task Allocation Algorithm Based on Auction in RoboCup Rescue Robot Simulation

  • 摘要: 为了有效解决RoboCup机器人救援仿真平台(RoboCup rescue simulation system,RCRSS)这一多智能体系统中的任务分配问题,提出了一种基于拍卖算法的救援机器人任务分配机制.设计的算法能够使智能体快速进入和离开拍卖市场以及完成动态角色的切换,同时引入焦虑度对分配后的任务进行动态调整,以适应动态变化的灾难环境.对比实验结果表明,相比遗传算法,采用本文算法的机器人救援队伍更能够适应多种复杂灾难环境,在有限时间内完成更多救援任务,并降低任务响应的迟滞性,提高了救援系统的整体效用.

     

    Abstract: In order to solve the task allocation problem in a multi-agent system, namely RoboCup robot rescue simulation system (RCRSS), a task allocation mechanism based on auction is proposed. Using the mechanism, the agents can enter and leave the auction market flexibly and switch role dynamically. Meanwhile the anxiety degree is introduced to adjust the allocation scheme after the basic task allocation. As a result, rescue agents can adapt to dynamic disaster environments. Experimental results show that the rescue team using the proposed method is more suitable for different complex disaster environments than the rescue team using the genetic algorithm in terms of fulfilling the rescue tasks, reducing the task response delay and improving the efficiency of rescue system in a limit time.

     

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