Distributed Task Assignment Method for Multiple Robots Based on Dynamic Auction Rules

Jiajie Xu, Chin-Yin Chen, Si-lu Chen, Qiang Liu
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Abstract

Existing researchs on multi-robots task assignment focus on improving the task assignment efficiency without considering the task execution time and ignoring the overall task completion efficiency, which leads to its low computational efficiency in dynamic task assignment scenarios. In addition, the use of a centralized control structure requires high communication quality between robots. To address the above issues, we propose a distributed task assignment method for multiple robots based on dynamic auction rules. The method uses distributed control of robot swarms to share and dynamically update each other's task sets, employs dynamic auction rules for task bidding, adds links to adjust the task execution order, and considers the overall task completion efficiency. Finally, relevant experiments are designed, and the experimental results show that the algorithm is more efficient in terms of distribution and stability, balancing higher execution efficiency and lower motion cost.
基于动态拍卖规则的多机器人分布式任务分配方法
现有的多机器人任务分配研究侧重于提高任务分配效率,没有考虑任务的执行时间,忽略了任务的整体完成效率,导致其在动态任务分配场景下的计算效率较低。此外,集中式控制结构的使用要求机器人之间的通信质量高。为了解决上述问题,我们提出了一种基于动态拍卖规则的多机器人分布式任务分配方法。该方法利用机器人群的分布式控制实现彼此任务集的共享和动态更新,采用动态拍卖规则进行任务竞价,增加环节调整任务执行顺序,并考虑整体任务完成效率。最后,设计了相关的实验,实验结果表明,该算法在分配和稳定性方面具有更高的效率,平衡了更高的执行效率和更低的运动成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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