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

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.