Multi-robot Dynamic Task Allocation Based on Improved Auction Algorithm

Abstract

Multi-robot task detection and task execution is an important direction that people have considered recently. We have considered the dynamic issues related to the detection and execution tasks of multiple robots. This paper presents an improved auction algorithm to solve this problem and combines it with the auction algorithm by optimizing the execution capacity utilization and load balancing of the robot. By designing a new auction cost function, the execution ability and load balancing of the robot are added to the auction algorithm, and the matching of the execution ability and task difficulty is added to the assignment of task execution. In addition, we also add a task sequence adjustment mechanism to avoid the redundant loss of the robot due to a lack of global consideration during a single auction. We compare the algorithm with SSI and CBBA. The experimental results show that this method is superior to the existing methods of performance utilization and load balancing.