Roadmap-Restricted Multi-Robot Collaborative Hunting Method Based on Improved Artificial Potential Field

Abstract

How to design a multi-robot collaborative hunting method according to the local roadmap has become a key issue in the field of Multi-Robot Systems (MRS). This paper firstly establishes a robot potential field model based on the characteristics of the local roadmap, combined with the basic idea of the artificial potential field method. Then a multi-robot collaborative hunting strategy called Mobile Prediction Collaborative Interception (MPCI) is proposed based on the robot potential field model. The Adaptive Artificial Potential Field (AAPF) and the constraints are proposed to improve the MPCI to solve the three main problems of Target Loss, Target Unreachable, and Following Deadlock in the hunting process. On this basis, AAPF-MPCI collaborative hunting algorithm is proposed to improve the efficiency and stability of the system. The final simulation results show that the AAPF-MPCI algorithm is more stable and effectively shortens the time spent for MRS to hunt prey robots in roadmap-restricted scenes.