完全信息下的四足机器人多对一追捕问题研究OA

The pursuit-evasion problem has wide applications in areas such as confrontation,cooperation,and search.This paper studies the multi-to-one pursuit-evasion game problem under complete information conditions.In this scenario,the positional information of all participants is publicly available.Based on this,a continuous random game framework is constructed,and using the fixed-point theorem,the existence of Nash equilibrium strategies for the pursuit-evasion game is proven within this framework.To achieve this,a method based on deep reinforcement learning is proposed,combining the concept of virtual self-game,to solve for the optimal strategies of both pursuer and evader.Then,comparisons are made with other traditional pursuit algorithms through simulations,demonstra-ting that the pursuit success rate of the proposed algorithm can reach 90%.Finally,experiments are conducted using a physical capture platform to validate the effectiveness and practicality of the proposed approach.