Threat potential field based Pursuit–Evasion Games for underactuated Unmanned Surface Vehicles

Unmanned Surface Vehicles (USVs) have been widely applied in ocean engineering. However, there are few studies for Pursuit–Evasion Game based on USVs. Most relevant pursuit game strategies based on nonrepresentational agents are simple and do not consider the USV’s underactuation. In order to solve this problem, this paper proposes a distributed pursuit game algorithm for USVs based on Threat potential fields (TPFs). The pursuit planning can be divided into three parts based on algorithm structure: First, according to the evader’s information, the proposed algorithm can quantitatively establish a unique TPF for evaluating the danger of the evader. Second, in the TPF, energy peak and energy valley concepts are innovatively presented to provide suitable conditions for the hunters’ pursuit. Third, the pursuit game optimization model is established based on a TPF for the hunters’ game decision-making by considering the orientation, heading, and formation shape of the USVs in the pursuit process. The proposed game optimization model is then solved using the particle swarm optimization algorithm to obtain the strategies. Finally, the effectiveness of the proposed algorithm is demonstrated via numeric simulation of USVs.