Path Planning Method for Unmanned Surface Vessel in On-call Submarine Search Based on Improved DQN Algorithm
Aiming at the situation that the unmanned surface vessel ( USV) manoeuvrs in the course and speed of on-call anti-submarine,a path planning method for USV based on the improved deep Q-learning ( DQN) algorithm is proposed. The proposed method uses the on-call submarine search model and introduces an improved deep reinforcement learning algorithm to obtain an optimal path by jointly adjusting the action space,action selection strategy and reward of the USV. The algorithm adopts a time-varying dynamic greedy strategy. The strategy can adaptively adjust the USV action selection according to the environment and the learning effect of the neural network,which improves the global search ability and avoids falling into the local optimal solution. The piecewise nonlinear reward and punishment function is set according to the obstacle environment and the current position of the USV so as to improve the convergence speed of the algorithm while avoiding the obstacles. Bezier algorithm is added to smooth the path. The simulated results show that the planning effect of the proposed method is better than DQN algorithm,A* algorithm and APF algorithm in the same environment,and it has better stability,convergence and safety.
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