基于时空感知增强的深度 Q 网络 无人水面艇局部路径规划.

Local path planning for unmanned surface vehicle ( USV) plays an important role in maritime rescue and marine transportation. Existing local path planning algorithms achieve good results in simple scenarios, but have poor performance when facing complex obstacles and sea current disturbances present in the environment. To this end, this paper proposed a reinforcement learning algorithm based on spatial and temporal sensing-enhanced deep Q-network. Firstly, it introduced a multiscale spatial attention module to capture the multiscale spatial information of distance sensors, which enhanced the perception capability of complex obstacle environments. Secondly, it used the LSTM-based current sensing module to extract the temporal sequence features of the current disturbance environment, which enhanced the perception capability of the current disturbance. In addition, by simulating the sensor and motion model of USV, it designed the reinforcement learning state space, action space and direction-guided reward function, it improved the navigation performance and convergence speed of the algorithm. Simulation experiments in complex scenarios show that the proposed algorithm improves both success rate and average arrival time metrics comparing to the original algorithm, and the algorithm shows strong adaptability to complex environment. [ABSTRACT FROM AUTHOR]