Research on USV path planning method based on CW-RNN framework

This paper presents an Unmanned Surface Vehicle (USV) path planning algorithm based on a Clockwork-RNN (CW-RNN) framework. Compared with RNN, CW-RNN divides the hidden state matrix into several small modules and uses a method similar to clock frequency mask to divide the memory of RNN into several parts, so that each part of CW-RNN memory matrix can process data at different times and enhance the memory effect. The existence of ocean current will cause drifting motion in the navigation track of unmanned ship, and make the ship deviate from the planned path and direction. In view of this interference factor, ocean current vectors of different sizes and directions are added in the simulation environment to make the environmental model closer to the actual sea surface. As the environment becomes more complex, reinforcement learning takes a long time to train in the complex environment and is not easy to converge. Therefore, this paper combines reinforcement learning method with the traditional path planning method Dijkstra algorithm, inputs the local map information detected by unmanned ship into Dijkstra algorithm to give task sub targets, and uses these sub targets to guide unmanned ship and improve the search efficiency of neural network. Finally, the simulation results and analysis show that the training algorithm is effective in the current environment.