A novel reinforcement learning framework-based path planning algorithm for unmanned surface vehicle

Unmanned surface vehicles (USVs) nowadays have been widely used in ocean observation missions, helping researchers to monitor climate change, collect environmental data, and observe marine ecosystem processes. However, path planning for USVs often faces several inherent difficulties during ocean observation missions: high dependence on environmental information, long convergence time, and low-quality generated paths. To solve these problems, this article proposes a novel artificial potential field-heuristic reward-averaging deep Q-network (APF-RADQN) framework-based path planning algorithm, aiming at finding optimal paths for USVs. First, the USV path planning is modeled as a Markov decision process (MDP). Second, a comprehensive reward function incorporating artificial potential field (APF) inspiration is designed to guide the USV to reach the target region. Subsequently, an optimized deep neural network with a reward-averaging strategy is constructed to effectively enhance the learning and convergence speed of the algorithm, thus further improving the global search capability and interface performance of USV path planning. In addition, the Bezier curve is applied to make the generated path more feasible. Finally, the effectiveness of the proposed algorithm is verified by comparing it with the DQN, A*, and APF algorithms in simulation experiments. Simulation results demonstrate that the APF-RADQN improves the interface ability and path quality, significantly enhancing the USV navigation safety and ocean observation mission operation efficiency.