Different-Flow-Field Adaptability-Oriented AUV Path Planning: A Continual Distributional Soft Actor–Critic Method

Autonomous Underwater Vehicle (AUV) is crucial to the Internet of Underwater Things setup and upkeep. However, Flow Fields (FFs) involving internal waves and submerged currents exhibit spatiotemporal diversity. Differences in their characteristics lead to an adaptability bottleneck for Path Planning (PP). Motivated by this challenge, an adaptability-oriented PP method, which is termed Continual Distributional Soft Actor-Critic (CDSAC), is proposed. Note that most existing learning-based methods perform well in a specific FF. When AUV operates across spatiotemporal shifts, however, FF variation necessitates extensive retraining of these methods for adaptation. CDSAC incorporates an Anti-Forgetting Replay (AFR) and a Policy Learning Accelerator (PLA) to improve the adaptability and generalization to different FFs. Here, AFR enhances the resistance to catastrophic forgetting (CF) for historical FFs. After that, PLA extracts the invariant features from different FFs and filters out spurious features to achieve a rapid convergence. Extensive simulations using actual FF data show that the proposed method has excellent adaptability in various FFs. Testing on a Hardware-In-the-Loop simulation platform shows the resistance to CF and generalization to unseen FFs of the trained model.