Learning-Based Discontinuous Path Following Control for a Biomimetic Underwater Vehicle

Abstract

This paper addresses a learning-based discontinuous path following control scheme for a biomimetic underwater vehicle (BUV) driven by undulatory fins. Despite the flexibility of the BUV motion, it faces the challenge of dealing with discontinuous paths affected by irregular seafloor topography and underwater vegetation. Therefore, BUV must employ path switching strategy to navigate to the next safe area. We introduce a discontinuous path following control method based on deep reinforcement learning (DRL). This method uses the line of sight (LOS) navigation algorithm to provide the Markov decision process (MDP) state inputs and the soft actor-critic (SAC) algorithm to train the control strategy of the BUV. Unlike the traditional fixed waveform control method, this method encourages the BUV to learn different waveforms and fluctuation frequencies through DRL. At the same time, the BUV has the ability to switch to a new path at necessary moments, such as when encountering underwater rocks. The results of simulations and experiments demonstrate the successful integration of the undulatory fins with the SAC controller, showcasing its efficacy and diversity in discontinuous underwater path following tasks.