Hybrid Motion Planning and Formation Control of Multi-AUV Systems Based on DRL
Behnaz Hadi, Alireza Khosravi, Pouria Sarhadi · 2024
This paper presents a novel approach to planning and controlling the hybrid formation motion of a fleet of underactuated autonomous underwater vehicles (AUVs). The leader AUV performs end-to-end motion planning and obstacle avoidance using deep reinforcement learning (DRL). The followers, on the other hand, are guided by a backstepping technique to maintain the desired formation behind the leader. Neuro-adaptive strategies are employed to estimate the followers' unknown nonlinear terms. Operating within a machine learning (ML) framework, the leader is trained to formulate a control policy that guarantees the safe movement of the entire group towards the target. Theoretical analysis using the Lyapunov stability theory demonstrates that the AUVs' formation control system ensures uniform ultimate boundedness (UUB). The effectiveness of the proposed methodology is evaluated across a range of simulation scenarios.