Deep Reinforcement Learning Controller for 3D Path-following and Collision Avoidance by Autonomous Underwater Vehicles
This addresses the challenge of autonomous navigation in complex underwater environments, though it is incremental as it applies existing DRL techniques to a specific domain.
The paper tackled the problem of controlling autonomous underwater vehicles for 3D path-following and collision avoidance using Deep Reinforcement Learning, achieving human-level decision-making in extreme obstacle configurations without prior knowledge.
Control theory provides engineers with a multitude of tools to design controllers that manipulate the closed-loop behavior and stability of dynamical systems. These methods rely heavily on insights about the mathematical model governing the physical system. However, in complex systems, such as autonomous underwater vehicles performing the dual objective of path-following and collision avoidance, decision making becomes non-trivial. We propose a solution using state-of-the-art Deep Reinforcement Learning (DRL) techniques, to develop autonomous agents capable of achieving this hybrid objective without having à priori knowledge about the goal or the environment. Our results demonstrate the viability of DRL in path-following and avoiding collisions toward achieving human-level decision making in autonomous vehicle systems within extreme obstacle configurations.