Deep Reinforcement Learning Controller for 3D Path Following and Collision Avoidance by Autonomous Underwater Vehicles

Frontiers in Robotics and AI

Simen Theie Havenstrøm¹  Adil Rasheed²  Omer San³ 

[1]Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway

[2]Mathematics and Cybernetics, SINTEF Digital, Trondheim, Norway

[3]School of Mechanical and Aerospace Engineering, Oklahoma State University, Stillwater, OK, United States

关键词: continuous control;    collision avoidance;    path following;    deep reinforcement learning;    autonomous under water vehicle;    curriculum learning;   

DOI  :  10.3389/frobt.2020.566037

来源: DOAJ

【 摘 要 】

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 into 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 nontrivial. 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 a priori knowledge about the goal or the environment. Our results demonstrate the viability of DRL in path following and avoiding collisions towards achieving human-level decision making in autonomous vehicle systems within extreme obstacle configurations.

【 授权许可】

Unknown