Taming an Autonomous Surface Vehicle for Path Following and Collision Avoidance Using Deep Reinforcement Learning

IEEE Access

Haakon Robinson¹  Eivind Meyer¹  Adil Rasheed¹  Omer San² 

[1] Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway;School of Mechanical and Aerospace Engineering, Oklahoma State University, Stillwater, OK, USA;

关键词: Deep reinforcement learning;    autonomous surface vehicle;    collision avoidance;    path following;    machine learning controller;   

DOI  :  10.1109/ACCESS.2020.2976586

来源: DOAJ

【 摘 要 】

In this article, we explore the feasibility of applying proximal policy optimization, a state-of-the-art deep reinforcement learning algorithm for continuous control tasks, on the dual-objective problem of controlling an underactuated autonomous surface vehicle to follow an a priori known path while avoiding collisions with non-moving obstacles along the way. The AI agent, which is equipped with multiple rangefinder sensors for obstacle detection, is trained and evaluated in a challenging, stochastically generated simulation environment based on the OpenAI gym Python toolkit. Notably, the agent is provided with real-time insight into its own reward function, allowing it to dynamically adapt its guidance strategy. Depending on its strategy, which ranges from radical path-adherence to radical obstacle avoidance, the trained agent achieves an episodic success rate close to 100%.

【 授权许可】

Unknown