COLREGs-compliant dynamic collision avoidance algorithm based on deep deterministic policy gradient

Abstract

In order to reduce collision avoidance accidents and improve the safety of ship navigation, a dynamic collision avoidance algorithm based on deep reinforcement learning is proposed in this paper. In order to avoid the fuzziness and uncertainty in the encounter process, the degree of risk is formulated to quantify the collision risk. International regulations for preventing collisions at sea (COLREGs) are quantified reasonably. Considering the factors of collision, position, speed, course and compliance with the COLREGs, the reward function of the algorithm is designed to ensure that the collision avoidance decision is safe and effective and meet the requirements of the COLREGs. Based on DDPG algorithm, the sample data processing mechanism is improved, the utilization rate of experience is improved, and the problems of long learning time and unstable training are solved. The navigation and collision avoidance for multiple ships are simulated respectively. The results show that this method can effectively avoid obstacle ships under the requirements of COLREGs, and it has good real-time performance and safety.