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This research addresses the crucial problem of collision avoidance decision making for autonomous ships under diverse encounter situations. Building upon the Deep Deterministic Policy Gradient (DDPG) algorithm, appropriate reward functions based on the International Regulations for Preventing Collisions at Sea (COLREGS) have been designed to effectively guide intelligent agents in acquiring optimal strategies. By incorporating the concept of potential reward shaping, the proposed approach ensures efficient obstacle avoidance while adhering strictly to the established rules. Moreover, extensive simulations have been conducted to validate the algorithm’s performance in collision avoidance for both dual-ship and multi-ship scenarios under varying encounter situations, and a comparative analysis with the TD3 algorithm has been undertaken. The obtained results demonstrate that the proposed algorithm exhibits rapid convergence and stable training performance. The resulting models successfully achieve collision-free navigation while strictly adhering to the COLREGS. Particularly, in two-ship encounter situations, the proposed algorithm outperforms the trajectory planned by the TD3 algorithm in terms of shorter path length and higher efficiency. [ABSTRACT FROM AUTHOR]

This research addresses the crucial problem of collision avoidance decision making for autonomous ships under diverse encounter situations. Building upon the Deep Deterministic Policy Gradient (DDPG) algorithm, appropriate reward functions based on the International Regulations for Preventing Collisions at Sea (COLREGS) have been designed to effectively guide intelligent agents in acquiring optimal strategies. By incorporating the concept of potential reward shaping, the proposed approach ensures efficient obstacle avoidance while adhering strictly to the established rules. Moreover, extensive simulations have been conducted to validate the algorithm’s performance in collision avoidance for both dual-ship and multi-ship scenarios under varying encounter situations, and a comparative analysis with the TD3 algorithm has been undertaken. The obtained results demonstrate that the proposed algorithm exhibits rapid convergence and stable training performance. The resulting models successfully achieve collision-free navigation while strictly adhering to the COLREGS. Particularly, in two-ship encounter situations, the proposed algorithm outperforms the trajectory planned by the TD3 algorithm in terms of shorter path length and higher efficiency.

ObjectiveIn order to solve the multi-objective collision avoidance problem of unmanned surface vehicles (USVs) in open waters, this paper takes a medium-sized USV as the object and carries out the research and application exploration of a local path planning algorithm based on the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS). MethodsA virtual obstacle line method is proposed to load the constraints of COLREGS based on the RRT algorithm for meeting the practical requirements of collision avoidance path planning in open water. In light of the problem that the RRT algorithm does not consider the speed dimension, a velocity obstacle (VO) algorithm is introduced. Next, a VO-RRT fusion algorithm is proposed and given the most dangerous obstacle strategy in order to solve the problem of real-time collision avoidance under multi-objective conditions. ResultsThe simulation and real ship test results show that the proposed algorithm has better real-time performance and takes less than 50 ms to undertake path re-planning. The collision avoidance path planned by the algorithm meets the relevant requirements of Articles 6, 8 and 13–18 of COLREGS. The proposed method can effectively deal with the multi-target collision avoidance problem in open waters. ConclusionThe proposed method has good rule compliance, real-time and safety performance, and high practical value.

Aiming at the path planning problem of unmanned surface vehicle (USV) in complex dynamic environment with moving multi-obstacle ships, a path planning method based on improved RRT*-Smart (RTSNew) was designed.Firstly, the sampling mode of nodes was optimized, nodes were sampled in the polar coordinate system with USV as the origin, an elliptical sampling range constraint was adopted to avoid invalid sampling, and a historical path buffer pool was used to make full use of the historical path.The optimization greatly reduced the amount of calculation and improved the speed of path planning.Secondly, the expansion mode of nodes was improved.In order to avoid treating dynamic obstacles as static obstacles, time information was added to each node to realize dynamic collision detection and the full use of dynamic obstacles motion information greatly improves the feasibility of the planned path.At the same time, considering the maneuverability of USV, the angle constraint was added in expansion of nodes to ensure smooth path.Finally, virtual obstacles were designed to mobile obstacle ships to make the planned path comply with International Regulations for Preventing Collisions at Sea (COLREGS).Based on VREP platform, the USV navigation simulation experiments and comparative experiments were carried out.The results show that RTSNew can make USV reach the destination efficiently and safely, and it performs better in planning efficiency, path optimization and path security than traditional methods in complex dynamic environment with multi-obstacle ships.RTSNew ensures that the motion path complies with COLREGS, and solves the problems of traditional methods: treating the dynamic obstacles as static obstacles, ignoring COLREGS, large amount of calculation and low efficiency, not suitable for the complex dynamic environment with moving multi-obstacle ships.

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