Automatic Collision Avoidance Route Planning Method in Complex Navigation Environments

Efficient collision avoidance (CA) route planning is one of the important technologies for ensuring the safety of autonomous ships. Many CA route planning studies have been developed for the open sea, but few studies have been conducted for ships in complex navigation environments. A complex navigation environment refers to water encompassing various factors, such as multiple obstacles, varying water depths, ships collision risk, and other maritime challenges. These factors collectively contribute to the complexity of the environment. CA planning in this type of environment is a special type of route planning. To address CA in complex navigation environments, firstly, the route binary tree algorithm is utilized for conducting global route planning in order to determine the optimal path. Secondly, this paper proposes a dynamic generation of local collision avoidance trajectories along the optimal path, seamlessly integrating global route optimization and local collision avoidance. Finally, a comparative analysis is conducted using classical techniques to verify the effectiveness of the proposed method. Numerical simulation results reveal that when a ship is taken as the object of CA in the open sea, the route generated using the proposed method exhibits a remarkable level of unity with the one generated by the benchmark method of improved Artificial Potential Field (APF), particularly in terms of collision avoidance strategy selection, collision avoidance trends, and the actual trajectory. The former's computational efficiency is improved by at least 30%. When dealing with CA at sea, with complex navigation obstacles, the proposed method considers static complex navigation obstacles, dynamic ships, and their unpredictable strategies to timeously generate a safe path which considers some key rules from the COLREGS (International Regulations for Preventing Collisions at Sea) (IMO, 1972).

Automated summary

Efficient collision avoidance (CA) route planning is crucial for ensuring autonomous ship safety. However, the research on CA route planning in complex navigation environments is limited. This paper proposes a method that dynamically generates local collision avoidance trajectories, combining global route optimization and local collision avoidance. Numerical simulation results demonstrate a significant level of unity between the proposed method and the benchmark method, with improved computational efficiency.