Collision Avoidance for Group Targets Based on Clustering Velocity Obstacle Algorithm

In crowded waters, especially with the presence of group targets such as multiple fishing vessels with unpredictable movements, the vessel navigation often faces challenges on the safe collision avoidance. If a vessel navigates through a fishing vessel group, it may cause high risk and even accident occurrence. To solve this problem, this paper proposes a collision avoidance method for group targets in crowded waters by integrating the Density-based spatial clustering of applications with noise (DBSCAN) Clustering algorithm with the velocity obstacle (VO), namely CVO method. DBSCAN is first employed to identify the groups of multiple target vessels, and these groups are treated as individual targets for the collision avoidance. The Closest Point of Approach (DCPA) and the Time to Closest Point of Approach (TCPA) are calculated for each target. Based on the collision risk assessment, DCPA, and TCPA, high-risk targets are identified and filtered out. Finally, the VO is employed to effectively avoid the collisions with the grouped targets. Simulation experiments demonstrate that the proposed method enhances navigation safety and efficiency, making it valuable for the high-efficiency navigation of autonomous vessel in crowded waters.