Research on Multi-UAV Collaborative Path Planning Methods for AtoN Inspection

This study aims to address the path planning problem for the cooperative surveillance of AtoN using multiple drones in maritime navigation tasks, with the goal of improving task efficiency and safety. A method for multi-drone cooperative surveillance path planning of AtoN is proposed. Firstly, latitude and longitude coordinates of the mark are transformed into a plane coordinate system. Firstly, latitude and longitude coordinates of the mark are transformed into a plane coordinate system, the K-means clustering algorithm is employed to cluster the positions of AtoN, dividing them into multiple surveillance regions. Secondly, the K-means clustering algorithm is employed to cluster the positions of AtoN, dividing them into multiple surveillance regions based on the number of drones available. Finally, the ant colony algorithm is used to optimize the paths of multiple drones to obtain the shortest total surveillance path. Using physical AtoN in the Pingtan sea area and three drones as an example, the experimental results demonstrate that the proposed method significantly enhances task performance. That the proposed method significantly enhances task efficiency compared to using a single drone with ant colony algorithm-based path planning. The total path length is reduced by 24.3% and the number of drones is reduced by 2.4%. Total path length is reduced by 24.6706 km, saving 5.4955% of the travel distance. Additionally, this method effectively reduces the travel distance for individual drones and shortens the travel time. Additionally, this method effectively reduces the travel distance for individual drones and shortens the flight coverage radius. The proposed method provides strong support for the collaborative monitoring of AtoN by multiple drones in maritime navigation tasks and can be extended to coordinate the scheduling of a larger area with more drones. This approach holds significant theoretical and practical significance.