Facilitating Multi-UAVs application for rescue in complex 3D sea wind offshore environment: A scalable Multi-UAVs collaborative path planning method based on improved coatis optimization algorithm

With the development of marine economy, maritime activities are becoming more frequent, and the importance of maritime rescue missions is becoming increasingly prominent. Multi-UAVs collaborative search and rescue provides a new solution for maritime rescue, and the UAV path planning problem is one of the key challenges. First, this paper uses the lamb-Ossen model to simulate the sea wind and construct a 3D offshore environmental model with sea wind. Secondly, this paper comprehensively considers factors such as sea wind and distance between UAVs, and proposes a multi-UAVs path cost function. Meanwhile, a guided collaborative control mechanism is proposed to form a novel multi-UAVs collaborative path planning method. In addition, the multiUAVs path solution uses an improved multi-Dimensional Coati Optimization Algorithm (MDCOA), designs new strategies through Levy flight, differential operator, and dynamic opposite learning, and adds a covariance matrix learning (CML) stage to expand the search perspective. Finally, the MDCOA algorithm was used to solve the multi-UAVs paths in different offshore simulation scenarios. The paths obtained by the algorithm reduced the path cost and showed excellent performance compared with other algorithms. Experimental results prove the effectiveness and scalability of the algorithm proposed in this paper.