An Enhanced Whale Algorithm for Three-Dimensional Path Planning for Meteorological Detection of the Unmanned Aerial Vehicle in Complex Environments

The paper proposes a path planning method for meteorological detection of the unmanned aerial vehicle (UAV) in complex obstacle environments based on an enhanced multi-strategy whale optimization algorithm. In the design of the modeling, a weight coefficient method and coordinate system-based single-objective path planning model are developed to solve the UAV’s flight path. It has the shortest trajectory, minimum threat, flight altitude, flight angle, and obstacles as constraints. In the algorithm design, quasi-opposition-based learning, real-time boundary processing, and enhanced search mechanisms are introduced into the basic whale optimization algorithm. This increases population diversity and improves the algorithm’s global optimization ability and convergence performance. Through theoretical analysis, it has been proven that the time complexity of the improved algorithm is the same as the basic whale optimization algorithm, and the convergence of the improved algorithm has been theoretically analyzed. The multi-dimensional and multi-algorithm extreme value optimization of the CEC2017 test function, as well as the optimization solution to the three-dimensional path planning problem for meteorological detection of the unmanned aerial vehicle in four complex scenarios, have been carried out. The experimental results clearly show that, considering both the maneuverability and turning performance of the UAV, the planned path can safely and efficiently avoid hazards. Compared with other algorithms, in complex environments, the path planning scheme obtained from the path planning model can, to some extent, alleviate the impact of increasing the number of trajectory points on solving performance. The coverage of the path planning method for the improved algorithm is 100%, and the success rate of path planning is increased by about 50%. The improved algorithm has excellent optimization ability and generates a higher-quality path.