Area segmentation based on UAV performance differences

Collaborative search is pivotal in obtaining battlefield environment information in future combat scenarios and has emerged a s a focal area of research in the domain of unmanned aerial vehicles (UAVs) task planning. The Entropy weight-TOPSIS algorithm is initially utilized to effectively address the UAV performance evaluation challenge. This paper introduces a Non-Equal Area Segmentation Algorithm based on Moving Feedback (NEASA-MF), which incrementally approximates the expected area and delineates precise segmentation lines. This algorithm allows for the search area to be partitioned into multiple sub-areas of varying sizes based on the comprehensive performance of the UAVs. Consequently, UAVs exhibiting superior performance can alleviate the search burden of those with lesser capabilities. Simulation results affirm the efficacy of the NEASA-MF algorithm in segmenting areas for both convex and concave polygons, validating its utility in strategic UAV deployment.