Task Parameter Planning Algorithm for UAV Area Complete Coverage in EO Sector Scanning Mode

Abstract

When Unmanned Aerial Vehicles (UAVs) are used in search and rescue operations, electro-optical (EO) devices are usually used as the detection equipment, and area coverage is used as the main search method. However, the sector scanning mode of EO puts forward higher requirements for task parameter planning. First, to ensure there is no missing coverage, a method to determine the full coverage width of EO equipment in sector scanning mode is proposed. Second, the constraint of no interval missing and the model of the speed-to-high ratio constraint are established, and the constraints of other factors are addressed in the context of the problem situation. Third, a coverage efficiency index is proposed for the boustrophedon coverage of a rectangular area, and a comprehensive coverage index is established. Finally, task parameter planning algorithms are designed, based on Immune Algorithm (IA), Grey Wolf Optimization (GWO) and Variable Neighborhood Search (VNS), respectively. The simulation results showed that the designed algorithms, based on IA, GWO and VNS, can effectively solve task planning problems. In general, IA is more suitable for offline occasions, VNS is suitable for online real-time planning, and GWO has characteristics between the two. The coverage process, based on optimized parameters, meets all constraints, has higher search efficiency and does not miss areas, proving the correctness of these models and the effectiveness of the planning algorithm. The research presented in this paper provides a technical basis for efficient and fully automated target search and rescue.