Multiregion Joint Coverage for Environmental Monitoring Using Energy-Constrained AAVs

Collecting data from spatially dispersed regions using multiple autonomous aerial vehicles (AAVs) is a fundamental task form in environmental monitoring. In practical applications, varying monitoring requirements and limited sensing coverage capabilities of AAV team pose challenges to the adaptability and efficiency of collaboration. This article introduces an environmental monitoring framework based on a joint coverage pattern, allowing multiple AAVs to collectively cover a single region. The framework employs a “plan-then-divide” strategy. Initially, the visiting sequence of regions, along with their coverage proportions, is determined. This planning problem is formalized as the fractional team orienteering problem (FTOP), for which an efficient heuristic algorithm named adaptive large neighborhood search-enhanced genetic algorithm (AEGA) is proposed. Subsequently, joint coverage regions are divided into subregions according to the coverage proportions, employing a division criterion based on coverage costs. Two coverage path planning (CPP) methods are utilized for intraregion path generation. Simulation studies demonstrate the superior performance of the proposed heuristic algorithm compared with baseline methods, particularly in large-scale tasks. Furthermore, the proposed framework exhibits adaptability and efficiency under various monitoring requirements and AAV fleet configurations.