Iterative Task Decomposition and Allocation for Fixed-Wing Multi-UAV Coverage Path Planning

Abstract

Fixed-wing unmanned aerial vehicles (UAVs) have become essential for large-area coverage missions. However, balancing individual workloads while satisfying nonholonomic constraints remains challenging when task areas are sparse and irregular. To address this gap, we present an integrated framework that first groups dense grids into task areas by a clustering algorithm, applies a sequential greedy algorithm (SGA) for initial assignment, and refines the solution through an iterative task decomposition and allocation (ITDA) scheme before generating Dubins-feasible coverage paths. The proposed method shortens the overall makespan by 30.17%, and reduces the execution time gap between UAVs by 92.3%. These results demonstrate that coupling iterative decomposition with fixed-wing UAV path planning under nonholonomic constraints enhances both efficiency and workload balance in multi-UAV coverage path planning.