Clustering-Based Multi-Region Coverage-Path Planning of Heterogeneous UAVs

Abstract

Unmanned aerial vehicles (UAVs) multi-area coverage-path planning has a broad range of applications in agricultural mapping and military reconnaissance. Compared to homogeneous UAVs, heterogeneous UAVs have higher application value due to their superior flexibility and efficiency. Nevertheless, variations in performance parameters among heterogeneous UAVs can significantly amplify computational complexity, posing challenges to solving the multi-region coverage path-planning problem. Consequently, this study studies a clustering-based method to tackle the multi-region coverage path-planning problem of heterogeneous UAVs. First, the constraints necessary during the planning process are analyzed, and a planning formula based on an integer linear programming model is established. Subsequently, this problem is decomposed into regional allocation and visiting order optimization subproblems. This study proposes a novel clustering algorithm that utilizes centroid iteration and spatiotemporal similarity to allocate regions and adopts the nearest-to-end policy to optimize the visiting order. Additionally, a distance-based bilateral shortest-selection strategy is proposed to generate region-scanning trajectories, which serve as trajectory references for real flight. Simulation results in this study prove the effective performance of the proposed clustering algorithm and region-scanning strategy.