空 地 异 构 协 同 轮 值 任 务 分 配 方 法

UAVs are widely used in civilian applications，especially in the era of the low-altitude economy. These ap- plications often involve multi-rounds and long durations. However，small or medium UAVs generally have limited en- durance. Compared to recharge after returning to their home base，cooperation with mobile ground-based energy-support UGVs may improve efficiency. This cooperation，however，imposes higher demands on task allocation. To address this issue，this paper considers the endurance constraints of UAVs and proposes a dynamic environmental modeling method to describe the usage of multi-round. Specifically，a grid-based dynamic graph model is designed to update waypoint deployment between each round. Aerial-ground heterogeneous platforms plan routes between way- points while accounting for no-fly and no-entry zones. Through this，an energy cost matrix is constructed. Additionally，a hierarchical fast-solving method for multi-round task allocation is developed，which can be divided into two different levels. At the first level，a clustering algorithm is used to achieve uniform partitioning of way-points into subregions. At the second level，an improved multi-objective genetic algorithm is developed to allocate tasks in two scenarios：within the subregions for UAVs and between the subregions for UGVs. Finally，the parameters affecting task duration and take-off/landing cycles are analyzed. Simulations and experiments validate the efficiency of this heterogeneous system in long-duration and large-area missions. This paper uses the concepts of endurance constraints and redeployment for heterogeneous aerial-ground task allocation and provides a comprehensive framework with real-time applicability，of- fering a valuable reference for the deployment of autonomous missions.