UAVs are widely used in civilian applications, especially in the era of the low-altitude economy. These applications often in-volve multiple rounds and long durations. However, small or medium UAVs generally have limited endurance. Compared to recharge after returning to their home base, cooperating with mobile ground-based energy-support UGVs may improve efficien-cy. This cooperation, however, imposes higher demands on tackling task allocation problems. To address this issue, this paper considers the endurance constraints of UAVs and employs a dynamic environmental modeling method to describe the usage of multiple rounds. Specifically, a grid-based dynamic graph model is designed to update waypoint deployment between each round. Aerial-ground heterogeneous platforms plan routes between waypoints while accounting for no-fly and no-entry zones. Through this, an energy cost matrix is constructed. Additionally, a hierarchical fast-solving method for multiple-round task allocation is developed. It can be divided into two different level. At the first level, a clustering algorithm is used to achieve uniform parti-tioning of way-points into subregions. At the second level, an improved multi-objective genetic algorithm is employed to allo-cate tasks in two scenarios: within the subregions for UAVs and between the subregions for UGVs. Finally, the study analyzes the parameters affecting task duration and take-off/landing cycles. Simulations and experiments validate the efficiency of this heterogeneous system for long-duration and large-area missions. This paper introduces the concepts of endurance constraints and redeployment into heterogeneous aerial-ground task allocation and provides a comprehensive framework with real-time applica-bility. These methods offer a valuable reference for the deployment of autonomous missions.
[1] LEE H W , LEE C S. Research on logistics of intelli-gent unmanned aerial vehicle integration system[J]. Journal of Industrial Information Integration, 2023, 36: 100534.
[2] CHEN X, WU Y, XU S. Mission Planning of UAVs and UGV for Building Inspection in Rural Area[J]. Algo-rithms, 2024, 17: 177.
[3] CHEN X L, LI G, Mehmood, M.S et al. Integration and differentiation: comparison of photography behav-iors using unmanned aerial vehicle data in China and Europe[J]. Humanit Soc Sci Commun, 2023, 10: 683.
[4] MAINI P, SUJIT P B. On cooperation between a fuel constrained UAV and a refueling UGV for large scale mapping applications[C]//2015 international conference on unmanned aircraft systems (ICUAS). IEEE. 2015: 1370-1377.
[5] ZHAO N, LU W D, SHENG M, et al. UAV-assisted emergency networks in disasters[J]. IEEE Wireless Communications, 2019, 26: 45-51.
[6] 孙冰, 王川, 杨强,等. 面向多起点均衡多旅行商问题的进化算法[J]. 计算机工程与设计, 2023, 44(07): 2030-2038.
SUN B, WANG C, YANG Q, et al. Improved evolution-ary algorithm for balanced multiple traveling salesmen problem with multiple starting points[J] Computer En-gineering and Design, 2023, 44(07): 2030-2038(in Chi-nese).
[7] 张曼, 王桢. 基于任务拆分的多无人机任务分配多目标优化[J]. 火力与指挥控制, 2024, 49(02): 69-74+81.
ZHANG M, WANG Z. Multi-objective Optimization of Multi-UAV Task Assignment Based on Task Splitting [J]. Fire Control & Command Control, 2024, 49(02): 69-74+81(in Chinese).
[8] 张安, 杨咪, 毕文豪, 等. 基于多策略GWO算法的不确定环境下异构多无人机任务分配[J]. 航空学报, 2023, 44(8): 327115.
ZHANG A, YANG M, BI W H, et al. Task allocation of heterogeneous multi-UAVs in uncertain environment based on multi-strategy integrated GWO[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(8): 327115 (in Chinese).
[9] YU K, BUDHIRAJA A K, BUEBEL S, et al. Algo-rithms and experiments on routing of unmanned aerial vehicles with mobile recharging stations[J]. Journal of Field Robotics, 2019, 36(3): 602-616.
[10] DODGE M, MIRHASSANI S A, HOOSHMAND F. A modeling and solution approach for wind-affected drone-truck routing problem under uncertainty[J]. Ex-pert Systems with Applications. 2024, 257: 124996.
[11] 陈志旺, 夏顺, 李建雄, 等. 考虑分配次序的无人机协同目标分配建模与遗传算法求解[J]. 控制理论与应用, 2019.36(7):1072-1082.
CHEN Z W, XIA S, LI J X, et al. Modeling of un-manned aerial vehicles cooperative target assignment with allocation order and its solving of genetic algo-rithm[J]. Control Theory & Applications, 2019, 36(7):1072-1082(in Chinese).
[12] 王然然, 魏文领, 杨铭超, 等. 考虑协同航路规划的多无人机任务分配[J]. 航空学报, 2020, 41(S2): 724234.
WANG R R, WEI W L, YANG M C, et al. Task alloca-tion of multiple UAVs considering cooperative route planning[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(S2): 724234(in Chinese).
[13] ZHAO M, LI D. Collaborative task allocation of heter-ogeneous multi-unmanned platform based on a hybrid improved contract net algorithm[J]. IEEE Access, 2021, 9: 78936-78946.
[14] MA Z Y, XIONG J, GONG H J, et al. Adaptive Depth Graph Neural Network-based Dynamic Task Allocation for UAV-UGVs Under Complex Environments[J]. IEEE Transactions on Intelligent Vehicles, 2024, 14(8): 1-14.
[15] 程亚南, 王晓峰, 刘凇佐,等. 求解多起点多旅行商问题的K-means聚类信息传播算法[J]. 科学技术与工程, 2022, 22(23): 10146-10154.
CHENG YN, WANG X F, LIU S Z. K-means Cluster-ing Information Propagation Algorithm for Multi-ple Depots Multiple Traveling Salesman Problem[J]. Science Technology and Engineering, 2022, 22(23): 10146-10154(in Chinese).
[16] NI J, TANG M, CHEN Y, et al. An Improved Coopera-tive Control Method for Hybrid Unmanned Aerial‐Ground System in Multitasks[J]. International Journal of Aerospace Engineering, 2020, 2020(1): 9429108.
[17] XIA Y, CHEN C, LIU Y, et al. Two-layer path planning for multi-area coverage by a cooperative ground vehicle and drone system[J]. Expert Systems with Applications, 2023, 217: 119604.
[18] ROPERO F, MUNOZ P, R-MORENO M D. TERRA: A path planning algorithm for cooperative UGV–UAV ex-ploration[J]. Engineering Applications of Artificial Intel-ligence, 2019, 78: 260-272.
[19] 赵晓林, 魏兆恬, 赵博欣, 等. 异构资源类型下多无人机任务分配[J]. 国防科技大学学报, 2023, 45(04): 232-242.
ZHAO X L, WEI Z T, ZHAO B X, et al. Task allocation of multiple UAVs under heterogeneous resource types[J]. Journal of National University of Defense Technology, 2023, 45(04): 232-242(in Chinese).
[20] 张瑞鹏, 冯彦翔, 杨宜康. 多无人机协同任务分配混合粒子群算法[J]. 航空学报, 2022, 43(12): 326011.
ZHANG R P, FENG Y X, YANG Y K. Hybrid particle swarm algorithm for multi-UAV cooperative task alloca-tion[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(12): 326011(in Chinese).
[21] 王峰, 付青坡, 韩孟臣, 等. LeCMPSO算法求解异构无人机协同多任务重分配问题[J]. 控制理论与应用, 2024, 41(06): 1009-1017.
WANG F, FU Q P, HAN M C. Learning-guided coevo-lution multi-objective particle swarm optimization for heterogeneous UAV cooperative multi-task reallocation problem[J]. Control Theory & Applications, 2024, 41(06): 1009-1017(in Chinese).
[22] 马秀娟, 武帅, 蔡春伟, 等. 应用于无人机的无线充电技术研究[J]. 电机与控制学报, 2019, 23(08): 1-9.
MA X J, WU S, CAI C W, et al. Research on wireless charging technology applied to UAVs[J]. Electric Ma-chines and Control, 2019, 23(08): 1-9(in Chinese).
CJA