AGCo-MATA: Air-Ground Collaborative Multi-Agent Task Allocation in Mobile Crowdsensing

The rapid advancement of intelligent unmanned systems has brought new opportunities to mobile crowd sensing (MCS). Compared with traditional homogeneous frameworks, heterogeneous air-ground collaborative multi-agent frameworks consisting of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) exhibit superior flexibility and efficiency in complex sensing tasks. Task allocation among agents is crucial for improving overall MCS quality. To achieve efficient task allocation for heterogeneous collaborative agents, this study investigated two typical complex multi-agent task allocation scenarios with dual optimization objectives: (1) For the Air-Ground Few-Agents-More-Tasks (AG-FAMT) scenario, the objectives are to maximize task completion and minimize total travel distance; (2) For the Air-Ground More-Agents-Few-Tasks (AG-MAFT) scenario (task allocation based on agent locations), the objectives are to minimize total travel distance and travel time cost. Overall, in this paper, we proposed two algorithms: a multi-task minimum cost maximum flow optimization algorithm called Multi-Task Minimum-Cost Maximum-Flow (MT-MCMF) tailored for AG-FAMT, and a multi-objective optimization algorithm called Weighted Integer Linear Programming (W-ILP) for AG-MAFT (with a focus on optimizing UAV charging path planning). Experiments on a large-scale real-world dataset demonstrated that both proposed algorithms outperform baseline methods under varying experimental settings (task quantity, difficulty, and distribution), providing a novel approach to enhance the overall quality of air-ground MCS tasks.