Rapid progress in intelligent unmanned systems has presented new
opportunities for mobile crowd sensing (MCS). Today, heterogeneous air-ground
collaborative multi-agent framework, which comprise unmanned aerial vehicles
(UAVs) and unmanned ground vehicles (UGVs), have presented superior flexibility
and efficiency compared to traditional homogeneous frameworks in complex
sensing tasks. Within this context, task allocation among different agents
always play an important role in improving overall MCS quality. In order to
better allocate tasks among heterogeneous collaborative agents, in this paper,
we investigated two representative complex multi-agent task allocation
scenarios with dual optimization objectives: (1) For AG-FAMT (Air-Ground Few
Agents More Tasks) scenario, the objectives are to maximize the task completion
while minimizing the total travel distance; (2) For AG-MAFT (Air-Ground More
Agents Few Tasks) scenario, where the agents are allocated based on their
locations, has the optimization objectives of minimizing the total travel
distance while reducing travel time cost. To achieve this, we proposed a
Multi-Task Minimum Cost Maximum Flow (MT-MCMF) optimization algorithm tailored
for AG-FAMT, along with a multi-objective optimization algorithm called W-ILP
designed for AG-MAFT, with a particular focus on optimizing the charging path
planning of UAVs. Our experiments based on a large-scale real-world dataset
demonstrated that the proposed two algorithms both outperform baseline
approaches under varying experimental settings, including task quantity, task
difficulty, and task distribution, providing a novel way to improve the overall
quality of mobile crowdsensing tasks.

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