Multi-UAV Urban Logistics Task Allocation Method Based on MCTS
Zeyuan Ma, Jing Chen
Abstract
Unmanned aerial vehicles (UAVs) open new methods for efficient and rapid transportation in urban logistics distribution, where task allocation is a significant issue. In urban logistics systems, the energy status of UAVs is a critical factor in ensuring mission fulfillment. While extensive literature addresses the energy consumption of UAVs during tasks, the feasibility of energy replenishment must be addressed, which introduces additional uncertainty to the task allocation. This paper realizes multi-tasking, considering the energy consumption and replenishment of UAVs, to ensure that the tasks can be accomplished while reducing energy consumption. This paper proposes uniform distribution K-means to realize balanced multi-task grouping. Based on the Monte Carlo tree search (MCTS), a task-allocation-oriented MCTS method is proposed, including improving the selection and simulation process of MCTS. The aim was to collaborate with multiple trees for node selection and record historical simulation information to guide subsequent simulations for better results. Finally, the optimality of the proposed method was validated by comparing it with other relevant MCTS methods through several randomized experiments.