Reinforcement-Learning-Assisted Multi-UAV Task Allocation and Path Planning for IIoT
Guodong Zhao, Ye Wang, Tong Mu, Zhijun Meng, Zichen Wang
Abstract
Exploring the widespread applications of unmanned aerial vehicles (UAVs) in Internet of Things has become a current research hotspot. In some tasks related to UAV-based environmental monitoring and transportation, the simultaneous consideration of UAV task allocation and path planning constitutes a category of joint optimization problems. This paper focuses on a warehouse cargo inspection scenario with multiple heterogeneous UAVs. In such scenarios, existing heuristic path finding algorithms that consider task allocation cannot make a good balance between solution time and solution quality. Therefore, in this paper, we propose a reinforcement learning assisted task allocation and conflict-free path framework to achieve better task allocation and path finding results. The framework uses a multiple traveling salesman transformation algorithm for task allocation and a multi-agent reinforcement learning (MARL) algorithm for conflict-free path finding. The path finding policy can be extended to larger-scale environments with more UAVs. We conduct the training of the path planning module and the verification of the overall framework in random environments. Simulation results show that our reinforcement learning assisted framework has a significant advantage over the existing algorithms in terms of solution time, solution quality and scalability.