Multi-UAV simultaneous target assignment and path planning based on deep reinforcement learning in dynamic multiple obstacles environments - Kong, Xiaoran; Zhou, Yatong; Li, Zhe; Wang, Shaohai (Frontiers in Neurorobotics, 2024)
Target assignment and path planning is crucial for the cooperativity of multiple unmanned aerial vehicles (UAV) system. However, it is a challenge considering the dynamics of environments and partially observability of UAVs. In this paper, the problem of multi-UAV target assignment and path planning is formulated as a partially observable Markov decision process (POMDP), and a novel deep reinforcement learning (DRL)-based algorithm is proposed to address it. Specifically, a target assignment network is introduced into the twin-delayed deep deterministic policy gradient (TD3) algorithm to solve the target assignment problem and path planning problem simultaneously. The target assignment network executes target assignment for each step of UAVs, while the TD3 guides UAVs to plan paths for this step based on the assignment result and provides training labels for the optimization of target assignment network. Experimental results demonstrate that the proposed approach can ensure an optimal completely target allocation and achieves collision-free path for each UAV in three-dimensional (3D) dynamic multiple obstacles environments, and present a superior performance in target completion and a better adaptability to complex environments compared with existing methods.