To address the challenges faced by heterogeneous Unmanned Aerial Vehicle (UAV) systems in complex task allocation, including over-reliance on centralized scheduling, training deadlock, inadequate capture of temporal collaboration, and unstable training under sparse reward conditions, this paper proposes a distributed task allocation algorithm based on reinforcement learning. The algorithm adopts a decentralized decision-making architecture, which enables the autonomous formation of UAV collaborative groups without the need for a global scheduling center. A cascaded submission timeout mechanism is introduced to prevent training deadlock; the combination of Long Short-Term Memory (LSTM) and attention mechanism is employed to accurately model temporal correlations and collaborative dependencies; and the Proximal Policy Optimization (PPO) algorithm is leveraged to optimize the training stability under sparse reward conditions. Experimental results demonstrate that the proposed algorithm achieves a 100% task success rate in scenarios of different scales, and its key metrics, including makespan, time cost and waiting time, are significantly superior to those of mainstream baseline methods such as the Genetic Algorithm (GA) and the Hungarian Algorithm (HA). Moreover, the algorithm still maintains excellent robustness under the conditions of UAV failures, parameter variations, and dynamic task perturbations. This method supports zero-shot generalization for any number of UAVs and tasks and provides an efficient and reliable solution for the real-time collaborative scheduling of heterogeneous UAV systems.
Sun et al. (Fri,) studied this question.