与邸健博士合作的、题为 “Autonomous Multi-Drone Racing Method Based on Deep Reinforcement Learning” 的论文已被《SCIENCE CHINA Information Sciences》接受发表。该论文摘要如下：

Racing drones have attracted increasing attention due to their remarkable high speed and excellent maneuverability. However, Autonomous multi-drone racing is quite difficult since it calls for quick and dexterous drone flight in intricate surroundings and rich drone interaction. To address these issues, we propose a novel autonomous multi-drone racing method based on deep reinforcement learning. A new set of reward functions is proposed to make racing drones learn the racing skills of human experts. Unlike previous methods that require global information about track and track boundary constraints, the proposed method requires only limited localized track information within the range of its own on-board sensors. Further, the dynamic response characteristics of racing drones are incorporated into the training environment, so that the proposed method is more in line with the requirements of real drone racing scenarios. In addition, our method has low computational cost and can meet the requirements of real-time racing. Finally, the effectiveness and superiority of the proposed method are verified by extensive comparison with the state-of-the-art methods in simulation and in the physical world.