Autonomous multi-drone racing method based on deep reinforcement learning  

作　　者：Yu KANG Jian DI Ming LI Yunbo ZHAO Yuhui WANG 

机构地区：[1]Department of Automation,University of Science and Technology of China,Hefei 230026,China [2]Institute of Advanced Technology,University of Science and Technology of China,Hefei 230088,China [3]Institute of Artificial Intelligence,Hefei Comprehensive National Science Center,Hefei 230088,China [4]College of Automation Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China

出　　处：《Science China(Information Sciences)》2024年第8期31-44,共14页中国科学（信息科学）（英文版）

基　　金：supported in part by National Key Research and Development Program of China (Grant No. 2018AAA0100801);National Natural Science Foundation of China (Grant No. 62033012)

摘　　要：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 requires quick and agile 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 required global information about tracks and track boundary constraints, the proposed method requires only limited localized track information within the range of its own onboard 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 a 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 a series of simulations and real-world experiments.

关 键 词：racing drone autonomous multi-drone racing sim-to-real deep reinforcement learning Markov game 

分 类 号：V279[航空宇航科学与技术—飞行器设计] TP18[自动化与计算机技术—控制理论与控制工程]