基于激光雷达的深度强化学习实现无人机群通信自由的集体导航
Communication-Free Collective Navigation for a Swarm of UAVs via LiDAR-Based Deep Reinforcement Learning
AI总结：
本文提出一种基于激光雷达的深度强化学习方法，使无人机群在无通信和无外部定位的情况下实现稳健的集体导航。
摘要：
本文提出了一种基于深度强化学习（DRL）的控制器，用于在通信受限环境中实现无人机群的集体导航，使无人机群能够在复杂、障碍物丰富的环境中实现稳健运行。受生物群体中知情个体指导群体而无需显式通信的启发，我们采用了一种隐式主从框架。在此范式中，只有领导者拥有目标信息，而跟随无人机仅使用机载激光雷达感知学习稳健的策略，无需任何 agent 间的通信或领导者识别。我们的系统利用激光雷达点聚类和扩展卡尔曼滤波实现稳定的邻居跟踪，提供可靠的感知，不依赖外部定位系统。我们的方法核心是一种 DRL 控制器，该控制器在 GPU 加速的 Nvidia Isaac Sim 中训练，使跟随者仅使用局部感知学习复杂涌现行为——平衡聚集和障碍物避让。这使无人机群能够隐式跟随领导者，同时稳健地应对感知挑战，如遮挡和有限视野。我们的方法的鲁棒性和仿真到现实的迁移性通过广泛的仿真和具有挑战性的现实世界实验得到验证，使用五架无人机的群体成功展示了在多样化的室内和室外环境中实现集体导航，而无需任何通信或外部定位。
英文摘要：
This paper presents a deep reinforcement learning (DRL) based controller for collective navigation of unmanned aerial vehicle (UAV) swarms in communication-denied environments, enabling robust operation in complex, obstacle-rich environments. Inspired by biological swarms where informed individuals guide groups without explicit communication, we employ an implicit leader-follower framework. In this paradigm, only the leader possesses goal information, while follower UAVs learn robust policies using only onboard LiDAR sensing, without requiring any inter-agent communication or leader identification. Our system utilizes LiDAR point clustering and an extended Kalman filter for stable neighbor tracking, providing reliable perception independent of external positioning systems. The core of our approach is a DRL controller, trained in GPU-accelerated Nvidia Isaac Sim, that enables followers to learn complex emergent behaviors - balancing flocking and obstacle avoidance - using only local perception. This allows the swarm to implicitly follow the leader while robustly addressing perceptual challenges such as occlusion and limited field-of-view. The robustness and sim-to-real transfer of our approach are confirmed through extensive simulations and challenging real-world experiments with a swarm of five UAVs, which successfully demonstrated collective navigation across diverse indoor and outdoor environments without any communication or external localization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
