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.

翻译：本文提出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（UAV）集群的协同导航，使其能够在复杂、障碍物密集的环境中鲁棒运行。受生物集群中知情个体无需显式通信即可引导群体的启发，我们采用了一种隐式领导者-跟随者框架。在此范式中，仅领导者掌握目标信息，而跟随者无人机仅利用机载激光雷达感知学习鲁棒策略，无需任何个体间通信或领导者识别。我们的系统利用激光雷达点云聚类与扩展卡尔曼滤波器实现稳定的邻居跟踪，提供不依赖外部定位系统的可靠感知。方法的核心是一个在GPU加速的Nvidia Isaac Sim中训练的DRL控制器，使跟随者仅通过局部感知即可学习复杂的涌现行为——平衡集群聚集与避障。这使得集群能够隐式跟随领导者，同时鲁棒地应对遮挡与有限视场等感知挑战。我们通过大量仿真及由五架无人机组成的集群在具有挑战性的真实世界实验，验证了所提方法的鲁棒性与仿真到现实的迁移能力。实验成功展示了在多样化的室内外环境中无需任何通信或外部定位的集群协同导航。