Decentralized cooperative pursuit in cluttered environments is challenging for autonomous aerial swarms, especially under partial and noisy perception. Existing methods often rely on abstracted geometric features or privileged ground-truth states, and therefore sidestep perceptual uncertainty in real-world settings. We propose a decentralized end-to-end multi-agent reinforcement learning (MARL) framework that maps raw LiDAR observations directly to continuous control commands. Central to the framework is the Predictive Spatio-Temporal Observation (PSTO), an egocentric grid representation that aligns obstacle geometry with predictive adversarial intent and teammate motion in a unified, fixed-resolution projection. Built on PSTO, a single decentralized policy enables agents to navigate static obstacles, intercept dynamic targets, and maintain cooperative encirclement. Simulations demonstrate that the proposed method achieves superior capture efficiency and competitive success rates compared to state-of-the-art learning-based approaches relying on privileged obstacle information. Furthermore, the unified policy scales seamlessly across different team sizes without retraining. Finally, fully autonomous outdoor experiments validate the framework on a quadrotor swarm relying on only onboard sensing and computing.

翻译：在杂乱环境中实现分散式协同追踪对自主空中集群具有挑战性，尤其面临部分观测与噪声感知的复杂条件。现有方法通常依赖于抽象的几何特征或特权真实状态，从而回避了现实场景中的感知不确定性。我们提出一种分散式端到端多智能体强化学习框架，该框架将原始激光雷达观测直接映射为连续控制指令。该框架的核心是预测性时空观测——一种自我中心化栅格表示方法，能够在统一、固定分辨率投影中同步融合障碍物几何特征、预测性对抗意图与队友运动信息。基于PSTO构建的单一分散式策略使智能体能够导航静态障碍、拦截动态目标并维持协同包围。仿真实验表明，与依赖特权障碍物信息的现有最优学习方法相比，本方法在捕获效率与成功率方面均展现出更优性能。此外，该统一策略无需重新训练即可无缝扩展至不同规模的集群中。最终，完全自主的户外实验验证了该框架在仅依赖机载感知与计算的四旋翼集群上的有效性。