基于深度强化学习的去中心化端到端多无人艇追捕
Decentralized End-to-End Multi-AAV Pursuit Using Predictive Spatio-Temporal Observation via Deep Reinforcement Learning
AI总结：
本文提出一种去中心化端到端多智能体强化学习框架，通过预测时空观测实现自主空中群体在复杂环境中的追捕，提升捕获效率和协同成功率。
摘要：
在复杂环境中实现去中心化协作追捕对自主空中群体而言具有挑战性，特别是在感知不完全和噪声存在的情况下。现有方法通常依赖于抽象几何特征或特权地面真实状态，从而回避了现实环境中的感知不确定性。本文提出了一种去中心化端到端多智能体强化学习（MARL）框架，直接将原始激光雷达观测映射到连续控制命令。框架的核心是预测时空观测（PSTO），一种以自我为中心的网格表示，将障碍物几何与预测对抗意图和队友运动统一在一个固定分辨率投影中。基于PSTO，单一去中心化策略使智能体能够导航静态障碍物、拦截动态目标并维持协同包围。仿真显示，所提方法在捕获效率和成功率方面优于依赖特权障碍信息的现有学习方法。此外，统一策略可无缝扩展到不同团队规模而无需重新训练。最后，完全自主的户外实验验证了该框架在仅依赖机载传感和计算的四旋翼群体上的有效性。
英文摘要：
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.
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。
