使用强化学习在受管制空域拦截未经授权的空中机器人
Intercepting Unauthorized Aerial Robots in Controlled Airspace Using Reinforcement Learning
AI总结：
针对受管制空域中未经授权或不合作的 UAV 威胁，本文采用 DreamerV3、TQC 和 SAC 训练固定翼追击者智能体，并在高保真仿真及多种逃逸、扰动场景中验证其自主拦截能力。
摘要：
无人驾驶航空器（UAV）在受管制空域的扩散带来了重大风险，包括潜在碰撞、空中交通中断以及安全威胁。确保空域安全、高效运行，尤其是在城市环境和关键基础设施附近，需要有有效的方法来拦截未经授权或不合作的 UAV。本工作通过使用强化学习（RL），满足了对能够管理此类威胁的鲁棒、自适应系统的关键需求。我们提出了一种利用 RL 训练固定翼 UAV 追击者智能体以拦截动态逃逸目标的新方法。我们的方法研究了基于模型和无模型的 RL 算法，具体包括 DreamerV3、截断分位数评论家（Truncated Quantile Critics，TQC）和软演员-评论家（Soft Actor-Critic，SAC）。这些算法的训练和评估在多种场景下进行，包括未见过的逃逸策略和环境扰动。我们的方法利用高保真飞行动力学仿真来创建逼真的训练环境。本研究强调了为 UAV 拦截开发智能、自适应控制系统的重要性，为推进安全、高效的空域管理作出了重要贡献。它证明了 RL 在训练能够自主完成这些关键任务的系统方面的潜力。
英文摘要：
The proliferation of unmanned aerial vehicles (UAVs) in controlled airspace presents significant risks, including potential collisions, disruptions to air traffic, and security threats. Ensuring the safe and efficient operation of airspace, particularly in urban environments and near critical infrastructure, necessitates effective methods to intercept unauthorized or non-cooperative UAVs. This work addresses the critical need for robust, adaptive systems capable of managing such threats through the use of Reinforcement Learning (RL). We present a novel approach utilizing RL to train fixed-wing UAV pursuer agents for intercepting dynamic evader targets. Our methodology explores both model-based and model-free RL algorithms, specifically DreamerV3, Truncated Quantile Critics (TQC), and Soft Actor-Critic (SAC). The training and evaluation of these algorithms were conducted under diverse scenarios, including unseen evasion strategies and environmental perturbations. Our approach leverages high-fidelity flight dynamics simulations to create realistic training environments. This research underscores the importance of developing intelligent, adaptive control systems for UAV interception, significantly contributing to the advancement of secure and efficient airspace management. It demonstrates the potential of RL to train systems capable of autonomously achieving these critical tasks.
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。
