RaCIL：基于射线追踪的多无人机通过复合模仿学习实现避障
RaCIL: Ray Tracing based Multi-UAV Obstacle Avoidance through Composite Imitation Learning
AI总结：
本文提出RaCIL框架，将PPO、BC、GAIL与射线追踪结合，用于多无人机避障，并在两架和四架无人机场景中验证了其避障与协调飞行能力的提升。
摘要：
在本研究中，我们通过一种创新的复合模仿学习方法应对无人机（UAV）的避障挑战，该方法将近端策略优化（PPO）与行为克隆（BC）和生成对抗模仿学习（GAIL）相结合，并通过集成射线追踪技术加以增强。我们的研究强调了射线追踪在提升障碍物检测与规避能力方面的重要作用。此外，我们证明了在协调两架无人机的飞行路径中引入GAIL的有效性，展示了改进的碰撞避免能力。扩展我们的方法，我们将结合PPO、BC、GAIL和射线追踪的框架应用于涉及四架无人机的场景，说明了其可扩展性和对更复杂场景的适应性。研究结果表明，我们的方法不仅提高了基于基础PPO的避障可靠性，还为拥挤或动态环境中先进的自主无人机运行铺平了道路。
英文摘要：
In this study, we address the challenge of obstacle avoidance for Unmanned Aerial Vehicles (UAVs) through an innovative composite imitation learning approach that combines Proximal Policy Optimization (PPO) with Behavior Cloning (BC) and Generative Adversarial Imitation Learning (GAIL), enriched by the integration of ray-tracing techniques. Our research underscores the significant role of ray-tracing in enhancing obstacle detection and avoidance capabilities. Moreover, we demonstrate the effectiveness of incorporating GAIL in coordinating the flight paths of two UAVs, showcasing improved collision avoidance capabilities. Extending our methodology, we apply our combined PPO, BC, GAIL, and ray-tracing framework to scenarios involving four UAVs, illustrating its scalability and adaptability to more complex scenarios. The findings indicate that our approach not only improves the reliability of basic PPO based obstacle avoidance but also paves the way for advanced autonomous UAV operations in crowded or dynamic environments.
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。
