  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.
