mlsanityData to 7 Oct 2026

RaCIL: Ray Tracing based Multi-UAV Obstacle Avoidance through Composite Imitation Learning

arXiv 2407.02520 ↗First posted 24 Jun 2024cs.RO cs.AI cs.LGcited by 0

On the map: Offline RL and imitation learning from data → Imitation learning methods

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 9 of its 26 references that are in the map. First posted on arXiv 24 Jun 2024.

Builds on

9 of 26 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024A Survey of Imitation Learning Methods, Environments and Metrics30 Apr
  2. 2024Multi-Agent Generative Adversarial Interactive Self-Imitation Learning for AUV Formation Control and Obstacle Avoidance21 Jan

Other references · 7

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2022Generative Adversarial Networkscited by 5,603
  3. 2014Going Deeper with Convolutionscited by 4,231
  4. 2016Generative Adversarial Imitation Learningcited by 1,576
  5. 2017Learning Robust Rewards with Adversarial Inverse Reinforcement Learningcited by 485
  6. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progresscited by 294
  7. 2019Randomized Adversarial Imitation Learning for Autonomous Drivingcited by 6

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Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024Synergistic Reinforcement and Imitation Learning for Vision-driven Autonomous Flight of UAV Along River
  2. 2020Deep Reinforcement Learning based Local Planner for UAV Obstacle Avoidance using Demonstration Data
  3. 2024Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight
  4. 2025RAPID: Robust and Agile Planner Using Inverse Reinforcement Learning for Vision-Based Drone Navigation
  5. 2024CGD: Constraint-Guided Diffusion Policies for UAV Trajectory Planning
  6. 2024Developing Path Planning with Behavioral Cloning and Proximal Policy Optimization for Path-Tracking and Static Obstacle Nudging
  7. 2026An End-to-end Flight Control Network for High-speed UAV Obstacle Avoidance based on Event-Depth Fusion
  8. 2019Sample Efficient Learning of Path Following and Obstacle Avoidance Behavior for Quadrotors ↗