デュアルエージェント強化学習による無人航空機の軌道追従
Dual Agent Learning Based Aerial Trajectory Tracking
3D点群を入力に、軌道追従用と衝突回避用の2つの強化学習エージェントを組み合わせ、障害物環境でのUAV軌道追従をリアルタイムに実現する手法を提案。
著者: Shaswat Garg, Houman Masnavi, Baris Fidan, Farrokh Janabi-Sharifi
分類: cs.RO
原文アブストラクト
This paper presents a novel reinforcement learning framework for trajectory tracking of unmanned aerial vehicles in cluttered environments using a dual-agent architecture. Traditional optimization methods for trajectory tracking face significant computational challenges and lack robustness in dynamic environments. Our approach employs deep reinforcement learning (RL) to overcome these limitations, leveraging 3D pointcloud data to perceive the environment without relying on memory-intensive obstacle representations like occupancy grids. The proposed system features two RL agents: one for predicting UAV velocities to follow a reference trajectory and another for managing collision avoidance in the presence of obstacles. This architecture ensures real-time performance and adaptability to uncertainties. We demonstrate the efficacy of our approach through simulated and real-world experiments, highlighting improvements over state-of-the-art RL and optimization-based methods. Additionally, a curriculum learning paradigm is employed to scale the algorithms to more complex environments, ensuring robust trajectory tracking and obstacle avoidance in both static and dynamic scenarios.
