双方向推力を用いたクアッドローターの強化学習による敏捷な転倒復帰
Agile Fall Recovery for Quadrotors with Bidirectional Thrust via Reinforcement Learning
クアッドローターが任意の姿勢で地面に接地した状態から、軽量なオンボードセンサのみを用いて安定ホバリングまで自律復帰する強化学習フレームワークを提案した。非対称アクター・クリティックとINDI制御を組み合わせ、シミュレーションと実機実験でロバスト性を実証した。
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著者: Anke Zhao, Yuhang Zhong, Kenghou Hoi, Junyu Mou, Junjie Wang, Lijie Wang, Jialiang Hou, Fei Gao
分類: cs.RO
原文アブストラクト
Autonomous fall recovery is a critical capability for quadrotors operating in real-world environments, where collisions or failures may leave the vehicle resting on the ground in an arbitrary attitude. This problem is challenging because recovery must be achieved under limited onboard sensing, in constrained free space, with ground contact, and in the presence of unknown disturbances. In this letter, we present an RL-based framework for autonomous fall recovery of a quadrotor from arbitrary ground attitudes to stable hover using only lightweight onboard sensors. To address severe partial observability and intermittent sensor invalidity, we train a recurrent policy within an asymmetric actor--critic architecture, leveraging an Incremental Nonlinear Dynamic Inversion (INDI) controller to track the policy output. Combined with high-fidelity simulations of motor response and optical flow, the overall training framework significantly reduces the sim-to-real gap. Simulation ablation studies validate the importance of the main design choices, while real-world experiments demonstrate zero-shot transfer and robust recovery under different initial attitudes, wind disturbances, and additional payloads. These results demonstrate that agile quadrotor fall recovery can be achieved without explicit state estimation using only limited and unreliable onboard sensing.
