端到端制御ネットワークによる全方向性マイクロ航空機の飛行学習
Learning to Fly Omnidirectional Micro Aerial Vehicles with an End-To-End Control Network
過剰駆動型チルトロータドローンの姿勢制御を強化学習で端到端に学習し、従来のモデルベース制御より外乱に強いことを示した。
著者: Eugenio Cuniato, Olov Andersson, Helen Oleynikova, Roland Siegwart, Michael Pantic
分類: cs.RO
原文アブストラクト
Overactuated tilt-rotor platforms offer many advantages over traditional fixed-arm drones, allowing the decoupling of the applied force from the attitude of the robot. This expands their application areas to aerial interaction and manipulation, and allows them to overcome disturbances such as from ground or wall effects by exploiting the additional degrees of freedom available to their controllers. However, the overactuation also complicates the control problem, especially if the motors that tilt the arms have slower dynamics than those spinning the propellers. Instead of building a complex model-based controller that takes all of these subtleties into account, we attempt to learn an end-to-end pose controller using reinforcement learning, and show its superior behavior in the presence of inertial and force disturbances compared to a state-of-the-art traditional controller.
