パッシブ境界を用いたモデルベース進化最適化による物理的人間-ロボット相互作用
A hybrid model-based evolutionary optimization with passive boundaries for physical human-robot interaction
手首リハビリ装置の見かけのインピーダンスを調整するため、パッシブ性で境界を定義したモデルベース進化最適化を提案し、3被験者でカスタマイズされた安定なインピーダンス制御を実現した。
著者: Gustavo J. G. Lahr, Henrique B. Garcia, Arash Ajoudani, Thiago Boaventura, Glauco A. P. Caurin
分類: cs.RO
原文アブストラクト
The field of physical human-robot interaction has dramatically evolved in the last decades. As a result, the robotic system's requirements have become more challenging, including personalized behavior for different tasks and users. Various machine learning techniques have been proposed to give the robot such adaptability features. This paper proposes a model-based evolutionary optimization algorithm to tune the apparent impedance of a wrist rehabilitation device. We used passivity to define boundaries for the possible controller outcomes, limiting the shared autonomy of the robot and ensuring the coupled system stability. The experiment consists of a hardware-in-the-loop optimization and a one-degree-of-freedom robot used for wrist rehabilitation. Experimental results showed that the proposed technique could generate customized passive impedance controllers for three subjects. Furthermore, when compared with a constant impedance controller, the method suggested decreased in 20\% the root mean square of interaction torques while maintaining stability during optimization.
