ガウス過程回帰による固有感覚型ロボット衝突検出
Proprioceptive Robot Collision Detection through Gaussian Process Regression
電流と関節角度のみからガウス過程回帰でロボットの衝突を検出する手法を提案し、UR10で有効性を実証した。
著者: Dalla Libera Alberto, Tosello Elisa, Pillonetto Gianluigi, Ghidoni Stefano, Carli Ruggero
分類: cs.RO
原文アブストラクト
This paper proposes a proprioceptive collision detection algorithm based on Gaussian Regression. Compared to sensor-based collision detection and other proprioceptive algorithms, the proposed approach has minimal sensing requirements, since only the currents and the joint configurations are needed. The algorithm extends the standard Gaussian Process models adopted in learning the robot inverse dynamics, using a more rich set of input locations and an ad-hoc kernel structure to model the complex and non-linear behaviors due to frictions in quasi-static configurations. Tests performed on a Universal Robots UR10 show the effectiveness of the proposed algorithm to detect when a collision has occurred.