Force, Impedance, and Trajectory Learning for Contact Tooling and Haptic Identification - Archive ouverte HAL
Article Dans Une Revue IEEE Transactions on Robotics Année : 2018

Force, Impedance, and Trajectory Learning for Contact Tooling and Haptic Identification

Résumé

Humans can skilfully use tools and interact with the environment by adapting their movement trajectory, contact force, and impedance. Motivated by the human versatility, we develop here a robot controller that concurrently adapts feedforward force, impedance, and reference trajectory when interacting with an unknown environment. In particular, the robot's reference trajectory is adapted to limit the interaction force and maintain it at a desired level, while feedforward force and impedance adaptation compensates for the interaction with the environment. An analysis of the interaction dynamics using Lyapunov theory yields the conditions for convergence of the closed-loop interaction mediated by this controller. Simulations exhibit adaptive properties similar to human motor adaptation. The implementation of this controller for typical interaction tasks including drilling, cutting, and haptic exploration shows that this controller can outperform conventional controllers in contact tooling.
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Dates et versions

hal-02049408 , version 1 (25-04-2019)

Identifiants

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Yanan Li, Gowrishankar Ganesh, Nathanaël Jarrassé, Sami Haddadin, Alin Albu-Schaeffer, et al.. Force, Impedance, and Trajectory Learning for Contact Tooling and Haptic Identification. IEEE Transactions on Robotics, 2018, 34 (5), pp.1170-1182. ⟨10.1109/tro.2018.2830405⟩. ⟨hal-02049408⟩
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