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Journal Articles IEEE Robotics and Automation Letters Year : 2021

A General Visual-Impedance Framework for Effectively Combining Vision and Force Sensing in Feature Space

Abstract

Robotic systems are increasingly used to work in dynamic and/or unstructured environments and to operate with a high degree of safety and autonomy. Consequently, they are often equipped with external sensors capable of perceiving the environment (e.g. cameras) and the contacts that may arise (e.g. force/torque sensors). This paper proposes a general framework for combining force and visual information in the visual feature space. By leveraging recent results on the derivation of visual servo dynamics, we generalize the treatment regardless of the visual features chosen. Vision and force sensing are coupled in the feature space, avoiding both the convergence to a local minimum and the arising of inconsistencies at the actuation level. Any task space direction is simultaneously controlled by both vision and force. Compliance against interaction forces is achieved in feature space along the features defining the visual task. Experiments on a real platform are carried out to evaluate the effectiveness of the proposed framework.
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Dates and versions

hal-03180717 , version 1 (25-03-2021)

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Alexander Oliva, Paolo Robuffo Giordano, François Chaumette. A General Visual-Impedance Framework for Effectively Combining Vision and Force Sensing in Feature Space. IEEE Robotics and Automation Letters, 2021, 6 (3), pp.4441-4448. ⟨10.1109/LRA.2021.3068911⟩. ⟨hal-03180717⟩
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