Run-time Self-classification of External Force using Virtual Point Mass Approximation for Object Manipulation

Authors
Bae, Ji-HunKim, Doik
Issue Date
2014-02
Publisher
INST CONTROL ROBOTICS & SYSTEMS, KOREAN INST ELECTRICAL ENGINEERS
Citation
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, v.12, no.1, pp.137 - 146
Abstract
Many tasks assigned to a manipulator include interactions with its operating environment or manipulating objects, which are detected as forces and moments by the force sensor. It is, however, not easy to detect when a pure external wrench occurred in interactions or manipulations since signals measured by the force sensor consist of the inertial effect of the end-effector and manipulating objects as well as the effect of interactions. In order to separate these combined effects, a self-classification method for the 6-axis force sensor is proposed in this paper by relating the wrench and the virtual point mass. With the proposed method, wrenches due to the end-effector and objects can be classified in run-time without any prior information for them, and thus a pure external wrench can also be distinguished from them. The effectiveness of the proposed self-classification method is verified through experiments.
Keywords
SENSOR FUSION; SENSOR FUSION; Classification of external force; force sensor; object manipulation; virtual point mass; wrench
ISSN
1598-6446
URI
https://pubs.kist.re.kr/handle/201004/127141
DOI
10.1007/s12555-012-0538-7
Appears in Collections:
KIST Article > 2014
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