Grip Control Using Biomimetic Tactile Sensing Systems

We present a proof-of-concept for controlling the grasp of an anthropomorphic mechatronic prosthetic hand by using a biomimetic tactile sensor, Bayesian inference, and simple algorithms for estimation and control. The sensor takes advantage of its compliant mechanics to provide a triaxial force sens...

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Bibliographic Details
Published in:	IEEE/ASME transactions on mechatronics Vol. 14; no. 6; pp. 718 - 723
Main Authors:	Wettels, N., Parnandi, A.R., Ji-Hyun Moon, Loeb, G.E., Sukhatme, G.S.
Format:	Journal Article
Language:	English
Published:	New York IEEE 01-12-2009 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Anthropomorphism Bayesian methods Biomimetic Biomimetics Biosensors Control systems Detection dexterous manipulators Force sensors grip control Inference algorithms Mathematical analysis Mechatronics On-line systems Prosthetic hand Prosthetics Sensors Tactile tactile sensor Tactile sensors
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Summary:	We present a proof-of-concept for controlling the grasp of an anthropomorphic mechatronic prosthetic hand by using a biomimetic tactile sensor, Bayesian inference, and simple algorithms for estimation and control. The sensor takes advantage of its compliant mechanics to provide a triaxial force sensing end-effector for grasp control. By calculating normal and shear forces at the fingertip, the prosthetic hand is able to maintain perturbed objects within the force cone to prevent slip. A Kalman filter is used as a noise-robust method to calculate tangential forces. Biologically inspired algorithms and heuristics are presented that can be implemented online to support rapid, reflexive adjustments of grip.
Bibliography:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23
ISSN:	1083-4435 1941-014X
DOI:	10.1109/TMECH.2009.2032686