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Research Papers

An Optimization Approach to Teleoperation of the Thumb of a Humanoid Robot Hand: Kinematic Mapping and Calibration

[+] Author and Article Information
Lei Cui

Department of Mechanical Engineering,
Curtin University,
Kent Street,
Bentley, Western Australia 6102, Australia
e-mail: lei.cui@curtin.edu.au

Ugo Cupcic

Shadow Robot Company,
251 Liverpool Road,
London N1 1LX, UK
e-mail: ugo@shadowrobot.com

Jian S. Dai

Centre for Robotics Research,
King's College London,
University of London,
Strand, London WC2R 2LS, UK
e-mail: jian.dai@kcl.ac.uk

1Corresponding author.

Contributed by the Mechanisms and Robotics Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received October 26, 2013; final manuscript received May 19, 2014; published online June 13, 2014. Assoc. Editor: Matthew B. Parkinson.

J. Mech. Des 136(9), 091005 (Jun 13, 2014) (7 pages) Paper No: MD-13-1483; doi: 10.1115/1.4027759 History: Received October 26, 2013; Revised May 19, 2014

The complex kinematic structure of a human thumb makes it difficult to capture and control the thumb motions. A further complication is that mapping the fingertip position alone leads to inadequate grasping postures for current robotic hands, many of which are equipped with tactile sensors on the volar side of the fingers. This paper aimed to use a data glove as the input device to teleoperate the thumb of a humanoid robotic hand. An experiment protocol was developed with only minimum hardware involved to compensate for the differences in kinematic structures between a robotic hand and a human hand. A nonlinear constrained-optimization formulation was proposed to map and calibrate the motion of a human thumb to that of a robotic thumb by minimizing the maximum errors (minimax algorithms) of fingertip position while subject to the constraint of the normals of the surfaces of the thumb and the index fingertips within a friction cone. The proposed approach could be extended to other teleoperation applications, where the master and slave devices differ in kinematic structure.

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Figures

Grahic Jump Location
Fig. 4

The thumb model matching the sensor positioning of the CyberGlove II

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Fig. 3

Inadequate precision grasp due to calibrating fingertip position alone

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Fig. 2

The sensor positioning of the CyberGlove II

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Fig. 1

Human-thumb joints

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Fig. 5

The thumb-tip workspace of the human-thumb model

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Fig. 6

The kinematic model of the thumb of the Shadow Hand

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Fig. 7

The thumb-tip workspace of the Shadow Hand

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Fig. 8

Generating sensor readings

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Fig. 9

Precision grasps of objects of length 25 mm, 50 mm, and 75 mm

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