Research Papers

Computational Optimization and Experimental Evaluation of Grasp Quality for Tendon-Driven Hands Subject to Design Constraints

[+] Author and Article Information
Joshua M. Inouye

Multiscale Muscle Mechanics Lab,
Department of Biomedical Engineering,
University of Virginia,
Charlottesville, VA 22902
e-mail: jmi@virginia.edu

Francisco J. Valero-Cuevas

Brain-Body Dynamics Lab,
Department of Biomedical Engineering &
the Division of Biokinesiology and
Physical Therapy,
University of Southern California,
Los Angeles, CA 90089
e-mail: valero@usc.edu

The sum of maximal tendon tensions being equal is an important constraint due to the size, weight, and motor torque (and therefore tendon tension) limitations inherent in dextrous hands. For example, the torque capacity of motors is roughly proportional to motor weight, and minimization of weight was an important consideration in the design of the DLR Hand II [26]. In addition, the maximal force production capabilities of McKibben-style muscles are roughly proportional to cross-sectional area [18]. Since the actuators typically will be located in the forearm, then the total cross-sectional area will be limited to the forearm cross-sectional area. In this study, we do not consider alternative constraints on the actuation system (e.g., electrical current capacity, tendon velocities, etc).

This defined a point-contact with friction, which is needed to match the mathematical framework of the analysis as it does not make the system over-constrained [32].

The Minkowski sum in this context refers to the combination of each vertex of one feasible object force set with every vertex of the other feasible object force set.

Contributed by the Mechanisms and Robotics Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received July 8, 2013; final manuscript received October 18, 2013; published online December 11, 2013. Assoc. Editor: Oscar Altuzarra.

J. Mech. Des 136(2), 021009 (Dec 11, 2013) (7 pages) Paper No: MD-13-1296; doi: 10.1115/1.4025964 History: Received July 08, 2013; Revised October 18, 2013

The chief tasks of robotic and prosthetic hands are grasping and manipulating objects, and size and weight constraints are very influential in their design. In this study, we use computational modeling to both predict and optimize the grasp quality of a reconfigurable, tendon-driven hand. Our computational results show that grasp quality, measured by the radius of the largest ball in wrench space, could be improved up to 259% by simply making some pulleys smaller and redistributing the maximal tensions of the tendons. We experimentally evaluated several optimized and unoptimized designs, which had either 4, 5, or 6 tendons and found that the theoretical calculations are effective at predicting grasp quality, with an average friction loss in this system of around 30%. We conclude that this optimization can be a very useful design tool and that using biologically inspired asymmetry and parameter adjustments can be used to maximize performance.

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Fig. 1

Top and side views of finger design and kinematics

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Fig. 2

Finger placements for each grasp

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Fig. 3

Base moment arm matrices used when finding realizable, unique tendon routings

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Fig. 9

Results from experimental testing of various routings. Experimental vs. theoretical grasp quality for both Grasp 1 and 2. Parity line is where experimental grasp quality would be exactly equal to theoretical grasp quality (intercept of 0, slope of 1). Regression line constant term forced to zero. 3-D force portions of grasp wrench set for two different tests shown on right (torque constrained to zero).

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Fig. 8

Computational predictions of fitness for unoptimized and optimized naive 2N design

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Fig. 7

Computational results of grasp quality for hand designs. (a) Optimization paths shown. (b) Final optimized designs indicating pulley-size and maximal tendon tension

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Fig. 6

(a) We used a ball-and-socket joint to hold the fingers in place. (b) Experimental grasps 1 and 2 are shown as depicted in Fig. 2

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Fig. 5

Experimental system for grasp testing

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Fig. 4

Illustration of Markov-Chain Monte Carlo algorithm for distribution of maximal tendon tensions

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Fig. 10

Experimental vs. predicted volumes of grasp wrench sets




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