Research Papers

Examining the Robustness of Grasping Force Optimization Methods Using Uncertainty Analysis

[+] Author and Article Information
Aimee Cloutier

Department of Mechanical Engineering,
Rose-Hulman Institute of Technology,
5500 Wabash Avenue,
Terre Haute, IN 47803
e-mail: cloutier@rose-hulman.edu

James Yang

Department of Mechanical Engineering,
Human-Centric Design Research Lab,
Texas Tech University,
Lubbock, TX 79409
e-mail: james.yang@ttu.edu

1Corresponding author.

Manuscript received June 17, 2017; final manuscript received February 19, 2018; published online April 30, 2018. Assoc. Editor: Faisal Khan.

ASME J. Risk Uncertainty Part B 4(4), 041007 (Apr 30, 2018) (8 pages) Paper No: RISK-17-1071; doi: 10.1115/1.4039467 History: Received June 17, 2017; Revised February 19, 2018

The development of robust and adaptable methods of grasping force optimization (GFO) is an important consideration for robotic devices, especially those which are designed to interact naturally with a variety of objects. Along with considerations for the computational efficiency of such methods, it is also important to ensure that a GFO approach chooses forces which can produce a stable grasp even in the presence of uncertainty. This paper examines the robustness of three methods of GFO in the presence of variability in the contact locations and in the coefficients of friction between the hand and the object. A Monte Carlo simulation is used to determine the resulting probability of failure and sensitivity levels when variability is introduced. Two numerical examples representing two common grasps performed by the human hand are used to demonstrate the performance of the optimization methods. Additionally, the method which yields the best overall performance is also tested to determine its consistency when force is applied to the object's center of mass in different directions. The results show that both the nonlinear and linear matrix inequality (LMIs) methods of GFO produce acceptable results, whereas the linear method produces unacceptably high probabilities of failure. Further, the nonlinear method continues to produce acceptable results even when the direction of the applied force is changed. Based on these results, the nonlinear method of GFO is considered to be robust in the presence of variability in the contact locations and coefficients of friction.

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Grahic Jump Location
Fig. 1

Visual representation of grasping problem

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Fig. 2

Generic view of the hand

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Fig. 3

Types of grasp: (a) tip grasp and (b) tripod grasp

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Fig. 4

Sensitivity levels for tip grasp (LMI method and HF contact)

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Fig. 5

Sensitivity levels for tip grasp (LMI method and SF contact)

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Fig. 6

Sensitivity levels for tip grasp (nonlinear method and HF contact)

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Fig. 7

Sensitivity levels for tip grasp (nonlinear method and SF contact)

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Fig. 8

Sensitivity levels for tip grasp (linear method and HF contact)

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Fig. 9

Sensitivity levels for tripod grasp (linear method and HF contact)

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Fig. 10

Sensitivity levels for tip grasp, nonlinear method (three cases, HF contact)

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Fig. 11

Sensitivity levels for tip grasp, nonlinear method (three cases, SF contact)




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