Research Papers

Grasping Force Optimization Approaches for Anthropomorphic Hands

[+] Author and Article Information
Aimee Cloutier

Human-Centric Design Research Lab,
Department of Mechanical Engineering,
Texas Tech University,
Lubbock, TX 79401
e-mail: aimee.cloutier@ttu.edu

James Yang

Human-Centric Design Research Lab,
Department of Mechanical Engineering,
Texas Tech University,
Lubbock, TX 79401
e-mail: james.yang@ttu.edu

1Corresponding author.

Manuscript received May 4, 2017; final manuscript received November 15, 2017; published online December 20, 2017. Assoc. Editor: Veronica J. Santos.

J. Mechanisms Robotics 10(1), 011004 (Dec 20, 2017) (10 pages) Paper No: JMR-17-1133; doi: 10.1115/1.4038684 History: Received May 04, 2017; Revised November 15, 2017

An appropriate choice of contact forces for anthropomorphic robotic grasping devices is important for achieving a balanced grasp. Too little applied force may cause an object to slip or be dropped, and too much applied force may cause damage to delicate objects. Prior methods of grasping force optimization (GFO) in the literature can be difficult to compare due to variability in the parameters, such as the type of grasping device, the object grasped, and the contact model, among other factors. Additionally, methods are typically tested on a very small number of scenarios and may not be as robust in other settings. This paper presents a detailed analysis of three optimization approaches based on the literature, comparing them on the basis of accuracy and computational efficiency. Numerical examples are provided for three types of grasp commonly performed by the human hand (cylindrical grasp, tip grasp, and tripod grasp) using both soft finger (SF) contact and hard finger (HF) contact friction models. For each method and grasping example, an external force is applied to the object in eighteen different directions to provide a more complete picture of the methods' performance. Contact points between the hand and the object are predetermined (given). A comparison of the results showed that the nonlinear and linear matrix inequality (LMI) approaches perform best in terms of accuracy, while the computational efficiency of the linear method is stronger unless the number of contact points and segments becomes too large. In this case, the nonlinear method performs more quickly. Future work will extend the problem of GFO to real-time implementation, and a related work (briefly addressed here) examines the sensitivity of optimization methods to variability in the contact locations.

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Grahic Jump Location
Fig. 1

Visual representation of grasping problem

Grahic Jump Location
Fig. 2

Approximation of a quadratic cone as a polyhedral cone

Grahic Jump Location
Fig. 3

Generic view of the hand

Grahic Jump Location
Fig. 4

Types of grasp: (a) tip grasp, (b) tripod grasp, and (c) cylinder grasp

Grahic Jump Location
Fig. 5

Forces applied to objects




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