A Neural Network Approach to the Frictionless Grasping Problem

被引:0
作者
R. Abu-Zitar
A. M. Al-Fahed Nuseirat
机构
[1] Al-Isra Private University,Computer Science Department
[2] Al-Isra Private University,Faculty of Engineering
来源
Journal of Intelligent and Robotic Systems | 2000年 / 29卷
关键词
linear complementarity problem; neural network; robot gripper; unilateral contact;
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中图分类号
学科分类号
摘要
This article presents a heuristic technique used for solving linear complementarity problems(LCP). Determination of minimum forces needed to firmly grasp an object by a multifingered robot gripper for different external force and finger positions is our proposed application. The contact type is assumed to be frictionless. The interaction in the gripper–object system is formulated as an LCP. A numerical algorithm (Lemke) is used to solve the problem [3]. Lemke is a direct deterministic method that finds exact solutions under some constraints. Our proposed neural network technique finds almost exact solutions in solvable positions, and very good solutions for positions that Lemke fails to find solutions. A new adaptive technique is used for training the neural network and it is compared with the standard technique. Mathematical analysis for the convergence of the proposed technique is presented.
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页码:27 / 45
页数:18
相关论文
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