Robust grasp preimages under unknown mass and friction distributions

被引：4

作者：

Price, Andrew ^{[1
]}

Balakirsky, Stephen ^{[2
]}

Christensen, Henrik ^{[3
]}

机构：

[1] Georgia Inst Technol, Inst Robot & Intelligent Machines, 801 Atlantic Dr NW, Atlanta, GA 30332 USA

[2] Georgia Tech Res Inst, Robot & Autonomous Syst Div, Atlanta, GA 30332 USA

[3] Univ Calif San Diego, Dept Comp Sci & Engn, La Jolla, CA 92093 USA

来源：

INTEGRATED COMPUTER-AIDED ENGINEERING | 2018年 / 25卷 / 02期

关键词：

Grasping; grasp planning; differential games; motion planning; sliding; STRATEGY; MECHANICS; PARTS;

D O I：

10.3233/ICA-180568

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This work introduces an algorithm for the computation of robust grasp preimages: the space of initial poses from which an object will converge into the desired grasp. Building on existing motion and friction models for pushed objects under contact, we describe a game-theoretic technique for estimating worst-case scenarios for difficult to observe properties like pressure and friction distributions. The use of this antagonistic model in the grasping simulations provides for a conservative estimate of the preimage of the given grasp. The antagonistic model is then validated against data from real grasping experiments on various robot grippers.

引用

页码：99 / 110

页数：12

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