Self-learning Skill Parameters for Robotic Peg-in-Hole Assembly Task

被引：0

作者：

Roshanbin, Ali ^{[1
]}

Zhang, Yang ^{[1
]}

Afzal, Muhammad Raheel ^{[1
]}

机构：

[1] Flanders Make, ProductionS CoreLab, Leuven, Belgium

来源：

2023 11TH INTERNATIONAL CONFERENCE ON CONTROL, MECHATRONICS AND AUTOMATION, ICCMA | 2023年

关键词：

peg-in-hole assembly task; Robot skill parameters; learning scheme;

D O I：

10.1109/ICCMA59762.2023.10375064

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This work proposes self-learning methods to find out optimal robot skill parameters for force-guided robotic peg-in-hole assembly task. To this end, learning schemes for skill parameters adjustment are implemented. Two approaches are proposed to facilitate robot programming: (i) interval method for spiral hole searching, and (ii) gradient method for tilted peg-in-hole searching approach. The Spiral approach is more advantageous for tight tolerances while tilted method is best for faster skill execution and looser tolerances. The proposed methodology is validated on a KUKA iiwa robot on the Cranfield kit.

引用

页码：253 / 258

页数：6

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