Robot belt grinding trajectory optimization based on GLS-PSO

  • Hongjun Yang*
  • , Yixu Song
  • , Wei Liang
  • , Peifa Jia
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

To automatically generate the reference trajectory of control parameters in robot belt grinding system, this paper presents a Genetic and Local Search-based Particle Swarm Optimization Algorithm to optimize two main parameters, contact force and feed rate. The proposed approach takes advantage of Local Search Technology to accelerate the learning and searching process, which is expected to improve the quality of particles as well; Meanwhile, Genetic crossover between individuals is used to combine good genes to produce better offspring. The experimental results show that the GLS-PSO is superior to LS-PSO, G-PSO and S-SPO in terms of both algorithm performance and optimized effects. In addition, the proposed GLS-PSO algorithm meets the requirements of industrial control in robotic belt grinding, which demonstrates the feasibility of this method.

Original languageEnglish
Title of host publicationProceedings of the 30th Chinese Control Conference, CCC 2011
Pages5418-5423
Number of pages6
Publication statusPublished - 2011
Externally publishedYes
Event30th Chinese Control Conference, CCC 2011 - Yantai, China
Duration: 22 Jul 201124 Jul 2011

Publication series

NameProceedings of the 30th Chinese Control Conference, CCC 2011

Conference

Conference30th Chinese Control Conference, CCC 2011
Country/TerritoryChina
CityYantai
Period22/07/1124/07/11

Keywords

  • Genetic Algorithm
  • Local Search
  • Particle Swarm Optimization
  • Robotic Belt Grinding
  • Trajectory Optimization

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