Ranged subgroup particle swarm optimization for localizing multiple odor sources

Wisnu Jatmiko, W. Pambuko, A. Febrian, Petrus Mursanto, Abdul Muis, Benyamin Kusumo Putro, K. Sekiyama, T. Fukuda

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

A new algorithm based on Modified Particle Swarm Optimization (MPSO) that follows is a local gradient of a chemical concentration within a plume and follows the direction of the wind velocity is investigated. Moreover, the niche or parallel search characteristic is adopted on MPSO to solve the multi-peak and multi-source problem. When using parallel MPSO, subgroup of robot is introduced then each subgroup can locate the odor source. Unfortunately, there is a possibility that more that one subgroup locates one odor sources. This is inefficient because other subgroups locate other source, then we proposed a ranged subgroup method for coping for that problem, then the searching performance will increase. Finally ODE (Open Dynamics Engine) library is used for physical modeling of the robot like friction, balancing moment and others so that the simulation adequate to accurately address the real life scenario.

Original languageEnglish
Pages (from-to)411-442
Number of pages32
JournalInternational Journal on Smart Sensing and Intelligent Systems
Volume3
Issue number3
DOIs
Publication statusPublished - 1 Jan 2010

Keywords

  • Modified particle swarm optimization
  • Multiple odor sources localization
  • Open dynamic engine
  • Parallel search
  • Real life scenario
  • Subgroup

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