Optimization based path planning via Big Bang-Big Crunch with Local Search

Sabri Yilmaz, Metin Gökaşan

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

1 Citation (Scopus)

Abstract

In this paper a search for the trajectory that minimizes the cost function is studied. In robotic studies the cost function can be defined as a function of time, tracking error or applied torque. In this study the cost function is selected as a function of applied torque, so the main aim is minimizing the energy consumption. For this purpose a simple robot manipulator is chosen, and its kinematic and dynamic models are derived by Denavit-Hartenberg convention and Euler-Lagrange method. Then two different trajectory polynomials are described, one is solved from boundary conditions without optimization and one is solved by optimization and the same boundary conditions. These two different trajectory polynomials and their cost functions values are compared. The effect and efficiency of optimization are examined.

Original languageEnglish
Title of host publicationProceedings - 5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages60-65
Number of pages6
ISBN (Electronic)9781479982523
DOIs
Publication statusPublished - 31 May 2016
Event5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015 - Batu Ferringhi, Penang, Malaysia
Duration: 27 Nov 201529 Nov 2015

Publication series

NameProceedings - 5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015

Conference

Conference5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015
Country/TerritoryMalaysia
CityBatu Ferringhi, Penang
Period27/11/1529/11/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

  • big bang-big crunch optimization algorithm
  • global optimization
  • robot dynamics
  • trajectory planning

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