Optimal Trajectory Path Generation for Jointed Structure of Excavator using Genetic Algorithm

Ggyebong Jang, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose an algorithm to generate optimal trajectory path considering the complex operating environment of excavator front part composed of the boom, arm, and bucket by using genetic algorithm. In order to express motion in space, we propose a method of coordinate plane space of grid cell, and define the fitness value by path distance. After generating chromosome candidates for each motion unit based on the polygonal structure of the front part of the excavator, we calculate the fitness value about each chromosome. The crossover and mutation operations between the chromosomes selected through roulette wheel of top 20% are repeatedly performed to generate paths with optimal fitness values. This paper verifies the structural analysis of the front part of excavator and the utility of the genetic algorithm to optimize the path in the grid space.

Original languageEnglish
Title of host publication2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1953-1959
Number of pages7
ISBN (Electronic)9781728121536
DOIs
Publication statusPublished - 2019 Jun
Event2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Wellington, New Zealand
Duration: 2019 Jun 102019 Jun 13

Publication series

Name2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

Conference

Conference2019 IEEE Congress on Evolutionary Computation, CEC 2019
CountryNew Zealand
CityWellington
Period19/6/1019/6/13

Fingerprint

Excavators
Optimal Trajectory
Chromosomes
Genetic algorithms
Trajectories
Genetic Algorithm
Fitness
Chromosome
Path
Roulette
Cartesian plane
Grid
Structural analysis
Motion
Wheels
Structural Analysis
Wheel
Crossover
Mutation
Express

All Science Journal Classification (ASJC) codes

  • Computational Mathematics
  • Modelling and Simulation

Cite this

Jang, G., & Cho, S. B. (2019). Optimal Trajectory Path Generation for Jointed Structure of Excavator using Genetic Algorithm. In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 1953-1959). [8790011] (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2019.8790011
Jang, Ggyebong ; Cho, Sung Bae. / Optimal Trajectory Path Generation for Jointed Structure of Excavator using Genetic Algorithm. 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1953-1959 (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings).
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Jang, G & Cho, SB 2019, Optimal Trajectory Path Generation for Jointed Structure of Excavator using Genetic Algorithm. in 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings., 8790011, 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 1953-1959, 2019 IEEE Congress on Evolutionary Computation, CEC 2019, Wellington, New Zealand, 19/6/10. https://doi.org/10.1109/CEC.2019.8790011

Optimal Trajectory Path Generation for Jointed Structure of Excavator using Genetic Algorithm. / Jang, Ggyebong; Cho, Sung Bae.

2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1953-1959 8790011 (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Jang G, Cho SB. Optimal Trajectory Path Generation for Jointed Structure of Excavator using Genetic Algorithm. In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1953-1959. 8790011. (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings). https://doi.org/10.1109/CEC.2019.8790011