An effective global path planning algorithm with teaching-learning-based optimization

Emad Hazrati Nejad; Sevgi Yigit-Sert; Sahin Emrah Amrahov

Kybernetika (2024)

  • Issue: 3, page 293-316
  • ISSN: 0023-5954

Abstract

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Due to the widespread use of mobile robots in various applications, the path planning problem has emerged as one of the important research topics. Path planning is defined as finding the shortest path starting from the initial point to the destination in such a way as to get rid of the obstacles it encounters. In this study, we propose a path planning algorithm based on a teaching-learning-based optimization (TLBO) algorithm with Bezier curves in a static environment with obstacles. The proposed algorithm changes the initially randomly selected control points step by step to obtain shorter Bezier curves that do not hit obstacles. We also improve the genetic algorithm-based path planning algorithm. Experimental results show that they provide better paths than other existing algorithms.

How to cite

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Hazrati Nejad, Emad, Yigit-Sert, Sevgi, and Emrah Amrahov, Sahin. "An effective global path planning algorithm with teaching-learning-based optimization." Kybernetika (2024): 293-316. <http://eudml.org/doc/299289>.

@article{HazratiNejad2024,
abstract = {Due to the widespread use of mobile robots in various applications, the path planning problem has emerged as one of the important research topics. Path planning is defined as finding the shortest path starting from the initial point to the destination in such a way as to get rid of the obstacles it encounters. In this study, we propose a path planning algorithm based on a teaching-learning-based optimization (TLBO) algorithm with Bezier curves in a static environment with obstacles. The proposed algorithm changes the initially randomly selected control points step by step to obtain shorter Bezier curves that do not hit obstacles. We also improve the genetic algorithm-based path planning algorithm. Experimental results show that they provide better paths than other existing algorithms.},
author = {Hazrati Nejad, Emad, Yigit-Sert, Sevgi, Emrah Amrahov, Sahin},
journal = {Kybernetika},
keywords = {path planning; mobile robot; teaching-learning based optimization; Bezier curve},
language = {eng},
number = {3},
pages = {293-316},
publisher = {Institute of Information Theory and Automation AS CR},
title = {An effective global path planning algorithm with teaching-learning-based optimization},
url = {http://eudml.org/doc/299289},
year = {2024},
}

TY - JOUR
AU - Hazrati Nejad, Emad
AU - Yigit-Sert, Sevgi
AU - Emrah Amrahov, Sahin
TI - An effective global path planning algorithm with teaching-learning-based optimization
JO - Kybernetika
PY - 2024
PB - Institute of Information Theory and Automation AS CR
IS - 3
SP - 293
EP - 316
AB - Due to the widespread use of mobile robots in various applications, the path planning problem has emerged as one of the important research topics. Path planning is defined as finding the shortest path starting from the initial point to the destination in such a way as to get rid of the obstacles it encounters. In this study, we propose a path planning algorithm based on a teaching-learning-based optimization (TLBO) algorithm with Bezier curves in a static environment with obstacles. The proposed algorithm changes the initially randomly selected control points step by step to obtain shorter Bezier curves that do not hit obstacles. We also improve the genetic algorithm-based path planning algorithm. Experimental results show that they provide better paths than other existing algorithms.
LA - eng
KW - path planning; mobile robot; teaching-learning based optimization; Bezier curve
UR - http://eudml.org/doc/299289
ER -

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