Adaptable Genetic Algorithm Path Planning of Mobile Robots Based on Gene Reallocating

Faten Abushmmala; Iyad Abuhadrous

doi:doi:10.11648/j.advances.20220303.22

| Peer-Reviewed

Adaptable Genetic Algorithm Path Planning of Mobile Robots Based on Gene Reallocating

Faten Abushmmala, Iyad Abuhadrous

Published in Advances (Volume 3, Issue 3)

Received: 31 August 2022 Accepted: 19 September 2022 Published: 29 September 2022

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Mobile path planning is rich field of employing artificial intelligence and machine learning algorithms to obtain the most effective outcomes. The Path planning task is a problem. The goal of path design is to find the quickest and most obstacle-free route from a starting point to a destination state. A set of states (position and orientation) or waypoints can make up the path. A map of the surroundings, as well as the start and target states, are needed for path planning. Path planning applications are diverse and unlimited, such as Automated robot navigation, autonomous vehicle Robotic surgery, digital animation of characters, and others. Different algorithms provide different solutions to this problem; usually the metric used to evaluate certain path effectiveness doesn’t take into consideration the physical attributes of the mobile robot. In this paper, an attempt is made to find the best path in terms of distance and smoothness (minim number of rotations); the smoothness means decreasing power consumption since the rotations take a lot of power to be executed. A traditional genetic algorithm is used to find the best path, and then modification is used to improve the path's characteristics. The experimental results obtained using MATLAB Simulator indicate that the enhanced approach applied in the genetic algorithm provides much better outcomes, the path edges are minimized along with the path length.

Published in	Advances (Volume 3, Issue 3)
DOI	10.11648/j.advances.20220303.22
Page(s)	125-131
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Path Planning, Genetic Algorithm, GA, Mobile Path

References

[1]	Rudnick-Cohen, E. S., Herrmann, J., & Azarm, S. (2015). Risk-based path planning optimization methods for uavs over inhabited areas. Journal of Computing & Information Science in Engineering.
[2]	Kamyar, R., & Taheri, E. (2014). Aircraft optimal terrain/threat-based trajectory planning and control. Journal of Guidance Control & Dynamics, 37 (2), 466-483.
[3]	Liang, H. F., Fan, G. C., & Dong, Y. F. (2005). Generation of target aircraft's flight track in air combat plan simulation. Computer Simulation, 22 (12), 32-34.
[4]	Xian, Y., He, B., Liu, G., & Lei, G. (2012). Cruise missile route planning based on quantum immune clone algorithm. Journal of Information & Computational Science, 9 (8), 2097-2105.
[5]	Qinggang Su, Wangwang Yu and Jun Liu. (2021), "Mobile Robot Path Planning Based on Improved Ant Colony Algorithm" 2021 Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS).
[6]	Chunyu Ju, Qinghua Luo and Xiaozhen Yan. (2020). "Path Planning Using an Improved A-star Algorithm", 2020 11th International Conference on Prognostics and System Health Management (PHM-2020 Jinan), 2166-5656/20/$31.00 ©2020 IEEE, DOI 10.1109/PHM-Jinan48558.2020.00012.
[7]	Chaymaa Laminia, Said Benhlima and Ali Elbekria, (2018) "Genetic Algorithm Based Approach for Autonomous Mobile Robot Path Planning", The First International Conference On Intelligent Computing in Data Sciences, 2018 Published by Elsevier B. V.
[8]	Lee, J., & Kim, D. W. (2016). An effective initialization method for genetic algorithm-based robot path planning using a directed acyclic graph. Elsevier Science Inc.
[9]	Tuncer, A., & Yildirim, M. (2012). Dynamic path planning of mobile robots with improved genetic algorithm. Computers & Electrical Engineering, 38 (6), 1564-1572.
[10]	Belhaous, S.; Baroud, S.; Chokri, S.; Hidila, Z.; Naji, A.; Mestari, M. Parallel implementation of A* search algorithm for road network. In Proceedings of the 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS), Marrakech, Morocco, 28–30 October 2019; pp. 1–7.
[11]	Dong, G.; Yang, F.; Tsui, K. L.; Zou, C. Active Balancing of Lithium-Ion Batteries Using Graph Theory and A-Star Search Algorithm. IEEE Trans. Ind. Inform. 2021, 17, 2587–2599.
[12]	Liu, H.; Shan, T.; Wang, W. Automatic Routing Study of Spacecraft Cable based on A-star Algorithm. In Proceedings of the 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China, 12–14 June 2020; pp. 716–719.
[13]	Lance D. Chambers, (2019) "The Practical Handbook of Genetic Algorithms: New Frontiers, Volume II, Volume 2", Publisher, CRC Press, 2019, ISBN 1420050079, 9781420050073, Length- 464 pages.
[14]	Zhang, Q., & Ding, L. (2016). A new crossover mechanism for genetic algorithms with variable-length chromosomes for path optimization problems. Expert Systems with Applications, 60 (C), 183-189.
[15]	Nandini, D., & Seeja, K. R. (2017). A novel path planning algorithm for visually impaired people. Journal of King Saud University - Computer and Information Sciences.
[16]	Dao, S. D., Abhary, K., & Marian, R. (2017). “A bibliometric analysis of genetic algorithms throughout the history”. Computers & Industrial Engineering, 110.
[17]	Ghiduk, A. S. (2014). “Automatic generation of basis test paths using variable length genetic algorithm”. Information Processing Letters, 114 (6), 304-316.
[18]	Zhang, Q., & Ding, L. (2016). “A new crossover mechanism for genetic algorithms with variable-length chromosomes for path optimization problems”. Expert Systems with Applications, 60 (C), 183-189.

Cite This Article

Plain Text BibTeX RIS

APA Style

Faten Abushmmala, Iyad Abuhadrous. (2022). Adaptable Genetic Algorithm Path Planning of Mobile Robots Based on Gene Reallocating. Advances, 3(3), 125-131. https://doi.org/10.11648/j.advances.20220303.22

Copy | Download

ACS Style

Faten Abushmmala; Iyad Abuhadrous. Adaptable Genetic Algorithm Path Planning of Mobile Robots Based on Gene Reallocating. Advances. 2022, 3(3), 125-131. doi: 10.11648/j.advances.20220303.22

Copy | Download

AMA Style

Faten Abushmmala, Iyad Abuhadrous. Adaptable Genetic Algorithm Path Planning of Mobile Robots Based on Gene Reallocating. Advances. 2022;3(3):125-131. doi: 10.11648/j.advances.20220303.22

Copy | Download

@article{10.11648/j.advances.20220303.22,
  author = {Faten Abushmmala and Iyad Abuhadrous},
  title = {Adaptable Genetic Algorithm Path Planning of Mobile Robots Based on Gene Reallocating},
  journal = {Advances},
  volume = {3},
  number = {3},
  pages = {125-131},
  doi = {10.11648/j.advances.20220303.22},
  url = {https://doi.org/10.11648/j.advances.20220303.22},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.advances.20220303.22},
  abstract = {Mobile path planning is rich field of employing artificial intelligence and machine learning algorithms to obtain the most effective outcomes. The Path planning task is a problem. The goal of path design is to find the quickest and most obstacle-free route from a starting point to a destination state. A set of states (position and orientation) or waypoints can make up the path. A map of the surroundings, as well as the start and target states, are needed for path planning. Path planning applications are diverse and unlimited, such as Automated robot navigation, autonomous vehicle Robotic surgery, digital animation of characters, and others. Different algorithms provide different solutions to this problem; usually the metric used to evaluate certain path effectiveness doesn’t take into consideration the physical attributes of the mobile robot. In this paper, an attempt is made to find the best path in terms of distance and smoothness (minim number of rotations); the smoothness means decreasing power consumption since the rotations take a lot of power to be executed. A traditional genetic algorithm is used to find the best path, and then modification is used to improve the path's characteristics. The experimental results obtained using MATLAB Simulator indicate that the enhanced approach applied in the genetic algorithm provides much better outcomes, the path edges are minimized along with the path length.},
 year = {2022}
}

Copy | Download

TY - JOUR
T1 - Adaptable Genetic Algorithm Path Planning of Mobile Robots Based on Gene Reallocating
AU - Faten Abushmmala
AU - Iyad Abuhadrous
Y1 - 2022/09/29
PY - 2022
N1 - https://doi.org/10.11648/j.advances.20220303.22
DO - 10.11648/j.advances.20220303.22
T2 - Advances
JF - Advances
JO - Advances
SP - 125
EP - 131
PB - Science Publishing Group
SN - 2994-7200
UR - https://doi.org/10.11648/j.advances.20220303.22
AB - Mobile path planning is rich field of employing artificial intelligence and machine learning algorithms to obtain the most effective outcomes. The Path planning task is a problem. The goal of path design is to find the quickest and most obstacle-free route from a starting point to a destination state. A set of states (position and orientation) or waypoints can make up the path. A map of the surroundings, as well as the start and target states, are needed for path planning. Path planning applications are diverse and unlimited, such as Automated robot navigation, autonomous vehicle Robotic surgery, digital animation of characters, and others. Different algorithms provide different solutions to this problem; usually the metric used to evaluate certain path effectiveness doesn’t take into consideration the physical attributes of the mobile robot. In this paper, an attempt is made to find the best path in terms of distance and smoothness (minim number of rotations); the smoothness means decreasing power consumption since the rotations take a lot of power to be executed. A traditional genetic algorithm is used to find the best path, and then modification is used to improve the path's characteristics. The experimental results obtained using MATLAB Simulator indicate that the enhanced approach applied in the genetic algorithm provides much better outcomes, the path edges are minimized along with the path length.
VL - 3
IS - 3
ER -

Copy | Download

Author Information

Faten Abushmmala

Computer Engineering, Islamic University, Gaza, Palestine
Iyad Abuhadrous

Computer Engineering, Palestine Technical College, Gaza, Palesitne

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Faten Abushmmala, Iyad Abuhadrous. (2022). Adaptable Genetic Algorithm Path Planning of Mobile Robots Based on Gene Reallocating. Advances, 3(3), 125-131. https://doi.org/10.11648/j.advances.20220303.22

Copy | Download

ACS Style

Faten Abushmmala; Iyad Abuhadrous. Adaptable Genetic Algorithm Path Planning of Mobile Robots Based on Gene Reallocating. Advances. 2022, 3(3), 125-131. doi: 10.11648/j.advances.20220303.22

Copy | Download

AMA Style

Faten Abushmmala, Iyad Abuhadrous. Adaptable Genetic Algorithm Path Planning of Mobile Robots Based on Gene Reallocating. Advances. 2022;3(3):125-131. doi: 10.11648/j.advances.20220303.22

Copy | Download

@article{10.11648/j.advances.20220303.22,
  author = {Faten Abushmmala and Iyad Abuhadrous},
  title = {Adaptable Genetic Algorithm Path Planning of Mobile Robots Based on Gene Reallocating},
  journal = {Advances},
  volume = {3},
  number = {3},
  pages = {125-131},
  doi = {10.11648/j.advances.20220303.22},
  url = {https://doi.org/10.11648/j.advances.20220303.22},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.advances.20220303.22},
  abstract = {Mobile path planning is rich field of employing artificial intelligence and machine learning algorithms to obtain the most effective outcomes. The Path planning task is a problem. The goal of path design is to find the quickest and most obstacle-free route from a starting point to a destination state. A set of states (position and orientation) or waypoints can make up the path. A map of the surroundings, as well as the start and target states, are needed for path planning. Path planning applications are diverse and unlimited, such as Automated robot navigation, autonomous vehicle Robotic surgery, digital animation of characters, and others. Different algorithms provide different solutions to this problem; usually the metric used to evaluate certain path effectiveness doesn’t take into consideration the physical attributes of the mobile robot. In this paper, an attempt is made to find the best path in terms of distance and smoothness (minim number of rotations); the smoothness means decreasing power consumption since the rotations take a lot of power to be executed. A traditional genetic algorithm is used to find the best path, and then modification is used to improve the path's characteristics. The experimental results obtained using MATLAB Simulator indicate that the enhanced approach applied in the genetic algorithm provides much better outcomes, the path edges are minimized along with the path length.},
 year = {2022}
}

Copy | Download