SavedAdd your journal

Journals / Avrupa Bilim ve Teknoloji Dergisi / 2021 / Sayı: 32

Comparison of Maths-Based Metaheuristics for Grid-Based Path Planning

Izgara Bazlı Yol Planlama için Matematik Tabanlı Metasezgisellerin Karşılaştırılması

Mustafa Yusuf YILDIRIM
Rüştü AKAY
Pages
521–530
Publication Date
2021-12-31
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

When the results were evaluated, it was observed that SFS algorithm has given better results than other algorithms in terms of shortest distance and obstacle avoidance.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Path planning, one of the most important components of robot navigation, has been extensively studied by researchers in recent years and many different metaheuristic algorithms are used for this problem.
📊
Key Findings & Data:In this study, the global path planning of a mobile robot in a grid-type environment is discussed and the effects of different maths-based metaheuristic algorithms for this problem are investigated.
💡
Conclusion & Impact:When the results were evaluated, it was observed that SFS algorithm has given better results than other algorithms in terms of shortest distance and obstacle avoidance.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Yol Planlama · Metasezgisel · Matematik Tabanlı Algoritmalar

Abstract

Path planning, one of the most important components of robot navigation, has been extensively studied by researchers in recent years and many different metaheuristic algorithms are used for this problem. In this study, the global path planning of a mobile robot in a grid-type environment is discussed and the effects of different maths-based metaheuristic algorithms for this problem are investigated. First, three different environments with grid type and different difficulty levels were designed. Then, the optimum paths of the robot in the environments were calculated by using different maths-based algorithms developed in recent years. Stochastic fractal search (SFS), arithmetic optimization algorithm (AOA) and sine cosine algorithm (SCA) were used as metaheuristic algorithms in the study. When the results were evaluated, it was observed that SFS algorithm has given better results than other algorithms in terms of shortest distance and obstacle avoidance.

Özet

Robot navigasyonunun en önemli bileşenlerinden biri olan yol planlama son yıllarda da araştırmacılar tarafından kapsamlı bir şekilde incelenmekte ve bu problem için birçok farklı metasezgisel algoritma kullanılmaktadır. Bu çalışmada ızgara tipi bir ortamda bir mobil robotun küresel yol planlaması ele alınmış ve bu problem için farklı matematik tabanlı metasezgisel algoritmalarının etkileri incelenmiştir. Öncelikle ızgara tipinde ve farklı zorluk derecelerinde üç farklı ortam tasarlanmıştır. Ardından, son yıllarda geliştirilen farklı matematik tabanlı algoritmalar kullanılarak robotun ortamlardaki optimum yolları hesaplanmıştır. Çalışmada metasezgisel algoritma olarak stokastik fraktal arama (Stochastic Fractal Search, SFS), aritmetik optimizasyon algoritması (Arithmetic Optimization Algorithm, AOA) ve sinüs kosinüs algoritması (Sine Cosine Algorithm, SCA) kullanılmıştır. Bulgular değerlendirildiğinde SFS algoritmasının en kısa mesafe ve engelden kaçınma açısından diğer algoritmalara göre daha iyi sonuçlar verdiği gözlemlenmiştir.

🔖

Cite This Article

YILDIRIM, M. Y., AKAY, R. (2021). Comparison of Maths-Based Metaheuristics for Grid-Based Path Planning. Avrupa Bilim ve Teknoloji Dergisi, Sayı: 32, 521–530. https://ajindex.com/articles/470151

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (11)

  1. Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A. H. (2021). The Arithmetic Optimization Algorithm. Computer Methods in Applied Mechanics and Engineering, 376, 113609. https://doi.org/10.1016/j.cma.2020.113609
  2. Abualigah, L. (2021). The Arithmetic Optimization Algorithm (AOA). MATLAB Central File Exchange. https://www.mathworks.com/matlabcentral/fileexchange/84742-the-arithmetic-optimization-algorithm-aoa
  3. Adamu, P. I., Okagbue, H. I., & Oguntunde, P. E. (2019). Fast and Optimal Path Planning Algorithm (FAOPPA) for a Mobile Robot. Wireless Personal Communications, 106(2), 577–592. https://doi.org/10.1007/s11277-019-06180-w
  4. Ajeil, F. H., Ibraheem, I. K., Sahib, M. A., & Humaidi, A. J. (2020). Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm. Applied Soft Computing Journal, 89, 106076. https://doi.org/10.1016/j.asoc.2020.106076
  5. Ajeil, F. H., Ibraheem, I. K., Azar, A. T., & Humaidi, A. J. (2020). Grid-based mobile robot path planning using aging-based ant colony optimization algorithm in static and dynamic environments. Sensors (Switzerland), 20(7). https://doi.org/10.3390/s20071880
  6. Akka, K., & Khaber, F. (2018). Mobile robot path planning using an improved ant colony optimization. International Journal of Advanced Robotic Systems, 15(3), 1–7. https://doi.org/10.1177/1729881418774673
  7. Ali, H., Gong, D., Wang, M., & Dai, X. (2020). Path Planning of Mobile Robot With Improved Ant Colony Algorithm and MDP to Produce Smooth Trajectory in Grid-Based Environment. Frontiers in Neurorobotics, 14(July), 1–13. https://doi.org/10.3389/fnbot.2020.00044
  8. Dai, X., Long, S., Zhang, Z., & Gong, D. (2019). Mobile robot path planning based on ant colony algorithm with a∗ heuristic method. Frontiers in Neurorobotics, 13(April). https://doi.org/10.3389/fnbot.2019.00015
  9. Das, P. K. (2020). Hybridization of Kidney-Inspired and Sine–Cosine Algorithm for Multi-robot Path Planning. Arabian Journal for Science and Engineering, 45(4), 2883–2900. https://doi.org/10.1007/s13369-019-04193-y
  10. Dolicanin, E., Fetahovic, I., Tuba, E., Capor-Hrosik, R., & Tuba, M. (2018). Unmanned combat aerial vehicle path planning by brain storm optimization algorithm. Studies in Informatics and Control, 27(1), 15–24. https://doi.org/10.24846/v27i1y201802
  11. Elmi, Z., & Efe, M. O. (2018). Multi-objective grasshopper optimization algorithm for robot path planning in static environments. Proceedings of the IEEE International Conference on Indu