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Mobil Robot Yol Planlaması ve Takip Kontrolünde Yol Doğrusallığının Etkisi

Yıl 2025, Cilt: 41 Sayı: 2, 590 - 603, 30.08.2025

Öz

Son yıllarda mobil robotlar sosyal yaşam, eğitim ve sağlık gibi sektörlerin yanı sıra endüstriyel alanda da kullanılmaya başlanmıştır. Bu nedenle mobil robotlar ve problemleri araştırmacıların ilgi odağı haline gelmiştir. Yol planlama ve takip kontrolü mobil robotların temel problemleri arasındadır. Ancak literatür genellikle algoritmik iyileştirmelere odaklanmış, uzun hesaplama süreleri ve yüksek maliyetler gibi yol tipinin neden olabileceği dezavantajları göz ardı etmiştir. Bu çalışma algoritmik iyileştirmeler yerine problemin basitleştirilmesine odaklanmış ve yol doğrusallığının bir robotun planlanan yolu takip etmesi üzerindeki etkisini incelemiştir. Bu nedenle yapay arı kolonisi algoritması ile planlanan eğri ve çizgi tipi optimal yollar için bir saf takip denetleyicisi tasarlanmış ve yol takip performansları karşılaştırılmıştır.

Kaynakça

  • Cebollada, S., Payá, L., Flores, M., Peidró, A., Reinoso, O. 2021. A State-of-the-Art Review on Mobile Robotics Tasks Using Artificial Intelligence and Visual Data. Expert Systems with Applications, 167, 114195.
  • Tzafestas, S. G. 2018. Mobile Robot Control and Navigation: A Global Overview. Journal of Intelligent & Robotic Systems, 91, 35-58.
  • Hadi N. H., Younus, K. K. 2020. Path Tracking and Backstepping Control for A Wheeled Mobile Robot (WMR) in A Slipping Environment. IOP Conference Series: Materials Science and Engineering 3rd International Conference on Engineering Sciences, November 4-6, Kerbala, Iraq, 012005.
  • Lu, S., Liu, D., Li, D., Shao, X. 2023. Enhanced Teaching-Learning-Based Optimization Algorithm for the Mobile Robot Path Planning Problem, Applied Sciences, 13(4), 2291.
  • Psotka, M., Duchoň, F., Roman, M., Michal, T., Michal, D. 2023. Global Path Planning Method Based on A Modification of the Wavefront Algorithm for Ground Mobile Robots, Robotics, 12(1), 25.
  • Ali, S., Yonan, J., Alniemi, O., Ahmed, A. 2022. Mobile Robot Path Planning Optimization Based on Integration of Firefly Algorithm and Cubic Polynomial Equation, Journal of ICT Research and Applications, 16(1), 1-22.
  • Wang, W., Ng, C., Chen, R. 2022. Vision-Aided Path Planning Using Low-Cost Gene Encoding for A Mobile Robot, Intelligent Automation & Soft Computing, 32(2), 991-1006.
  • Abed, B., Jasim, W. 2022. Hybrid Approach for Multi-Objective Optimization Path Planning with Moving Target, Indonesian Journal of Electrical Engineering and Computer Science, 29(1), 348-357.
  • Qiao, B., Wu, W., Deng, Y. 2022. A Path Planning Algorithm Based on Deep Reinforcement Learning for Mobile Robots in Unknown Environment, IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference, December 16-18, Chongqing, China, 1661-1666.
  • Li, J., Cai, L. 2022. Research on Robot Path Planning Based on Machine Vision, International Conference on Electronic Information Technology, March 18-20, Chengdu, China, 671-676.
  • Yanhua, C., Fenggang, L., Bei, Y. 2022. Research on Path Planning Method of Mobile Robot Based on Improved Spider Swarm Algorithm, 5th International Conference on Machine Vision and Applications, February 18-20, Singapore, 123-128.
  • Alabdalbari, A., Abed, I. 2022. New Robot Path Planning Optimization Using Hybrid GWO-PSO Algorithm, Bulletin of Electrical Engineering and Informatics, 11(3), 1289-1296. Yildirim, M. Y., Akay, R. 2021. Fast Path Planning in Multi-Obstacle Environments for Mobile Robots, Journal of the Faculty of Engineering and Architecture of Gazi University, 36(3), 1551-1564.
  • Akay, R., Yildirim, M. Y. 2023. Multi-strategy and Self-Adaptive Differential Sine-Cosine Algorithm for Multi-Robot Path Planning, Expert Systems with Applications, 232, 120849.
  • Galarza-Falfan, J., García-Guerrero, E. E., Aguirre-Castro, O. A., López-Bonilla, O. R., Tamayo-Pérez, U. J., Cárdenas-Valdez, J. R., Hernández-Mejía, C., Borrego-Dominguez, S., Inzunza-Gonzalez, E. 2024. Path Planning for Autonomous Mobile Robot Using Intelligent Algorithms. Technologies, 12(6), 82.
  • Li, P., Chen, D., Wang, Y., Zhang, L., Zhao, S. 2024. Path Planning of Mobile Robot Based on Improved TD3 Algorithm in Dynamic Environment. Heliyon, 10(11), e32167.
  • Wang, L., Chen, Z., Zhu, W. 2022. An Improved Pure Pursuit Path Tracking Control Method Based on Heading Error Rate. Ind. Robot, 49, 973-980.
  • Nawawi, S., Abdeltawab, A., Samsuria, N., Sirkunan, N. 2022. Modelling, Simulation and Navigation of a Two-Wheel Mobile Robot Using Pure Pursuit Controller, ELEKTRIKA-Journal of Electrical Engineering, 21(3), 69-75.
  • Anurag, R., Jisha, V. 2022. Performance Analysis of Path Tracking Controllers for Robotaxi Manoeuvres, IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, March 10-12, Thiruvananthapuram, India, 152-157.
  • Zhao, T., Qin, P., Zhong, Y. 2023. Trajectory Tracking Control Method for Omnidirectional Mobile Robot Based on Self-Organizing Fuzzy Neural Network and Preview Strategy, Entropy, 25(2), 248.
  • Shi, Q. 2022. Trajectory Tracking Control Method of Mobile Robot Based on AVRX Operating System, Journal of Computational Methods in Sciences and Engineering, 23(1), 253-265.
  • Mérida-Calvo, L., Rodríguez, A., Ramos, F., Feliu-Batlle, V. 2022. Advanced Motor Control for Improving the Trajectory Tracking Accuracy of a Low-Cost Mobile Robot, Machines, 11(1), 14.
  • Zeng, H., Di, Y. 2022. Trajectory Tracking Control of Mobile Robot Based on Interval Type II Fuzzy Sliding Mode, IEEE 2nd International Conference on Electronic Technology, Communication and Information, May 27-29, Changchun, China, 1420-1425.
  • Xu, L., Du, J., Song, B., Cao, M. 2022. A Combined Backstepping and Fractional-Order PID Controller to Trajectory Tracking of Mobile Robots, Systems Science & Control Engineering, 10, 134-141.
  • Fadlo, S., Elmahjoub, A., Rabbah, N. 2022. Optimal Trajectory Tracking Control for A Wheeled Mobile Robot Using Backstepping Technique, International Journal of Electrical and Computer Engineering, 12(6), 5979-5987.
  • Zaman, M., Wu, H. 2022. Fuzzy Reinforcement Learning Based Trajectory-Tracking Control of an Autonomous Mobile Robot, 22nd International Conference on Control, Automation and Systems, November 27-December 1, Jeju, Korea, 840-845.
  • Zhao, Y., Ma, Y., Hu, S. 2021. USV Formation and Path-Following Control via Deep Reinforcement Learning with Random Braking. IEEE Transactions on Neural Networks and Learning Systems, 32(12), 5468-5478.
  • Yildirim, M. Y., Akay, R. 2021. Investigation of linearity in path planning of mobile robot, European Journal of Science and Technology, 24, 138-142.
  • Karaboga, D., Akay, B., Karaboga, N. 2020. A Survey on The Studies Employing Machine Learning (ML) for Enhancing Artificial Bee Colony (ABC) Optimization Algorithm, Cogent Engineering, 7(1), 1855741.
  • Saleh, R. A., Akay, R. 2021. Artificial Bee Colony Algorithm with Directed Scout”, Soft Computing, 25(21), 13567-13593.
  • Yildirim, M. Y., Akay, R. 2021. A Comparative Study of Optimization Algorithms for Global Path Planning of Mobile Robots, Sakarya University Journal of Science, 25(2), 417-428.
  • Coulter, R. C. 1992. Implementation of the Pure Pursuit Path Tracking Algorithm, Carnegie Mellon University, The Robotics Institute, 92-01.

The Effect of Path Linearity on Mobile Robot Path Planning and Tracking Control

Yıl 2025, Cilt: 41 Sayı: 2, 590 - 603, 30.08.2025

Öz

In recent years, mobile robots have begun to be used in sectors such as social life, education and health as well as in the industrial sector. For this reason, mobile robots and their problems have become the focus of attention by researchers. Path planning and tracking control are among the basic problems of mobile robots. However, the literature generally focuses on algorithmic improvements, ignoring the disadvantages that the path type can cause such as long computation times and high costs. This study focuses on the problem simplifying instead of the algorithmic improvements, and examines the effect of path linearity on a robot's tracking the planned path. Therefore, a pure pursuit controller is designed for curve and line-type optimal paths planned by artificial bee colony algorithm and path tracking performances are compared.

Kaynakça

  • Cebollada, S., Payá, L., Flores, M., Peidró, A., Reinoso, O. 2021. A State-of-the-Art Review on Mobile Robotics Tasks Using Artificial Intelligence and Visual Data. Expert Systems with Applications, 167, 114195.
  • Tzafestas, S. G. 2018. Mobile Robot Control and Navigation: A Global Overview. Journal of Intelligent & Robotic Systems, 91, 35-58.
  • Hadi N. H., Younus, K. K. 2020. Path Tracking and Backstepping Control for A Wheeled Mobile Robot (WMR) in A Slipping Environment. IOP Conference Series: Materials Science and Engineering 3rd International Conference on Engineering Sciences, November 4-6, Kerbala, Iraq, 012005.
  • Lu, S., Liu, D., Li, D., Shao, X. 2023. Enhanced Teaching-Learning-Based Optimization Algorithm for the Mobile Robot Path Planning Problem, Applied Sciences, 13(4), 2291.
  • Psotka, M., Duchoň, F., Roman, M., Michal, T., Michal, D. 2023. Global Path Planning Method Based on A Modification of the Wavefront Algorithm for Ground Mobile Robots, Robotics, 12(1), 25.
  • Ali, S., Yonan, J., Alniemi, O., Ahmed, A. 2022. Mobile Robot Path Planning Optimization Based on Integration of Firefly Algorithm and Cubic Polynomial Equation, Journal of ICT Research and Applications, 16(1), 1-22.
  • Wang, W., Ng, C., Chen, R. 2022. Vision-Aided Path Planning Using Low-Cost Gene Encoding for A Mobile Robot, Intelligent Automation & Soft Computing, 32(2), 991-1006.
  • Abed, B., Jasim, W. 2022. Hybrid Approach for Multi-Objective Optimization Path Planning with Moving Target, Indonesian Journal of Electrical Engineering and Computer Science, 29(1), 348-357.
  • Qiao, B., Wu, W., Deng, Y. 2022. A Path Planning Algorithm Based on Deep Reinforcement Learning for Mobile Robots in Unknown Environment, IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference, December 16-18, Chongqing, China, 1661-1666.
  • Li, J., Cai, L. 2022. Research on Robot Path Planning Based on Machine Vision, International Conference on Electronic Information Technology, March 18-20, Chengdu, China, 671-676.
  • Yanhua, C., Fenggang, L., Bei, Y. 2022. Research on Path Planning Method of Mobile Robot Based on Improved Spider Swarm Algorithm, 5th International Conference on Machine Vision and Applications, February 18-20, Singapore, 123-128.
  • Alabdalbari, A., Abed, I. 2022. New Robot Path Planning Optimization Using Hybrid GWO-PSO Algorithm, Bulletin of Electrical Engineering and Informatics, 11(3), 1289-1296. Yildirim, M. Y., Akay, R. 2021. Fast Path Planning in Multi-Obstacle Environments for Mobile Robots, Journal of the Faculty of Engineering and Architecture of Gazi University, 36(3), 1551-1564.
  • Akay, R., Yildirim, M. Y. 2023. Multi-strategy and Self-Adaptive Differential Sine-Cosine Algorithm for Multi-Robot Path Planning, Expert Systems with Applications, 232, 120849.
  • Galarza-Falfan, J., García-Guerrero, E. E., Aguirre-Castro, O. A., López-Bonilla, O. R., Tamayo-Pérez, U. J., Cárdenas-Valdez, J. R., Hernández-Mejía, C., Borrego-Dominguez, S., Inzunza-Gonzalez, E. 2024. Path Planning for Autonomous Mobile Robot Using Intelligent Algorithms. Technologies, 12(6), 82.
  • Li, P., Chen, D., Wang, Y., Zhang, L., Zhao, S. 2024. Path Planning of Mobile Robot Based on Improved TD3 Algorithm in Dynamic Environment. Heliyon, 10(11), e32167.
  • Wang, L., Chen, Z., Zhu, W. 2022. An Improved Pure Pursuit Path Tracking Control Method Based on Heading Error Rate. Ind. Robot, 49, 973-980.
  • Nawawi, S., Abdeltawab, A., Samsuria, N., Sirkunan, N. 2022. Modelling, Simulation and Navigation of a Two-Wheel Mobile Robot Using Pure Pursuit Controller, ELEKTRIKA-Journal of Electrical Engineering, 21(3), 69-75.
  • Anurag, R., Jisha, V. 2022. Performance Analysis of Path Tracking Controllers for Robotaxi Manoeuvres, IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, March 10-12, Thiruvananthapuram, India, 152-157.
  • Zhao, T., Qin, P., Zhong, Y. 2023. Trajectory Tracking Control Method for Omnidirectional Mobile Robot Based on Self-Organizing Fuzzy Neural Network and Preview Strategy, Entropy, 25(2), 248.
  • Shi, Q. 2022. Trajectory Tracking Control Method of Mobile Robot Based on AVRX Operating System, Journal of Computational Methods in Sciences and Engineering, 23(1), 253-265.
  • Mérida-Calvo, L., Rodríguez, A., Ramos, F., Feliu-Batlle, V. 2022. Advanced Motor Control for Improving the Trajectory Tracking Accuracy of a Low-Cost Mobile Robot, Machines, 11(1), 14.
  • Zeng, H., Di, Y. 2022. Trajectory Tracking Control of Mobile Robot Based on Interval Type II Fuzzy Sliding Mode, IEEE 2nd International Conference on Electronic Technology, Communication and Information, May 27-29, Changchun, China, 1420-1425.
  • Xu, L., Du, J., Song, B., Cao, M. 2022. A Combined Backstepping and Fractional-Order PID Controller to Trajectory Tracking of Mobile Robots, Systems Science & Control Engineering, 10, 134-141.
  • Fadlo, S., Elmahjoub, A., Rabbah, N. 2022. Optimal Trajectory Tracking Control for A Wheeled Mobile Robot Using Backstepping Technique, International Journal of Electrical and Computer Engineering, 12(6), 5979-5987.
  • Zaman, M., Wu, H. 2022. Fuzzy Reinforcement Learning Based Trajectory-Tracking Control of an Autonomous Mobile Robot, 22nd International Conference on Control, Automation and Systems, November 27-December 1, Jeju, Korea, 840-845.
  • Zhao, Y., Ma, Y., Hu, S. 2021. USV Formation and Path-Following Control via Deep Reinforcement Learning with Random Braking. IEEE Transactions on Neural Networks and Learning Systems, 32(12), 5468-5478.
  • Yildirim, M. Y., Akay, R. 2021. Investigation of linearity in path planning of mobile robot, European Journal of Science and Technology, 24, 138-142.
  • Karaboga, D., Akay, B., Karaboga, N. 2020. A Survey on The Studies Employing Machine Learning (ML) for Enhancing Artificial Bee Colony (ABC) Optimization Algorithm, Cogent Engineering, 7(1), 1855741.
  • Saleh, R. A., Akay, R. 2021. Artificial Bee Colony Algorithm with Directed Scout”, Soft Computing, 25(21), 13567-13593.
  • Yildirim, M. Y., Akay, R. 2021. A Comparative Study of Optimization Algorithms for Global Path Planning of Mobile Robots, Sakarya University Journal of Science, 25(2), 417-428.
  • Coulter, R. C. 1992. Implementation of the Pure Pursuit Path Tracking Algorithm, Carnegie Mellon University, The Robotics Institute, 92-01.
Toplam 31 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Algoritmalar ve Hesaplama Kuramı, Otomatik Yazılım Mühendisliği
Bölüm Makaleler
Yazarlar

Sertaç Savaş 0000-0001-8096-1140

Mustafa Yusuf Yıldırım 0000-0003-0302-8466

Rüştü Akay 0000-0002-3585-3332

Yayımlanma Tarihi 30 Ağustos 2025
Gönderilme Tarihi 25 Nisan 2025
Kabul Tarihi 3 Haziran 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 41 Sayı: 2

Kaynak Göster

APA Savaş, S., Yıldırım, M. Y., & Akay, R. (2025). The Effect of Path Linearity on Mobile Robot Path Planning and Tracking Control. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, 41(2), 590-603.
AMA Savaş S, Yıldırım MY, Akay R. The Effect of Path Linearity on Mobile Robot Path Planning and Tracking Control. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi. Ağustos 2025;41(2):590-603.
Chicago Savaş, Sertaç, Mustafa Yusuf Yıldırım, ve Rüştü Akay. “The Effect of Path Linearity on Mobile Robot Path Planning and Tracking Control”. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi 41, sy. 2 (Ağustos 2025): 590-603.
EndNote Savaş S, Yıldırım MY, Akay R (01 Ağustos 2025) The Effect of Path Linearity on Mobile Robot Path Planning and Tracking Control. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi 41 2 590–603.
IEEE S. Savaş, M. Y. Yıldırım, ve R. Akay, “The Effect of Path Linearity on Mobile Robot Path Planning and Tracking Control”, Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, c. 41, sy. 2, ss. 590–603, 2025.
ISNAD Savaş, Sertaç vd. “The Effect of Path Linearity on Mobile Robot Path Planning and Tracking Control”. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi 41/2 (Ağustos2025), 590-603.
JAMA Savaş S, Yıldırım MY, Akay R. The Effect of Path Linearity on Mobile Robot Path Planning and Tracking Control. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi. 2025;41:590–603.
MLA Savaş, Sertaç vd. “The Effect of Path Linearity on Mobile Robot Path Planning and Tracking Control”. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, c. 41, sy. 2, 2025, ss. 590-03.
Vancouver Savaş S, Yıldırım MY, Akay R. The Effect of Path Linearity on Mobile Robot Path Planning and Tracking Control. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi. 2025;41(2):590-603.

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