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Mobil Robotların Yol Planlamasında Doğrusallığın İncelenmesi

Year 2021, Issue: 24, 138 - 142, 15.04.2021
https://doi.org/10.31590/ejosat.902932

Abstract

Endüstriyel ortamlarda giderek daha fazla kullanılan mobil robotlar için yol planlanması önemli bir problemdir. Bu problem, engelli bir ortamda başlangıç düğümünden hedef düğüme kadar mesafe ve süre gibi bazı kriterler dikkate alınarak engellere çarpmadan uygun bir yolun bulunmasıdır. Alternatif yolların üretilmesinde doğrusal (düz çizgi) veya doğrusal olmayan (eğri) amaç fonksiyonlarının kullanılması performansı önemli ölçüde etkilemektedir. Bu çalışmada, küresel yol planlama için farklı zorluk derecelerine sahip ortamlarda doğrusal ve doğrusal olmayan amaç fonksiyonlarının kullanımı karşılaştırmalı incelenmiştir. Uygun yolların bulunmasında evrimsel algoritmalardan biri olan genetik algoritma (Genetic Algorithm, GA) kullanılmıştır. Simülasyon sonuçları, mobil robotların yol planlamasında doğrusal amaç fonksiyonu kullanılmasının hem mesafe hem de algoritma çalışma süresi açısından avantajlı olduğunu göstermiştir.

References

  • 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
  • Bakdi, A., Hentout, A., Boutami, H., Maoudj, A., Hachour, O., & Bouzouia, B. (2017). Optimal path planning and execution for mobile robots using genetic algorithm and adaptive fuzzy-logic control. Robotics and Autonomous Systems, 89, 95–109. https://doi.org/10.1016/j.robot.2016.12.008
  • CAI, Z., YU, L., XIAO, C., & LIU, L. (2008). Path Planning for Mobile Robots in Irregular Environment Using Immune Evolutionary Algorithm. In IFAC Proceedings Volumes (Vol. 41, Issue 2). IFAC. https://doi.org/10.3182/20080706-5-kr-1001.00395
  • Chołodowicz, E., & Figurowski, D. (2017). Mobile Robot Path Planning with Obstacle Avoidance using Particle Swarm Optimization. Pomiary Automatyka Robotyka, 21(3), 59–68. https://doi.org/10.14313/par_225/59
  • Contreras-Cruz, M. A., Ayala-Ramirez, V., & Hernandez-Belmonte, U. H. (2015). Mobile robot path planning using artificial bee colony and evolutionary programming. Applied Soft Computing, 30, 319–328. https://doi.org/10.1016/j.asoc.2015.01.067
  • Davoodi, M., Panahi, F., Mohades, A., & Hashemi, S. N. (2015). Clear and smooth path planning. Applied Soft Computing Journal, 32, 568–579. https://doi.org/10.1016/j.asoc.2015.04.017
  • Elhoseny, M., Tharwat, A., & Hassanien, A. E. (2018). Bezier Curve Based Path Planning in a Dynamic Field using Modified Genetic Algorithm. Journal of Computational Science, 25, 339–350. https://doi.org/10.1016/j.jocs.2017.08.004
  • Ergezer, H., & Leblebicioǧlu, K. (2011). Planning unmanned aerial vehicle’s path for maximum information collection using evolutionary algorithms. IFAC Proceedings Volumes (IFAC-PapersOnline), 44(1 PART 1), 5591–5596. https://doi.org/10.3182/20110828-6-IT-1002.02977
  • Heris M. K. (2020). Practical Genetic Algorithms in Python and MATLAB - Video Tutorial (URL: https://yarpiz.com/632/ypga191215-practical-genetic-algorithms-in-python-and-matlab)
  • Lamini, C., Benhlima, S., & Elbekri, A. (2018). Genetic algorithm based approach for autonomous mobile robot path planning. Procedia Computer Science, 127, 180–189. https://doi.org/10.1016/j.procs.2018.01.113
  • Liu, X., Du, X., Zhang, X., Zhu, Q., & Guizani, M. (2019). Evolution-algorithm-based unmanned aerial vehicles path planning in complex environment. Computers and Electrical Engineering, 80, 106493. https://doi.org/10.1016/j.compeleceng.2019.106493
  • MahmoudZadeh, S., Yazdani, A. M., Sammut, K., & Powers, D. M. W. (2018). Online path planning for AUV rendezvous in dynamic cluttered undersea environment using evolutionary algorithms. Applied Soft Computing Journal, 70, 929–945. https://doi.org/10.1016/j.asoc.2017.10.025
  • Mohanta, J. C., Parhi, D. R., & Patel, S. K. (2011). Path planning strategy for autonomous mobile robot navigation using Petri-GA optimisation. Computers and Electrical Engineering, 37(6), 1058–1070. https://doi.org/10.1016/j.compeleceng.2011.07.007
  • Niu, H., Ji, Z., Savvaris, A., & Tsourdos, A. (2020). Energy efficient path planning for Unmanned Surface Vehicle in spatially-temporally variant environment. Ocean Engineering, 196(April 2019). https://doi.org/10.1016/j.oceaneng.2019.106766
  • Orozco-Rosas, U., Montiel, O., & Sepúlveda, R. (2019). Mobile robot path planning using membrane evolutionary artificial potential field. Applied Soft Computing Journal, 77, 236–251. https://doi.org/10.1016/j.asoc.2019.01.036
  • Patle, B. K., Parhi, D. R. K., Jagadeesh, A., & Kashyap, S. K. (2018). Matrix-Binary Codes based Genetic Algorithm for path planning of mobile robot. Computers and Electrical Engineering, 67, 708–728. https://doi.org/10.1016/j.compeleceng.2017.12.011
  • Qu, H., Xing, K., & Alexander, T. (2013). An improved genetic algorithm with co-evolutionary strategy for global path planning of multiple mobile robots. Neurocomputing, 120, 509–517. https://doi.org/10.1016/j.neucom.2013.04.020
  • Raja, R., Dutta, A., & Venkatesh, K. S. (2015). New potential field method for rough terrain path planning using genetic algorithm for a 6-wheel rover. Robotics and Autonomous Systems, 72, 295–306. https://doi.org/10.1016/j.robot.2015.06.002
  • Saicharan, B., Tiwari, R., & Roberts, N. (2017). Multi Objective optimization based Path Planning in robotics using nature inspired algorithms: A survey. 1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, ICPEICES 2016. https://doi.org/10.1109/ICPEICES.2016.7853442
  • Sarkar, R., Barman, D., & Chowdhury, N. (2020). Domain knowledge based genetic algorithms for mobile robot path planning having single and multiple targets. Journal of King Saud University - Computer and Information Sciences, xxxx. https://doi.org/10.1016/j.jksuci.2020.10.010
  • Song, B., Wang, Z., & Zou, L. (2021). An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve. Applied Soft Computing, 100, 106960. https://doi.org/10.1016/j.asoc.2020.106960
  • Tuncer, A., & Yildirim, M. (2012). Dynamic path planning of mobile robots with improved genetic algorithm. Computers and Electrical Engineering, 38(6), 1564–1572. https://doi.org/10.1016/j.compeleceng.2012.06.016
  • Yu, X., Li, C., & Yen, G. G. (2021). A knee-guided differential evolution algorithm for unmanned aerial vehicle path planning in disaster management. Applied Soft Computing, 98, 106857. https://doi.org/10.1016/j.asoc.2020.106857
  • Yu, X., Li, C., & Zhou, J. F. (2020). A constrained differential evolution algorithm to solve UAV path planning in disaster scenarios. Knowledge-Based Systems, 204, 106209. https://doi.org/10.1016/j.knosys.2020.106209

Investigation of Linearity in Path Planning of Mobile Robot

Year 2021, Issue: 24, 138 - 142, 15.04.2021
https://doi.org/10.31590/ejosat.902932

Abstract

Path planning is an important problem for mobile robots, which are increasingly used in industrial environments. This problem is to find a suitable path from a starting node to a target node without colliding obstacles, taking into account some criteria such as distance and time. The use of linear (straight line) or nonlinear (curve) objective functions in the generation of alternative paths significantly affects the performance. In this study, the use of linear and nonlinear objective functions for global path planning in environments with different degrees of difficulty has been comparatively examined. Genetic algorithm (GA), one of the evolutionary algorithms, was used to find suitable paths. The simulation results showed that using the linear objective function in path planning of mobile robots is advantageous in terms of both distance and algorithm running time.

References

  • 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
  • Bakdi, A., Hentout, A., Boutami, H., Maoudj, A., Hachour, O., & Bouzouia, B. (2017). Optimal path planning and execution for mobile robots using genetic algorithm and adaptive fuzzy-logic control. Robotics and Autonomous Systems, 89, 95–109. https://doi.org/10.1016/j.robot.2016.12.008
  • CAI, Z., YU, L., XIAO, C., & LIU, L. (2008). Path Planning for Mobile Robots in Irregular Environment Using Immune Evolutionary Algorithm. In IFAC Proceedings Volumes (Vol. 41, Issue 2). IFAC. https://doi.org/10.3182/20080706-5-kr-1001.00395
  • Chołodowicz, E., & Figurowski, D. (2017). Mobile Robot Path Planning with Obstacle Avoidance using Particle Swarm Optimization. Pomiary Automatyka Robotyka, 21(3), 59–68. https://doi.org/10.14313/par_225/59
  • Contreras-Cruz, M. A., Ayala-Ramirez, V., & Hernandez-Belmonte, U. H. (2015). Mobile robot path planning using artificial bee colony and evolutionary programming. Applied Soft Computing, 30, 319–328. https://doi.org/10.1016/j.asoc.2015.01.067
  • Davoodi, M., Panahi, F., Mohades, A., & Hashemi, S. N. (2015). Clear and smooth path planning. Applied Soft Computing Journal, 32, 568–579. https://doi.org/10.1016/j.asoc.2015.04.017
  • Elhoseny, M., Tharwat, A., & Hassanien, A. E. (2018). Bezier Curve Based Path Planning in a Dynamic Field using Modified Genetic Algorithm. Journal of Computational Science, 25, 339–350. https://doi.org/10.1016/j.jocs.2017.08.004
  • Ergezer, H., & Leblebicioǧlu, K. (2011). Planning unmanned aerial vehicle’s path for maximum information collection using evolutionary algorithms. IFAC Proceedings Volumes (IFAC-PapersOnline), 44(1 PART 1), 5591–5596. https://doi.org/10.3182/20110828-6-IT-1002.02977
  • Heris M. K. (2020). Practical Genetic Algorithms in Python and MATLAB - Video Tutorial (URL: https://yarpiz.com/632/ypga191215-practical-genetic-algorithms-in-python-and-matlab)
  • Lamini, C., Benhlima, S., & Elbekri, A. (2018). Genetic algorithm based approach for autonomous mobile robot path planning. Procedia Computer Science, 127, 180–189. https://doi.org/10.1016/j.procs.2018.01.113
  • Liu, X., Du, X., Zhang, X., Zhu, Q., & Guizani, M. (2019). Evolution-algorithm-based unmanned aerial vehicles path planning in complex environment. Computers and Electrical Engineering, 80, 106493. https://doi.org/10.1016/j.compeleceng.2019.106493
  • MahmoudZadeh, S., Yazdani, A. M., Sammut, K., & Powers, D. M. W. (2018). Online path planning for AUV rendezvous in dynamic cluttered undersea environment using evolutionary algorithms. Applied Soft Computing Journal, 70, 929–945. https://doi.org/10.1016/j.asoc.2017.10.025
  • Mohanta, J. C., Parhi, D. R., & Patel, S. K. (2011). Path planning strategy for autonomous mobile robot navigation using Petri-GA optimisation. Computers and Electrical Engineering, 37(6), 1058–1070. https://doi.org/10.1016/j.compeleceng.2011.07.007
  • Niu, H., Ji, Z., Savvaris, A., & Tsourdos, A. (2020). Energy efficient path planning for Unmanned Surface Vehicle in spatially-temporally variant environment. Ocean Engineering, 196(April 2019). https://doi.org/10.1016/j.oceaneng.2019.106766
  • Orozco-Rosas, U., Montiel, O., & Sepúlveda, R. (2019). Mobile robot path planning using membrane evolutionary artificial potential field. Applied Soft Computing Journal, 77, 236–251. https://doi.org/10.1016/j.asoc.2019.01.036
  • Patle, B. K., Parhi, D. R. K., Jagadeesh, A., & Kashyap, S. K. (2018). Matrix-Binary Codes based Genetic Algorithm for path planning of mobile robot. Computers and Electrical Engineering, 67, 708–728. https://doi.org/10.1016/j.compeleceng.2017.12.011
  • Qu, H., Xing, K., & Alexander, T. (2013). An improved genetic algorithm with co-evolutionary strategy for global path planning of multiple mobile robots. Neurocomputing, 120, 509–517. https://doi.org/10.1016/j.neucom.2013.04.020
  • Raja, R., Dutta, A., & Venkatesh, K. S. (2015). New potential field method for rough terrain path planning using genetic algorithm for a 6-wheel rover. Robotics and Autonomous Systems, 72, 295–306. https://doi.org/10.1016/j.robot.2015.06.002
  • Saicharan, B., Tiwari, R., & Roberts, N. (2017). Multi Objective optimization based Path Planning in robotics using nature inspired algorithms: A survey. 1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, ICPEICES 2016. https://doi.org/10.1109/ICPEICES.2016.7853442
  • Sarkar, R., Barman, D., & Chowdhury, N. (2020). Domain knowledge based genetic algorithms for mobile robot path planning having single and multiple targets. Journal of King Saud University - Computer and Information Sciences, xxxx. https://doi.org/10.1016/j.jksuci.2020.10.010
  • Song, B., Wang, Z., & Zou, L. (2021). An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve. Applied Soft Computing, 100, 106960. https://doi.org/10.1016/j.asoc.2020.106960
  • Tuncer, A., & Yildirim, M. (2012). Dynamic path planning of mobile robots with improved genetic algorithm. Computers and Electrical Engineering, 38(6), 1564–1572. https://doi.org/10.1016/j.compeleceng.2012.06.016
  • Yu, X., Li, C., & Yen, G. G. (2021). A knee-guided differential evolution algorithm for unmanned aerial vehicle path planning in disaster management. Applied Soft Computing, 98, 106857. https://doi.org/10.1016/j.asoc.2020.106857
  • Yu, X., Li, C., & Zhou, J. F. (2020). A constrained differential evolution algorithm to solve UAV path planning in disaster scenarios. Knowledge-Based Systems, 204, 106209. https://doi.org/10.1016/j.knosys.2020.106209
There are 24 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Mustafa Yusuf Yıldırım 0000-0003-0302-8466

Rüştü Akay 0000-0002-3585-3332

Publication Date April 15, 2021
Published in Issue Year 2021 Issue: 24

Cite

APA Yıldırım, M. Y., & Akay, R. (2021). Mobil Robotların Yol Planlamasında Doğrusallığın İncelenmesi. Avrupa Bilim Ve Teknoloji Dergisi(24), 138-142. https://doi.org/10.31590/ejosat.902932