БІОНАТХНЕННІ МЕТОДИ ПЛАНУВАННЯ ШЛЯХУ МОБІЛЬНИХ РОБОТІВ

Автор(и)

  • Олександр Геннадійович Гурко Харківський національний автомобільно-дорожній університет, Україна
  • Володимир Олександрович Гурко Харківський національний автомобільно-дорожній університет, Україна

DOI:

https://doi.org/10.30977/BUL.2219-5548.2022.98.0.37

Ключові слова:

мобільний робот, планування шляху, методи оптимізації, біонатхненні алгоритми, алгоритми ройового інтелекту

Анотація

Біонатхненні інтелектуальні алгоритми є різновидом методів оптимізації, що знайшли широке використання у мобільній робототехніці. У даній роботі представлено огляд біонатхненних алгоритмів, що використовуються для планування шляху автономних мобільних роботів у заздалегідь невідомій місцевості. Запропоновано класифікацію біонатхненних алгоритмів оптимізації. Проведено аналіз основних алгоритмів ройового інтелекту та наведені відповідні ним псевдокоди.

Біографії авторів

Олександр Геннадійович Гурко , Харківський національний автомобільно-дорожній університет

д.т.н., проф. каф. автоматизації та комп’ютерно-інтегрованих технологій

Володимир Олександрович Гурко, Харківський національний автомобільно-дорожній університет

студент магістратури, каф. автоматизації та комп’ютерно-інтегрованих технологій

Посилання

T. T. Mac, C. Copot, D. T. Tran and R. De Key-ser. Heuristic approaches in robot path planning: A survey, Robotics and Autonomous Systems, 2016, vol. 86, pp. 13–28. https://doi.org/10.1016/j.robot.2016.08.001

K. Karur, N. Sharma, C. Dharmatti and J. E. Siegel. A survey of path planning algorithms for mobile robots, Vehicles, 2021, vol. 3, no 3, pp. 448–468. https://doi.org/10.3390/vehicles3030027

M. S. Abed, O. F. Lutfy and Q. Al-Doori. A review on path planning algorithms for mobile robots. Engineering and Technology Journal, 2021. vol. 39, no 5A, pp. 804–820. https://doi.org/10.30684/etj.v39i5a.1941

J. Kallannan. Bio-Inspired Algorithms in PID Controller Optimization. CRC Press, 2018. https://doi.org/10.1201/9780429486579

J. Ni, L. Wu, X. Fan and S. X. Yang. Bioinspired intelligent algorithm and its applications for mo-bile robot control: A survey. Computational Intel-ligence and Neuroscience, 2016, pp. 1–16. https://doi.org/10.1155/2016/3810903

M. N. Ab Wahab, S. Nefti-Meziani and A. Atyabi. A comprehensive review of swarm optimization algorithms. Plos One, 2015, vol. 10, no 5, e0122827. https://doi.org/10.1371/journal.pone.0122827

N. Adzhar, Y. Yusof and M. A. Ahmad. A review on autonomous mobile robot path planning algo-rithms. Advances in Science, Technology and Engineering Systems Journal, 2020, vol. 5, no 3, pp. 236–240. https://doi.org/10.25046/aj050330

M. N. A. Wahab, S. Nefti-Meziani, A. Atyabi. A comparative review on mobile robot path plan-ning: Classical or meta-heuristic methods? Annu-al Reviews in Control, 2020, vol. 50, pp. 233–252. https://doi.org/10.1016/j.arcontrol.2020.10.001

I. Skrjanc, G. Klancar, A. Zdesar, andS. Blazic. Wheeled Mobile Robotics: From Fundamentals Towards Autonomous Systems. Elsevier Science & Technology Books, 2017.

A. S. Matveev, A. V. Savkin, M. Hoy and C. Wang. Survey of algorithms for safe naviga-tion of mobile robots in complex environments. Safe Robot Navigation Among Moving and Steady Obstacles. Elsevier, 2016, pp. 21–49. https://doi.org/10.1016/b978-0-12-803730-0.00003-2

L. Armesto, et al. Mobile robot obstacle avoid-ance based on quasi-holonomic smooth paths. Advances in Autonomous Robotics. Springer Ber-lin Heidelberg, 2012, pp. 244–255. https://doi.org/10.1007/978-3-642-32527-4_22

A. Muhammad, A. H. A. Mohammed, and I. H. Shanono. Path planning methods for mobile robots: A systematic and bibliometric review, ELEKTRIKA-Journal of Electrical Engineering, 2020, vol. 19, no 3, pp. 14–34. https://doi.org/10.11113/elektrika.v19n3.225

D. Molina, et al. Comprehensive taxonomies of nature- and bio-inspired optimization: inspiration versus algorithmic behavior, critical analysis rec-ommendations. Cognitive Computation, 2020, vol. 12, no 5, pp. 897–939. https://doi.org/ Я10.1007/s12559-020-09730-8

C. Guo, H. Tang, B. Niu and C. Boon Patrick Lee. A survey of bacterial foraging optimization. Neu-rocomputing, 2021, vol. 452, pp. 728-74. https://doi.org/10.1016/j.neucom.2020.06.142

S. Das, A. Biswas and S. Dasgupta, A. Abraham. Bacterial foraging optimization algorithm: Theo-retical foundations, analysis, and applications. Foundations of Computational Intelligence. Vol. 3. Springer Berlin Heidelberg, 2009, pp. 23–55. https://doi.org/10.1007/978-3-642-01085-9_2

D. Rai and K. Tyagi. Bio-inspired optimization techniques. ACM SIGSOFT Software Engineer-ing Notes, 2013, vol. 38, no 4, pp. 1–7. https://doi.org/10.1145/2492248.2492271

T. Sarkar, et al. Application of bio-inspired opti-mization algorithms in food processing. Current Research in Food Science, 2022, vol. 5, pp. 432–450. https://doi.org/10.1016/j.crfs.2022.02.006

J. Ni, L. Wu, X. Fan and S. X. Yang. Bioinspired intelligent algorithm and its applications for mo-bile robot control: A survey. Computational Intel-ligence and Neuroscience, 2016, vol. 2016, pp. 1–16. https://doi.org/10.1155/2016/3810903

D. Simon. Biogeography-based optimization. IEEE Transactions on Evolutionary Computa-tion, 2008, vol. 12, no 6, pp. 702–713. https://doi.org/10.1109/tevc.2008.919004.

H. Burchardt, and R. Salomon. Implementation of path planning using genetic algorithms on mo-bile robots. In Proceedings of the 2006 IEEE In-ternational Conference on Evolutionary Compu-tation, Vancouver, Canada, 16–21 July 2006; pp. 1831–1836. https://doi.org/10.1109/CEC.2006.1688529

R. Deveza, D. Thiel, A. Russell and A. Mackay-Sim. Odor sensing for robot guidance. The Inter-national Journal of Robotics Research, 1994, v. 13, no 3, pp.232-239. https://doi.org/10.1177/027836499401300305

X. Chen, J. Huang. Odor source localization algo-rithms on mobile robots: A review and future out-look. Robotics and Autonomous Systems, 2019, vol. 112, pp. 123-136. https://doi.org/10.1016/j.robot.2018.11.014.

L. Wang, S. Pang and J. Li. Olfactory-based navi-gation via model-based reinforcement learning and fuzzy inference methods. In IEEE Transac-tions on Fuzzy Systems, 2021, vol. 29, no. 10, pp. 3014-3027, https://doi.org/10.1109/TFUZZ.2020.3011741

R. Gowri and R. Rathipriya. Non-swarm plant intelligence algorithm: bladderworts suction (BWS) algorithm, 2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET), 2018, pp. 1-7. https://doi.org/10.1109/ICCSDET.2018.8821225

A. Gurko and Y. Petrenko. A PSO-based control-ler tuning for a laser technical vision system. 2022 IEEE 3rd KhPI Week on Advanced Tech-nology (KhPIWeek), 2022, pp. 1-5, https://doi.org/10.1109/KhPIWeek57572.2022.9916393.

J. Kaliannan, A. Baskaran, N. Dey and A. S. Ash-our. Bio-inspired algorithms in PID controller op-timization. London: CRC Press, 2020.

C. Luo, G. E. Jan, Z. Chu and X. Li. Biologically inspired intelligence with applications on robot navigation. In Artificial Intelligence - Emerging Trends and Applications. London, United King-dom: IntechOpen, 2018 [Online]. Available: https://www.intechopen.com/chapters/61601 doi: 10.5772/intechopen.75692

Y. Liu, X. Zhang, X. Guan and D. Delahaye. Po-tential odor intensity grid based UAV path plan-ning algorithm with particle swarm optimization approach. Mathematical Problems in Engineer-ing, 2016, vol. 2016, Article ID 7802798, 16 pag-es. https://doi.org/10.1155/2016/7802798

S. Salmanpour, H. Omranpour and H. Motameni. An intelligent water drops algorithm for solving robot path planning problem. 2013 IEEE 14th In-ternational Symposium on Computational Intel-ligence and Informatics (CINTI), 2013, pp. 333-338, https://doi.org/10.1109/CINTI.2013.6705216

J. Kennedy and R. Eberhart. Particle swarm op-timization. Proceedings of ICNN'95 - Interna-tional Conference on Neural Networks, 1995, vol. 4, pp. 1942-1948. https://doi.org/10.1109/ICNN.1995.488968.

Wei Li and Gai-Yun Wang. Application of im-proved PSO in mobile robotic path planning. 2010 International Conference on Intelligent Computing and Integrated Systems, 2010, pp. 45-48. https://doi.org/10.1109/iciss.2010.5655007.

B. Song, Z. Wang and L. Zou. An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve. Applied Soft Computing, 2021, vol. 100, p. 106960. https://doi.org/10.1016/j.asoc.2020.106960.

B. Deepak and D. Parhi. PSO based path planner of an autonomous mobile robot. Open Computer Science, 2012, vol. 2, no 2, pp. 152-168. https://doi.org/10.2478/s13537-012-0009-5.

M. K. Rath and B. B. V. L. Deepak. PSO based system architecture for path planning of mobile robot in dynamic environment. 2015 Global Conference on Communication Technologies (GCCT), 2015, pp. 797-801, https://doi.org/10.1109/ GCCT.2015.7342773.

F. H. Ajeil, I. K. Ibraheem, M. A. Sahib and A. J. Humaidi. Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm. Applied Soft Com-puting, 2020, vol. 89, p. 106076. https://doi.org/10.1016/j.asoc.2020.106076.

W. Jatmiko, K. Sekiyama and T. Fukuda. A pso-based mobile robot for odor source localization in dynamic advection-diffusion with obstacles envi-ronment: theory, simulation and measurement. In IEEE Computational Intelligence Magazine, 2007, vol. 2, no. 2, pp. 37-51. https://doi.org/10.1109/MCI.2007.353419.

M. Dorigo and T. Stützle. Ant colony optimiza-tion: Overview and recent advances. In Hand-book of Metaheuristics. Boston, MA: Springer US, 2010, pp. 227–263. https://doi.org/10.1007/978-1-4419-1665-5_8.

P. Stodola, J. Mazal, M. Podhorec and O. Litvaj. Using the Ant Colony Optimization algorithm for the Capacitated Vehicle Routing Problem. Pro-ceedings of the 16th International Conference on Mechatronics - Mechatronika 2014, 2014, pp. 503-510, https://doi.org/10.1109/MECHATRONIKA.2014.7018311

Yee Zi Cong and S. G. Ponnambalam. Mobile robot path planning using ant colony optimiza-tion. 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2009, pp. 851-856, https://doi.org/10.1109/AIM.2009.5229903.

Sh. Li, G. Zhao and W. Yue. Research on path planning for mobile robot based on improved ant colony algorithm. Journal of Physics: Confer-ence Series, 2021, vol. 2026, no. 1, p. 012049. https://doi.org/10.1088/1742-6596/2026/1/012049.

K. Akka and F. Khaber. Mobile robot path plan-ning using an improved ant colony optimization. International Journal of Advanced Robotic Sys-tems, 2018, vol. 15, no 3. https://doi.org/10.1177/1729881418774673.

X. Dai, S. Long, Z. Zhang and D. Gong. Mobile robot path planning based on ant colony algo-rithm with A* heuristic method. Frontiers in neu-rorobotics, 2019, vol. 13, article 15. https://doi.org/10.3389/fnbot.2019.00015.

X.-S. Yang. A new metaheuristic bat-inspired algorithm. In Nature Inspired Cooperative Strat-egies for Optimization (NICSO 2010). Springer Berlin Heidelberg, 2010, pp. 65–74. https://doi.org/10.1007/978-3-642-12538-6_6.

P. Suárez, A. Iglesias and A. Gálvez. Make robots be bats: specializing robotic swarms to the bat al-gorithm. Swarm and Evolutionary Computation, 2019, vol. 44, pp. 113-129. https://doi.org/10.1016/j.swevo.2018.01.005.

J. Perez, P. Et al. Trajectory optimization for an autonomous mobile robot using the bat algo-rithm. In Fuzzy Logic in Intelligent System De-sign. Cham: Springer International Publishing, 2017, pp. 232–241. https://doi.org/10.1007/978-3-319-67137-6_25.

F. H. Ajeil, I. K. Ibraheem, A. J. Humaidi and Z. H. Khan. A novel path planning algorithm for mobile robot in dynamic environments using modified bat swarm optimization. The Journal of Engineering, 2021, no 1, pp. 37–48. https://doi.org/10.1049/tje2.12009

M.A. Contreras-Cruz, V. Ayala-Ramirez and U. H. Hernandez-Belmonte. Mobile robot path planning using artificial bee colony and evolu-tionary programming. Applied Soft Computing, 2015, vol. 30, pp. 319-328. https://doi.org/10.1016/j.asoc.2015.01.067.

Y.Y. Cui, W. Hu and A. Rahmani. Fractional-order artificial bee colony algorithm with applica-tion in robot path planning. European Journal of Operational Research, 2022. https://doi.org/10.1016/j.ejor.2022.11.007

C. B. Kalayci, O. Ertenlice, H. Akyer and H. Aygoren. An artificial bee colony algorithm with feasibility enforcement and infeasibility tolera-tion procedures for cardinality constrained port-folio optimization, Expert Systems With Applica-tions, 2017, vol. 85, pp. 61–75. https://doi.org/10.1016/j.eswa.2017.05.018

X.-S. Yang and Suash Deb. Cuckoo Search via Lévy flights. 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009, pp. 210-214. https://doi.org/10.1109/NABIC.2009. 5393690.

P. K. Mohanty and D. R. Parhi. Optimal path planning for a mobile robot using cuckoo search algorithm, Journal of Experimental & Theoreti-cal Artificial Intelligence, 2014, vol. 28, no 1-2, pp. 35–52. https://doi.org/10.1080/0952813x.2014.971442.

P. K. Mohanty and D. R. Parhi. Cuckoo search algorithm for the mobile robot navigation. In Swarm, Evolutionary, and Memetic Computing. Cham: Springer International Publishing, 2013, pp. 527–536. https://doi.org/10.1007/978-3-319-03753-0_47

K. Sharma, S. Singh and R. Doriya. Optimized cuckoo search algorithm using tournament selec-tion function for robot path planning. Interna-tional Journal of Advanced Robotic Systems, 2021, vol. 18, no 3, p. 172988142199613. https://doi.org/10.1177/1729881421996136.

X. S. Yang and S. Deb. Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimi-sation, 2010, vol. 1, no 4, pp. 330. https://doi.org/10.1504/ijmmno.2010.035430.

S. Mirjalili, S. M. Mirjalili and A. Lewis. Grey wolf optimizer. Advances in Engineering Software, 2014, vol. 69, pp. 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007.

A. Mallikarjuna Rao, K. Ramji and T. Naveen Kumar. Intelligent navigation of mobile robot us-ing grey wolf colony optimization. Materials To-day: Proceedings, 2018, vol. 5, no 9, pp. 19116–19125. https://doi.org/10.1016/j.matpr.2018.06.265

M. Petrović, A. Jokić, Z. Miljković and Z. Kulesza. Multi-objective scheduling of single mobile robot based on grey wolf optimization al-gorithm. SSRN Electronic Journal, 2022. 29 p. [Online]. Available: https://doi.org/10.2139/ssrn.4058009

L. Doğan and U. Yüzgeç. Robot path planning using gray wolf optimizer. International Confer-ence on Advanced Technologies, Computer En-gineering and Science (ICATCES’18). [Online]. Available: http://indexive.com/uploads/papers/icatces2018-17.pdf

E. Malayjerdi, M. Yaghoobi and M. Kardan. Mobile robot navigation based on fuzzy cogni-tive map optimized with grey wolf optimization algorithm used in augmented reality. In 2017 5th RSI International Conference on Robotics and Mechatronics (ICRoM), Tehran, Iran, 25–27 Oct. 2017. IEEE. [Online]. Available: https://doi.org/10.1109/ icrom.2017.8466169

R. Kumar, L. Singh and R. Tiwari. Path planning for the autonomous robots using modified grey wolf optimization approach. Journal of Intelli-gent & Fuzzy Systems, 2021, vol. 40, no 5, pp. 9453–9470 https://doi.org/10.3233/jifs-201926.

##submission.downloads##

Опубліковано

2023-05-23

Номер

Розділ

АВТОМАТИЗАЦІЯ ТА КОМП'ЮТЕРНО-ІНТЕГРОВАНІ ТЕХНОЛОГІЇ