طراحی مسیر ربات‌های متحرک با ترکیب روش میدان پتانسیل مصنوعی و الگوریتم فراابتکاری شاهین
محورهای موضوعی : انرژی های تجدیدپذیر
حسین سعیدی مسینه
1
,
محمد سعادت
2
1 - گروه مهندسی مکانیک- واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف‌آباد، ایران
2 - مرکز تحقیقات فناوری‌های نوین ساخت و تولید- واحد نجف آباد، دانشگاه آزاد اسلامی، نجف‌آباد، ایران
کلید واژه: طراحی مسیر, ربات متحرک, بهینهسازی طول مسیر, حداقل محلی, روش میدان پتانسیل مصنوعی, الگوریتم شاهین,
چکیده مقاله :
طراحی مسیر ربات‌های متحرک یکی از مسائل مهم در حوزه رباتیک است. همچنین امروزه بهینه سازی طول مسیر حرکت و عبور از تله‌های حداقل محلی یکی از مشکلات اساسی و به روز در ربات‌های متحرک است. یکی از روش‌های مهم در طراحی مسیر این گونه ربات‌ها، روش میدان پتانسیل مصنوعی است زیرا مبتنی بر محاسبات ساده ریاضی است. از مهم‌ترین معایب این روش می‌توان به گیرکردن در تله‌های حداقل محلی اشاره نمود. یک رویکرد برای رفع مشکل حداقل‌های محلی، استفاده از روش های بهینه سازی برای یافتن ضرایب مناسب جذب، دفع و طول گام است که بتواند هم از حداقل های محلی عبور کند و هم طول مسیر را نیز در بهینه سازی لحاظ نماید. الگوریتم شاهین یک الگوریتم فراابتکاری قوی و جدید در حوزه بهینهسازی است که مبتنی بر جمعیت و الهام گرفته شده از زندگی شاهین‌ها در طبیعت است. این الگوریتم توانسته است برتری خود را بر روش های بهینه سازی مشابه در یافتن جواب بهینه، همگرایی سریعتر، زمان حل کمتر و دوری از حداقل های محلی اثبات نماید. از آن جایی که این روش تاکنون در طراحی مسیر ربات های متحرک مورد استفاه قرار نگرفته است، در این مقاله به منظور رفع معایب روش میدان پتانسیل مصنوعی و هم چنین بهینهسازی طول مسیر، بازده مسیریابی و زمان همگرایی از الگوریتم شاهین استفاده شده است. نتایج شبیه‌سازی حاکی از رفع معایب روش میدان پتانسیل مصنوعی و بهینه‌شدن طول مسیر حرکت، افزایش بازده مسیریابی و کاهش زمان همگرایی است.
Path planning of mobile robots is one of the important issues in the field of robotics. Also, optimizing the path length and crossing the local minima traps are the basic and up-to-date challenges in this field. One of the important methods in path planning of these robots is the artificial potential field method. Because it is based on simple mathematical calculations. One of the most important disadvantages of this method is getting trapped in local minima situations. One approach for solving the problem of local minima is to use optimization methods to find suitable coefficients (attractive and repulsive) and step length that can solve local minima and optimize the path length. The Harris Hawks algorithm is a powerful and new meta-heuristic algorithm in the field of optimization that is based on population and inspired by the life of Harris Hawks in nature. This algorithm has been able to prove its superiority over similar optimization methods in finding the optimal solution, faster convergence, lower computational time and not trapping in local minima. This method has not been used in the path planning of mobile robots. In order to eliminate the disadvantages of the artificial potential field method and to optimize the path length, the Harris Hawks algorithm has been used in this paper. The simulation results showed that the combination of the artificial potential field method and the Harris Hawks algorithm can solve the local minima problem and optimize the path length, increase the path efficiency and reduce the convergence time.
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_||_[1] J. Han, Y. Seo, "Mobile robot path planning with surrounding point set and path improvement", Applied Soft Computing, vol. 57, pp. 35–47, Aug. 2017 (doi: 10.1016/j.asoc.2017.03.035).
[2] A. Hidalgo-Paniagua, M.A. Vega-Rodríguez, J. Ferruz, "Applying the MOVNS(multi-objective variable neighborhood search) algorithm to solve the pathplanning problem in mobile robotics", Expert Systems with Applications, vol. 58, pp. 20–35, Oct. 2016 (doi: 10.1016/j.eswa.2016.03.035).
[3] E. Abbas-Nejad, A. Harifi, “Design of a sliding mode controller for two-wheeled balancing robot”, Journal of Intelligent Procedures in Electrical Technology, vol. 5, no. 19, pp. 45-54, Autumn 2014 (in Persian).
[4] P. K. Das, P.K. Jena,“Multi-robot path planning using improved particle swarm optimization algorithm through novel evolutionary operators”,Applied Soft Computing, vol. 92,pp.1-24, Jul. 2020 (doi: 10.1016/j.asoc.2020.106312).
[5] O. Khatib, "Real-time obstacle avoidance for manipulators and mobile robots," Proceedings. 1985 IEEE International Conference on Robotics and Automation, St. Louis, MO, USA, 1985, pp. 500-505, 1985 (doi: 10.1109/ROBOT.1985.1087247).
[6] B. Kovacs, “Path planning of autonomous service robots”, PhD thesis, Budapest university of engineering and technology, 2017.
[7] K.N. McGuire, G.C.H.E. de Croon, K. Tuyls, “A comparative study of bug algorithms for robot navigation”, Robotics and Autonomous Systems, vol. 121, Nov. 2019 (doi:10.1016/j.robot.2019.103261).
[8] S. Gorji, S. Zamanian, M. Moazzami, “Techno-economic and environmental base approach for optimal energy management of microgrids using crow search algorithm”, Journal of Intelligent Procedures in Electrical Technology, vol. 11, no. 43, pp. 49-68, Autumn 2020 (in Persian).
[9] A. Najar-Khoda-Bakhsh, M. Moradian, L. Najar-Khodabakhsh, N. Abjadi, “Stabilization of electromagnetic suspension system behavior by genetic algorithm”, Journal of Intelligent Procedures in Electrical Technology, vol. 3, no. 11, pp. 53-61, Summer 2013 (in Persian).
[10] Y. Gheraibia, A. Moussaoui, “Penguins search optimization algorithm (PeSOA)”, In: M. Ali, T. Bosse, K. V. Hindriks, M. Hoogendoorn, C. M. Jonker, J. Treur (eds) Recent Trends in Applied Artificial Intelligence. IEA/AIE 2013. Lecture Notes in Computer Science, vol. 7906. Springer, Berlin, Heidelberg, 2013 (doi: 10.1007/978-3-642-38577-3_23).
[11] S. A. Mirjalili, S. M. Mirjalili, A. Lewis, ”Grey wolf optimizer”, Advances in Engineering Software,vol. 69, pp. 46-61, March 2014 (doi: 10.1016/j.advengsoft.2013.12.007).
[12] S. Saremi, S. A. Mirjalili, A. Lewis, ”Grasshopper optimisation algorithm: Theory and application”, Advances in Engineering Software, vol. 105, pp. 30-47, Mar. 2017 (doi: 10.1016/j.advengsoft.2017.01.004).
[13] B. Zolghadr-Asli, O. Bozorg-Haddad, X. Chu, “Crow search algorithm (CSA)”. In: O. Bozorg-Haddad (eds) Advanced Optimization by Nature-Inspired Algorithms. Studies in Computational Intelligence, vol. 720. Springer, Singapore, 2018 (doi: 10.1007/978-981-10-5221-7_14).
[14] S. Binitha, S. S. Sathya, “A Survey of bio inspired optimization algorithms”, International Journal of Soft Computing and Engineering, vol. 2, pp. 137–151, May 2012.
[15] W. Zhang, X. Gong, G. Han, Y. Zhao, "An improved ant colony algorithm for path planning in one scenic area with many spots", IEEE Access, vol. 5, pp. 13260-13269, 2017 (doi: 10.1109/ACCESS.2017.2723892).
[16] M. A. Porta Garcia, O. Montiel, O. Castillo, R. Sepúlveda, P. Melin, “Path planningfor autonomous mobile robot navigation with ant colony optimization andfuzzy cost function evaluation”, Applied Soft Computing, vol. 9, pp. 1102–1110, Jun. 2009 (doi: 10.1016/j.asoc.2009.02.014).
[17] R. Shakiba, M. Najafipour, M. E. Salehi, "An improved PSO-based path planning algorithm for humanoid soccer playing robots", Proceeding of the IEEE/RIOS, pp. 1-6, Tehran, Iran, April 2013 (doi: 10.1109/RIOS.2013.6595312).
[18] V. Roberge, M. Tarbouchi, G. Labonte, "Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning", IEEE Trans. on Industrial Informatics, vol. 9, no. 1, pp. 132-141, Feb. 2013 (doi: 10.1109/TII.2012.2198665).
[19] L. Amador-Angulo, O. Mendoza, J. R. Castro, A. Rodriguez-Diaz, P. Melin, O.Castillo, “Fuzzy sets in dynamic adaptation of parameters of a bee colony optimization for controlling the trajectory of an autonomous mobile robot”, Sensors, vol. 16, no. 9, pp. 1–27, Sep. 2016 (doi: /doi.org/10.3390/s16091458).
[20] L. Amador-Angulo, O. Castillo, J. R. Castro, "A generalized type-2 fuzzy logic system for the dynamic adaptation the parameters in a bee colony optimization algorithm applied in an autonomous mobile robot control", Proceeding of the IEEE/FUZZ, pp. 537-544, Vancouver, BC, Canada, July 2016 (doi: 10.1109/FUZZ-IEEE.2016.7737733).
[21] U. Orozco-Rosas, O. Montiel, R. Sepúlveda. “Mobile robot path planning using membrane evolutionary artificial potential field”, Applied Soft Computing, vol. 77, pp. 236-251 , Apr. 2019 (10.1016/j.asoc.2019.01.036).
[22] A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, H. Chen, “Harris hawks optimization: Algorithm and applications”, Future Generation Computer Systems, vol. 97 , pp. 849-872, Aug. 2019 (doi: 10.1016/j.future.2019.02.028).
[23] Z. Zhou, J. Wang, Z. Zhu, D. Yang, J. Wu, “Tangent navigated robot path planning strategy using particle swarm optimized artificial potential field”, Optik, vol. 158, pp. 639-651, Apr. 2018 (doi: 10.1016/j.ijleo.2017.12.169).
