مهندسی مکانیک مدرس

مهندسی مکانیک مدرس

بهبود طراحی مسیر جمعی ربات ها به روش میدان های پتانسیل مصنوعی تطبیقی

نوع مقاله : پژوهشی اصیل

نویسندگان
مرتضی حق بیگی
اسماعیل خان میرزا
امیرحسین دوائی­ مرکزی
دانشگاه علم و صنعت ایران
10.48311/mme.24.7.455
چکیده
میدان­های پتانسیل مصنوعی یکی از روش­های پرکاربرد در طراحی مسیر پیوسته است. اما، کاربرد این روش در طراحی مسیر جمعی با چالش­هایی مواجه است که می­توان به مساله نقاط کمینه محلی و نیز ایجاد ترافیک در صورت افزایش تعداد ربات­ها اشاره کرد. هدف از روش پیشنهادی در این مقاله، بهبود طراحی مسیر جمعی در محیط­های پیچیده و با حضور تعداد متغیری ربات است. یک تابع پتانسیل تطبیقی جدید معرفی شده است که احتمال همگرایی و ورود هم­زمان ربات­ها به یک ناحیه و در نتیجه احتمال ایجاد ترافیک و نقاط کمینه محلی را کاهش می­دهد. همچنین، توابع پتانسیل جدیدی پیشنهاد شده است که منجر به مسیرهای هموارتر با زمان پیمایش کمتر، در مواجهه ربات با موانع می­شود. در این توابع، علاوه بر موقعیت ربات­ها و موانع، جهت حرکت ربات­ها و موقعیت هدف نیز در نظر گرفته شده است. به­منظور ارزیابی روش پیشنهادی، یک معماری نرم­افزاری هیبریدی طراحی و در بستر سیستم عامل رباتی پیاده­سازی شده است که در آن امکان پیوستن ربات­های جدید و یا تعریف هدف جدید، همزمان با حرکت دیگر ربات­ها وجود دارد. نتایج نشان می­دهد که استفاده از توابع پتانسیل پیشنهادی منجر به کاهش همگرایی ربات­ها و در نتیجه کاهش زمان پیمایش مسیرها می­شود. در شبیه‌سازی صورت گرفته برای 2 ربات، استفاده از توابع تطبیقی توسعه داده شده منجر با کاهش 35 درصدی در زمان پیمایش مسیرها شده است. درحالیکه در طراحی مسیر برای 15 ربات در نقشه یکسان، کاهش 50 درصدی در زمان پیمایش مسیرها حاصل شده است.
کلیدواژه‌ها
طراحی مسیر
سیستم چندرباتی
سیستم تطبیقی
میدان پتانسیل مصنوعی
معماری هیبریدی
ربات خودکار

موضوعات

عنوان مقاله English

Improving Multi-Robot Path Planning by Adaptive Artificial Potential Fields

نویسندگان English

Morteza Haghbeigi
Esmaeel Khanmirza
Amir Hossein Davaie Markazi
Iran University of Science and Technology
چکیده English

The Artificial Potential Fields approach is amongst the widely used path planning methods in continuous environments. However, the implementation of it in multi-robot path planning encounters challenges such as the local-minima and an increase in traffic probability with the rise in the number of robots. The purpose of the proposed method is to improve multi-robot path planning in complex environments. A new adaptive potential function is introduced that reduces the probability of the robots entering an area at the same time and thus reducing the probability of traffic. Also, new potential functions have been proposed that lead to smoother paths with less traverse time when the robot encounters obstacles. In these functions, in addition to the position of robots and obstacles, heading of the robot and the position of the target are also considered. In order to evaluate this method, a distributed software architecture has been designed and implemented in the framework of the robot operating system. In this architecture, as robots move, new robots can join the operation or new tasks can be assigned to robots. Two series of real-time simulations are carried out in the Gazebo environment. The results show that the use of the proposed potential functions leads to a decrease in the convergence of the robots. In the simulation done for 2 robots, proposed method has resulted in a 35% reduction in the traversal time. While in case of 15 robots in the same map, a 50% reduction in the traversal time has been achieved.

کلیدواژه‌ها English

path planning
multirobot system
adaptive system
artificial potential field
hybrid architecture
autonomous robot
F. Matoui, B. Boussaid, B. Metoui, M.N. Abdelkrim, Contribution to the path planning of a multi-robot system: centralized architecture, Intell. Serv. Robot. 13 (2020) 147–158. https://doi.org/10.1007/s11370-019-00302-w.
T. Zhao, H. Li, S. Dian, Multi-robot path planning based on improved artificial potential field and fuzzy inference system, J. Intell. Fuzzy Syst. 39 (2020) 7621–7637. https://doi.org/10.3233/JIFS-200869.
G. Klancar, A. Zdesar, S. Blazic, I. Skrjanc, Path Planning, in: Wheel. Mob. Robot., Elsevier, 2017: pp. 161–206. https://doi.org/10.1016/B978-0-12-804204-5.00004-4.
N. Zagradjanin, D. Pamucar, K. Jovanovic, Cloud-based multi-robot path planning in complex and crowded environment with multi-criteria decision making using full consistency method, Symmetry (Basel). 11 (2019). https://doi.org/10.3390/sym11101241.
F. Kendoul, Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems, J. F. Robot. 29 (2012) 315–378. https://doi.org/10.1002/rob.20414.
E. Khanmirza, M. Haghbeigi, M. Nazarahari, S. Doostie, A Comparative Study of Deterministic and Probabilistic Mobile Robot Path Planning Algorithms, 5th RSI Int. Conf. Robot. Mechatronics, IcRoM 2017. (2018) 534–539. https://doi.org/10.1109/ICRoM.2017.8466197.
D. Nikkhoe Tanha, M. Habibnejad korayem, S. fathollahi dehkordi, Path Design and Control of a Moving Social Robot in an Environment with Moving Obstacles in Order to Reach a Moving Target through Fuzzy Control, Amirkabir J. Mech. Eng. 53 (2021) 993–1014. https://doi.org/10.22060/mej.2019.16409.6362.
S.-A. Abtahi, M.A. Amiri Atashgah, B. Tarvirdizadeh, M. Shahbazi, Aerial Robotics in Urban Environments: Optimized Path Planning and SITL Assessments, in: 2023 11th RSI Int. Conf. Robot. Mechatronics, 2023: pp. 271–278. https://doi.org/10.1109/ICRoM60803.2023.10412604.
A.K. Seyed Iman Kassaei, S.I. Kassaei, A. Kosari, Aircraft Trajectory Planning with an Altitude-Bound in terrain-following flight, Modares Mech. Eng. 17 (2018) 135–144. http://mme.modares.ac.ir/article-15-7770-fa.html.
A. Analooee, S. Azadi, R. Kazemi, arezoo cadkhodajafarian, A. Analooee, S. Azadi, R. Kazemi, Collision-Free Navigation and Control for Autonomous Vehicle in Complex Urban Environments, Modares Mech. Eng. 17 (2018) 277–288. http://mme.modares.ac.ir/article-15-400-en.html.
M. Fakoor, A. Kosari, M. Jafarzadeh, Humanoid robot path planning with fuzzy Markov decision processes, J. Appl. Res. Technol. 14 (2016) 300–310. https://doi.org/https://doi.org/10.1016/j.jart.2016.06.006.
S.A.A. Moosavian, A. Daneshvar, M. Moradi, M. Morady, zero reaction path planning for mobile robot arms, Modares Mech. Eng. 11 (2011) 43–51. https://mme.modares.ac.ir/article-15-3280-en.html.
O. Khatib, Real-time obstacle avoidance for manipulators and mobile robots, in: Proceedings. 1985 IEEE Int. Conf. Robot. Autom., Institute of Electrical and Electronics Engineers, 1985: pp. 500–505. https://doi.org/10.1109/ROBOT.1985.1087247.
M.C. Lee, M.G. Park, Artificial potential field based path planning for mobile robots using a virtual obstacle concept, in: Proc. 2003 IEEE/ASME Int. Conf. Adv. Intell. Mechatronics (AIM 2003), 2003: pp. 735–740 vol.2. https://doi.org/10.1109/AIM.2003.1225434.
R. Szczepanski, A. Bereit, T. Tarczewski, Efficient Local Path Planning Algorithm Using Artificial Potential Field Supported by Augmented Reality, Energies. 14 (2021) 6642. https://doi.org/10.3390/en14206642.
A. Tahri, L. Guenfaf, Local-Minimum-Free Artificial Potential Field Method for Obstacle Avoidance, in: K. Arai (Ed.), Intell. Syst. Appl., Springer International Publishing, Cham, 2022: pp. 323–331. https://doi.org/10.1007/978-3-030-82199-9_20.
S. Tao, Improved artificial potential field method for mobile robot path planning, Appl. Comput. Eng. 33 (2024) 157–166. https://doi.org/10.54254/2755-2721/33/20230259.
S.D. Han, J. Yu, DDM: Fast Near-Optimal Multi-Robot Path Planning Using Diversified-Path and Optimal Sub-Problem Solution Database Heuristics, IEEE Robot. Autom. Lett. 5 (2020) 1350–1357. https://doi.org/10.1109/LRA.2020.2967326.
W. Zhao, R. Lin, S. Dong, W. Zhao, Y. Cheng, Dynamic node allocation-based multirobot path planning, IEEE Access. 9 (2021) 106399–106411. https://doi.org/10.1109/ACCESS.2021.3097897.
B. Tang, K. Xiang, M. Pang, Z. Zhanxia, Multi-robot path planning using an improved self-adaptive particle swarm optimization, Int. J. Adv. Robot. Syst. 17 (2020) 1–19. https://doi.org/10.1177/1729881420936154.
G. Sharon, R. Stern, A. Felner, N.R. Sturtevant, Conflict-based search for optimal multi-agent pathfinding, Artif. Intell. 219 (2015) 40–66. https://doi.org/10.1016/j.artint.2014.11.006.
A. Rathi, R. G, M. Vadali, Dynamic Prioritization for Conflict-Free Path Planning of Multi-Robot Systems, Rob. Auton. Syst. (2021). http://arxiv.org/abs/2101.01978.
P.R. Wurman, R. D’Andrea, M. Mountz, Coordinating hundreds of cooperative, autonomous vehicles in warehouses, AI Mag. 29 (2008) 9–19.
K. Sharma, R. Doriya, Coordination of multi-robot path planning for warehouse application using smart approach for identifying destinations, Intell. Serv. Robot. 14 (2021) 313–325. https://doi.org/10.1007/s11370-021-00363-w.
W. Wu, S. Bhattacharya, A. Prorok, Multi-Robot Path Deconfliction through Prioritization by Path Prospects, in: Proc. - IEEE Int. Conf. Robot. Autom., 2020: pp. 9809–9815. https://doi.org/10.1109/ICRA40945.2020.9196813.
K. Sharma, R. Doriya, Coordination of multi-robot path planning for warehouse application using smart approach for identifying destinations, Intell. Serv. Robot. 2021 142. 14 (2021) 313–325. https://doi.org/10.1007/S11370-021-00363-W.
D. Foead, A. Ghifari, M.B. Kusuma, N. Hanafiah, E. Gunawan, A Systematic Literature Review of A*Pathfinding, Procedia Comput. Sci. 179 (2021) 507–514. https://doi.org/10.1016/j.procs.2021.01.034.
G. Wagner, H. Choset, Subdimensional expansion for multirobot path planning, Artif. Intell. 219 (2015) 1–24. https://doi.org/10.1016/j.artint.2014.11.001.
T. Standley, Finding optimal solutions to cooperative pathfinding problems, in: Proc. Natl. Conf. Artif. Intell., 2010: pp. 173–178.
M. Goldenberg, A. Felner, R. Stern, G. Sharon, N. Sturtevant, R.C. Holte, J. Schaeffer, Enhanced partial expansion A*, J. Artif. Intell. Res. 50 (2014) 141–187. https://doi.org/10.1613/jair.4171.
P. Surynek, Towards optimal cooperative path planning in hard setups through satisfiability solving, in: Pacific Rim Int. Conf. Artif. Intell., Springer, 2012: pp. 564–576.
E. Erdem, D.G. Kisa, U. Oztok, P. Schüller, A general formal framework for pathfinding problems with multiple agents, Proc. 27th AAAI Conf. Artif. Intell. AAAI 2013. (2013) 290–296.
G. Sharon, R. Stern, M. Goldenberg, A. Felner, The increasing cost tree search for optimal multi-agent pathfinding, in: Artif. Intell., 2013: pp. 470–495. https://doi.org/10.1016/j.artint.2012.11.006.
G. Wagner, H. Choset, M*: A complete multirobot path planning algorithm with performance bounds, in: IEEE/RSJ Int. Conf. Intell. Robot. Syst., Institute of Electrical and Electronics Engineers (IEEE), 2011: pp. 3260–3267. https://doi.org/10.1109/iros.2011.6095022.
R. Luna, K.E. Bekris, Efficient and complete centralized multi-robot path planning, Proc. 4th Annu. Symp. Comb. Search, SoCS 2011. (2011) 201–202. https://doi.org/10.1109/iros.2011.6095085.
B. De Wilde, A.W. Ter Mors, C. Witteveen, Push and rotate: Cooperative multi-agent path planning, 12th Int. Conf. Auton. Agents Multiagent Syst. 2013, AAMAS 2013. 1 (2013) 87–94.
F. Matoui, B. Boussaid, M.N. Abdelkrim, Distributed path planning of a multi-robot system based on the neighborhood artificial potential field approach, Simulation. 95 (2019) 637–657. https://doi.org/10.1177/0037549718785440.
D. Zhang, G. Zhu, Q. Zhang, Multi-Robot Motion Planning: A Learning-Based Artificial Potential Field Solution, in: 2023 2nd Conf. Fully Actuated Syst. Theory Appl., IEEE, 2023: pp. 233–238. https://doi.org/10.1109/CFASTA57821.2023.10243195.
مهندسی مکانیک مدرس
دوره 24، شماره 7
تیر 1403
صفحه 455-466