Development of the scheme and improvement of the motion control method of a group of mobile robotic platforms

2023;
: 97-104
https://doi.org/10.23939/ujit2023.02.097
Received: October 20, 2023
Accepted: October 26, 2023

Цитування за ДСТУ: Цмоць І. Г., Теслюк В. М., Опотяк Ю. В., Олійник О. О. Розроблення схеми та удосконалення методу управління рухом групи мобільних робототехнічних платформ. Український журнал інформаційних технологій. 2023. Т. 5, № 2. С. 97–104.
Citation APA: Tsmots, I. G., Teslyuk, V. M., Opotiak, Yu. V., & Oliinyk, O. O. (2023). Development of the scheme and improvement of the motion control method of a group of mobile robotic platforms. Ukrainian Journal of Information Technology, 5(2), 97–104. https://doi.org/10.23939/ujit2023.02.097

1
Lviv Polytechnic National University, Lviv, Ukraine
2
Lviv Polytechnic National University, Lviv, Ukraine
3
Lviv Polytechnic National University, Department of Automated Control Systemst
4
Lviv Polytechnic National University, Department of Automated Control Systemst

When managing a group of mobile robotic platforms, there are specific tasks of ensuring operational analysis and taking into account changes in the functioning parameters of each individual platform and the impact of the surrounding environment on it and the group as a whole. It is necessary to realize not only the coordinated management of a separate robotic platform but also to ensure the interaction of separate platforms in order to fulfill the task as a whole. At the same time, it is necessary to analyze the navigational state of the surrounding environment, the composition and coordinates of the platforms in the group, to keep track of the available resources necessary for the performed task. When performing complex tasks by a group of robots, it is necessary to take into account the possibility of losing individual robot during the execution of the task and the fact that each individual robot can perform relatively simple operations, which are determined by its characteristics (radius of action, energy resource, set of executive devices). Groups of mobile robotic platforms can be homogeneous or heterogeneous, which determines the peculiarities of their management. The hybrid management method, which is a combination of centralized and distributed, in the case of heterogeneous platforms, which is most often encountered in practice, should be considered the most adequate. Under the conditions of heterogeneity of platforms in the group, control algorithms should be implemented with unconditional consideration of the features and characteristics of each individual platform. The main requirements for the hybrid management of the robots group are to ensure: effective management of the robots group in real time; respond to changes in working conditions and the surrounding environment; implement various scenarios for achieving a common goal and fulfilling a common task; scaling the number of robots that need to be managed in the group; increasing the accuracy of movement control of each robot in the group. To implement the specified tasks, the method of controlling the movement of a group of mobile robotic platforms has been improved, which, by taking into account the changing parameters of the platforms and the changing state of the surrounding environment, provides effective management of the group of platforms in real time. A generalized scheme of the group management process has been developed, which ensures the adaptation of the group management process to the changing conditions of the surrounding environment. A block diagram of the autonomous motion control algorithm of a separate mobile robotic platform has been developed, which ensures its effective functioning taking into account the variable characteristics of the platform and the state of the environment.

1. Kawashima, K., & Ogawa, T. (2012). Complex-valued neural network for group-movement control of mobile robots. Proceedings of SICE Annual Conference (SICE), Akita, Japan, 1806-1809.
2. Yuqing, Chen, Yan, Zhuang, & Wei, Wang. (2006). Cooperative Control for Formations of Mobile Robots under the Nonholonomic Constraints. 6th World Congress on Intelligent Control and Automation, Dalian, 9042-9046. 
https://doi.org/10.1109/WCICA.2006.1713749
3. Mariappan, M., Sing, J. C., Wee, C. C., Khoo, B. & Wong, W. K. (2014). Simultaneous rotation and translation movement for four omnidirectional wheels holonomic mobile robot. IEEE International Symposium on Robotics and Manufacturing Automation (ROMA), Kuala Lumpur, Malaysia, 69-73. 
https://doi.org/10.1109/ROMA.2014.7295864
4. Huang, Q. et al. (2022). Resistant Compliance Control for Biped Robot Inspired by Humanlike Behavior. IEEE/ASME Transactions on Mechatronics, 27(5), 3463-3473. 
https://doi.org/10.1109/TMECH.2021.3139332
5. Sun, C., Liu, C., Feng, X., & Jiao, X. (2021). Visual Servoing of Flying Robot Based on Fuzzy Adaptive Linear Active Disturbance Rejection Control. IEEE Transactions on Circuits and Systems II: Express Briefs, 68(7), 2558-2562. 
https://doi.org/10.1109/TCSII.2021.3053083
6. Chang, S., Du, H., Cong, Y., Xie, F., & Zhang, J. (2020). Gait Planning of Quadruped Robot Based on ROS. 7th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS), Guangzhou, China, 761-766, 
https://doi.org/10.1109/ICCSS52145.2020.9336765
7. Mehrjerdi, H., Saad, M., & Ghommam, J. (2010). Hierarchical Fuzzy Cooperative Control and Path Following for a Team of Mobile Robots. IEEE/ASME Transactions on Mechatronics, 16(5), 907-917, 
https://doi.org/10.1109/TMECH.2010.2054101
8. Zhang, H., Meng, Z., & Lin, Z. (2012). Experimental verification of a multi-robot distributed control algorithm with containment and group dispersion behaviors. Proceedings of the 31st Chinese Control Conference, Hefei, China, 6159-6164.
9. Trilaksono, B. R. (2015). Distributed consensus control of robot swarm with obstacle and collision avoidance. 2nd International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), Semarang, Indonesia, pp. 2-2, 
https://doi.org/10.1109/ICITACEE.2015.7437759
10. Chen, H., Qi, J., Dong, Y., and Zhong, S. (2021). Multi-Robot Formation Control And Implementation. 40th Chinese Control Conference (CCC), Shanghai, China, 879-884, 
https://doi.org/10.23919/CCC52363.2021.9549282
11. Wen-Ran, Zhang. (1997). Neurofuzzy agents and neurofuzzy laws for autonomous machine learning and control. Proceedings of International Conference on Neural Networks (ICNN'97), Houston, TX, USA, vol.3, pp. 1732-1737. 
https://doi.org/10.1109/ICNN.1997.614157
12. Yusof, Y., Mansor, H.M.A.H., Ahmad, A. (2016). Formulation of a lightweight hybrid ai algorithm towards self-learning autonomous systems. Proc. of the 2016 IEEE Confer. on Systems, Process and Control (IC-SPC), Melaka, Malaysia, 16 18 December 2016, pp. 142 147.
https://doi.org/10.1109/SPC.2016.7920719
13. Chen, G., Hou, B., Guo, S. & Wang, J. (2020). Dynamic Balance and Trajectory Tracking Control of Quadruped Robots Based on Virtual Model Control. 39th Chinese Control Conference (CCC), Shenyang, China, pp. 3771-3776.
https://doi.org/10.23919/CCC50068.2020.9189645
14. Reguii, I., Hassani, I., & Rekik, C. (2022). Neuro-fuzzy Control of a Mobile Robot IEEE 21st international Ccnference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), Sousse, Tunisia, pp. 45-50. 
https://doi.org/10.1109/STA56120.2022.10018999
15. Ovur, S. E. , Candan, F., Beke, A., & Kumbasar, T. (2018). YAFT: A Fuzzy Logic based Real Time Two-Wheeled Inverted Pendulum Robot. 6th International Conference on Control Engineering & Information Technology (CEIT), Istanbul, Turkey, pp. 1-6.
https://doi.org/10.1109/CEIT.2018.8751767
16. Wildani, F., Mardiati, R., Mulyana, E., Setiawan, A. E., Nurmalasari, R. R., & Sartika, N. (2022). Fuzzy Logic Control for Semi-Autonomous Navigation Robot Using Integrated Remote Control. 8th International Conference on Wireless and Telematics (ICWT), Yogyakarta, Indonesia, pp. 1-5. 
https://doi.org/10.1109/ICWT55831.2022.9935458
17. Oultiligh, A., Ayad, H., Pozna, C., Mogan, G., ELbouzekraoui, M. and Elkari, B. (2020). Obstacle Avoidance using Fuzzy Controller for Unicycle Robot. International Conference on Control, Automation and Diagnosis (ICCAD), Paris, France, pp. 1-6. 
https://doi.org/10.1109/ICCAD49821.2020.9260553
18. Gao, J., Chen, K., Wu, C. and Wang, S. (2023). Obstacle avoidance and formation transformation of multi-agent groups based on six-wheeled robot. First International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE), Shenyang, China, pp. 1-5. 
https://doi.org/10.1109/ICCSIE55183.2023.10175276
19. Zheng, S, Lin, Z., Zeng, Q., Zheng, R., Liu, C. and Xiong, H. (2018). IAPcloud: A Cloud Control Platform for Heterogeneous Robots. IEEE Access, 6, 30577-30591. 
https://doi.org/10.1109/ACCESS.2018.2837904
20. Zhang, Z., Ling, Q., & Yang, Z. (2019). Formation Control with Obstacle Avoidance of Multi-Robot Systems with Second-Order Dynamics. Chinese Control Conference (CCC), Guangzhou, China, pp. 5978-5983. 
https://doi.org/10.23919/ChiCC.2019.8866564
21. Jeong, D. B., & Ko, N. Y. (2022). Dead Reckoning of a Mobile Robot in 2-Dimensional Special Euclidean Group. 22nd International Conference on Control, Automation and Systems (ICCAS), Jeju, Korea, Republic of, pp. 1069-1071.
https://doi.org/10.23919/ICCAS55662.2022.10003795
22. Nubert, J., Köhler, J., Berenz, V., Allgöwer, F., & Trimpe, S. (2020). Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control. IEEE Robotics and Automation Letters, 5(2), pp. 3050-3057. 
https://doi.org/10.1109/LRA.2020.2975727
23. Bai, Y., Svinin, M., & Magid, E. (2020). Multi-Robot Control for Adaptive Caging and Tracking of a Flood Area. 59th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Chiang Mai, Thailand, pp. 1452-1457. 
https://doi.org/10.23919/SICE48898.2020.9240385