Hostname: page-component-745bb68f8f-hvd4g Total loading time: 0 Render date: 2025-02-03T05:50:37.757Z Has data issue: false hasContentIssue false
  • English
  • Français

Multi-robot movement based on a new modified source-seeking algorithm

Published online by Cambridge University Press:  31 January 2025

Morsy A. Morsy Open the ORCID record for Morsy A. Morsy [Opens in a new window] ,
Humaid Eqab Al-Otaibi ,
Yasser Bin Salamah Open the ORCID record for Yasser Bin Salamah [Opens in a new window]  and
Irfan Ahmad
Morsy A. Morsy*
Affiliation:
Department of Electrical Engineering, Shaqra University, Ar. Riyadh, Saudi Arabia Department of Electronics and Communication Engineering, Ain Shams University, Cairo, Egypt
Humaid Eqab Al-Otaibi
Affiliation:
Department of Electrical Engineering, King Saud University, Ar. Riyadh, Saudi Arabia
Yasser Bin Salamah
Affiliation:
Department of Electrical Engineering, King Saud University, Ar. Riyadh, Saudi Arabia
Irfan Ahmad
Affiliation:
Department of Electrical Engineering, King Saud University, Ar. Riyadh, Saudi Arabia
*
Corresponding author: Morsy A. Morsy; Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The two main sources of difficulty for a group of mobile robots employing sensors to find a source are robot collisions and wireless ambient noise, such as light, sound, and other sounds. This paper introduces a novel approach to multi-robot system cooperation and collision avoidance: the new modified source-seeking control with noise cancelation technology. The robot team works together on an incline of a light source field; the team’s mobility is dependent upon following the upward gradient’s direction and forming a particular movement pattern. The proposed program also takes into account each robot’s size, speed limit, obstacles, and noise. The noise cancelation technique has been used to avoid the delay and false decisions to find the target point of the source. When the noise is canceled, all control inputs to the algorithm are accurate, and the feedback decision will be true. In this study, we use the MATLAB simulation tools to test the velocity, position, time delay, and performance of each robot in the used group of robots. The simulation and practical results of the robots in searching for a light source showed very satisfactory performance compared with the results in the literature.

Keywords

multi-robot systemcooperative controlcollision avoidancesource-seeking algorithmnoise cancelationperformance enhancement
Type
Research Article
Information
Robotica , First View , pp. 1 - 14
Copyright
© The Author(s), 2025. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Vikas, P. D. R., “Multi-robot path planning using a hybrid dynamic window approach and modified chaotic neural oscillator-based hyperbolic gravitational search algorithm in a complex terrain,” Intel. Serv. Robotics 16(2), 213–230 (2023). doi: 10.1007/s11370-023-00460-y.CrossRefGoogle Scholar
La, H. M. and Sheng, W., “Distributed sensor fusion for scalar field mapping using mobile sensor networks,” IEEE Trans. Cybern. 43(2), 766–778 (2013).CrossRefGoogle ScholarPubMed
Liu, Z., Wang, J., Li, J., Liu, P. and Ren, K., “A novel multiple targets detection method for service robots in the indoor complex scenes,” Intel. Serv. Robotics 16(4), 453–469 (2023). doi: 10.1007/s11370-023-00471-9.CrossRefGoogle Scholar
Secil, S. and Ozkan, M., “A collision-free path planning method for industrial robot manipulators considering safe human-robot interaction,” Intel. Serv. Robotics 16(3), 323–359 (2023). doi: 10.1007/s11370-023-00465-7.CrossRefGoogle Scholar
Eqab, H., Salamah, Y. B., Ahmad, I. and Morsy, M. A., “Development of source seeking algorithm for mobile robots,” Intel. Serv. Robotics 16(3), 393–401 (2023). doi: 10.1007/s11370-023-00470-w.CrossRefGoogle Scholar
Basiri, A., Mariani, V., Silano, G., Aatif, M., Iannelli, L. and Glielmo, L., “A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture,” J. Navig. 75(2), 364–383 (2022). doi: 10.1017/S0373463321000825.CrossRefGoogle Scholar
Sabiha, A. D., Kamel, M. A. and Said, E., “Hussein WM real-time path planning for autonomous vehicle based on teaching-learning-based optimization,” Intell. Serv. Robot. 15, 381–398 (2022). doi: 10.1007/S11370-022-00429-3/FIGURES/19.CrossRefGoogle Scholar
Elor, Y. and Bruckstein, A. M., “Two-robot source seeking with point measurements,” Theor. Comput. Sci. 457, 76–85 (2012). doi: 10.1016/j.tcs.2012.06.036.CrossRefGoogle Scholar
Vikas, , Parhi, D. R. and Kashyap, A. K., “Humanoid robot path planning using memory-based gravity search algorithm and enhanced differential evolution approach in a complex environment,” Expert. Syst. Appl. 215, 119423 (2023). doi: 10.1016/J.ESWA.2022.119423.CrossRefGoogle Scholar
Mirjalili, S. and Lewis, A., “Adaptive gbest-guided gravitational search algorithm,” Neural Comput. Appl. 25, 1569–1584 (2014). doi: 10.1007/S00521-014-1640-Y/TABLES/10.CrossRefGoogle Scholar
Xu, H., Jiang, S. and Zhang, A., “Path planning for unmanned aerial vehicle using a mix-strategy-based gravitational search algorithm,” IEEE Access 9, 57033–57045 (2021). doi: 10.1109/ACCESS.2021.3072796.CrossRefGoogle Scholar
Khan, T. A. and Ling, S. H., “A novel hybrid gravitational search particle swarm optimization algorithm,” Eng. Appl. Artif. Intell. 102, 104263 (2021). doi: 10.1016/J.ENGAPPAI.2021.104263.CrossRefGoogle Scholar
Atanasov, N. A., Leny, J. and Pappas, G. J., “Distributed algorithms for stochastic source seeking with mobile robot networks,” J. Dyn. Syst. Meas. Control 137(3), 031004 (2015).CrossRefGoogle Scholar
Li, S., Kong, R. and Guo, Y., “Cooperative distributed source seeking by multiple robots: Algorithms and experiments,” IEEE/ASME Trans. Mechatron 19(6), 1810–1820 (2014). doi: 10.1109/TMECH.2013.2295036.CrossRefGoogle Scholar
Fu, W., Qin, J., Zheng, W. X., Chen, Y. and Kang, Y., “Resilient cooperative source seeking of double-integrator multi-robot systems under deception attacks,” IEEE Trans. Ind. Electron. 68(5), 4218–4227 (2021). doi: 10.1109/TIE.2020.2987270.CrossRefGoogle Scholar
Dönmez, E., Kocamaz, A. F. and Dirik, M., “A vision-based real-time mobile robot controller design based on Gaussian function for indoor environment,” Arab. J. Sci. Eng. 43(12), 7127–7142 (2018). doi: 10.1007/s13369-017-2917-0.CrossRefGoogle Scholar
Dönmez, E. and Kocamaz, A. F., “Design of mobile robot control infrastructure based on decision trees and adaptive potential area methods,” Iran. J. Sci. Technol. Trans. Electr. Eng. 44(1), 431–448 (2020). doi: 10.1007/s40998-019-00228-0.CrossRefGoogle Scholar
Lynch, K. M. and Park, F. C.. Modern Robotics: Mechanics, Planning, and Control. 1st edition, Cambridge University Press, Cambridge, MA, (2017).CrossRefGoogle Scholar
Dirik, M., Kocamaz, F. and Dönmez, E., “Visual servoing based control methods for non-holonomic mobile robot,” J. Eng. Res. 8(2), (2020).Google Scholar
Sun, W. and Tan, Y., “Model guided extremum seeking control of electromagnetic micromirrors,” Sci. Rep. 11(1), 1–15 (2021). doi: 10.1038/s41598-021-97098-6.Google ScholarPubMed
Diachuk, M. and Easa, S. M., “Improved technique for autonomous vehicle motion planning based on integral constraints and sequential optimization,” Vehicles 4(4), 1122–1157 (2022). doi: 10.3390/VEHICLES4040060.CrossRefGoogle Scholar
González, D., Pérez, J., Milanés, V. and Nashashibi, F., “A review of motion planning techniques for automated vehicles,” IEEE Trans. Intell. Transp. Syst 17(4), 1135–1145 (2016). doi: 10.1109/TITS.2015.2498841.CrossRefGoogle Scholar
Betz, J., Zheng, H., Liniger, A., Rosolia, U., Karle, P., Behl, M., Krovi, V. and Mangharam, R., “Autonomous vehicles on the edge: A survey on autonomous vehicle racing,” IEEE Open J. Intell. Transp. Syst. 3, 458–488 (2022). doi: 10.1109/OJITS.2022.3181510.CrossRefGoogle Scholar