Cooperative Line Formation Control of Multi-Agent Systems Based on Least Squares Estimation
DOI:
https://doi.org/10.55981/jet.490Keywords:
LSE, formation control, distributed optimization, multi-agent systems,Abstract
In this paper, we consider the problem of multi-agent systems where each agent aims to establish a line formation in a distributed manner. In constructing an efficient line formation, finding a line with the closest total distance from every agent is essential. We propose a formation control using least squares estimation (LSE) performed by each agent with only the local information that consists of the corresponding agent’s and neighbors’ positions. Each agent calculates the local cost function, which is the squared distance from the LSE line to the related agent’s and its neighbors’ positions. Our goal is to minimize the global cost function, which is the sum of these local cost functions. To achieve this, we employ distributed optimization to the global cost function of the overall system using the subgradient method performed by each agent locally. We evaluate our proposed method using numerical simulation, and the result complies with our goal of this paper
Downloads
References
N. Jabeur, T. Al-Belushi, M. Mbarki, and H. Gharrad, “Toward leveraging smart logistics collaboration with a multi-agent system based solution,” Procedia Comput. Sci., vol. 109, no. 2016, pp. 672–679, 2017, doi: 10.1016/j.procs.2017.05.374. Crossref
N. R. Gans and J. G. Rogers, “Cooperative multirobot systems for military applications,” Curr. Robot. Reports, vol. 2, no. 1, pp. 105–111, 2021, doi: 10.1007/s43154-020-00039-w. Crossref
A. Degas, E. Kaddoum, M. P. Gleizes, F. Adreit, and A. Rantrua, “Cooperative multi-agent model for collision avoidance applied to air traffic management,” Eng. Appl. Artif. Intell., vol. 102, no. May, p. 104286, 2021, doi: 10.1016/j.engappai.2021.104286. Crossref
S. Chen, Y. Leng, and S. Labi, “A deep learning algorithm for simulating autonomous driving considering prior knowledge and temporal information,” Comput. Civ. Infrastruct. Eng., vol. 35, no. 4, pp. 305–321, 2020, doi: 10.1111/mice.12495. Crossref
S. He, T. Wang, and S. Wang, “Load-aware satellite handover strategy based on multi-agent reinforcement learning,” 2020 IEEE Glob. Commun. Conf. GLOBECOM 2020 - Proc., 2020, doi: 10.1109/GLOBECOM42002.2020.9322449. Crossref
L. Wang, K. Wang, C. Pan, W. Xu, N. Aslam, and L. Hanzo, “Multi-agent deep reinforcement learning-based trajectory planning for multi-uav assisted mobile edge computing,” IEEE Trans. Cogn. Commun. Netw., vol. 7, no. 1, pp. 73–84, 2021, doi: 10.1109/TCCN.2020.3027695. Crossref
F. Chen and W. Ren, “On the control of multi-agent systems: a survey,” Found. Trends Syst. Control, vol. 6, no. 4, pp. 1–164, 2019, doi: 10.1561/260-000-0019. Crossref
M. M. Gulzar, S. T. H. Rizvi, M. Y. Javed, U. Munir, and H. Asif, “Multi-agent cooperative control consensus: a comparative review,” Electron., vol. 7, no. 2, 2018, doi: 10.3390/electronics7020022. Crossref
H.-S. Ahn, Formation Control: Approaches for Distributed Agents. 2019. doi: 10.1007/978-3-030-15187-4. Crossref
Y. P. Pane, S. Fuady, and K. Mutijarsa, “Overtaking in centralized multi robot formation control based on pedestrian behavior,” Proc. - UKSim 15th Int. Conf. Comput. Model. Simulation, UKSim 2013, pp. 271–276, 2013, doi: 10.1109/UKSim.2013.146. Crossref
H. Chu, J. Chen, D. Yue, and C. Dou, “Observer-based consensus of nonlinear multiagent systems with relative state estimate constraints,” IEEE Trans. Syst. Man, Cybern. Syst., vol. 50, no. 7, pp. 2456–2465, 2020, doi: 10.1109/TSMC.2018.2818172. Crossref
Y. H. Choi and D. Kim, “Distance-based formation control with goal assignment for global asymptotic stability of multi-robot systems,” IEEE Robot. Autom. Lett., vol. 6, no. 2, pp. 2020–2027, 2021, doi: 10.1109/LRA.2021.3061071. Crossref
S. Fuady, A. R. Ibrahim, and B. R. Trilaksono, “Comparative experimental study of formation control of mobile robots,” Procedia Technol., vol. 11, no. Iceei, pp. 689–695, 2013, doi: 10.1016/j.protcy.2013.12.246. Crossref
A. Nedić and A. Ozdaglar, Cooperative distributed multi-agent optimization, vol. 9780521762. 2009. doi: 10.1017/CBO9780511804458.011. Crossref
W. Wu, I. D. Couzin, and F. Zhang, “Bio-inspired source seeking with no explicit gradient estimation,” in Proc. 3rd IFAC Work. Distrib. Estim. Control Networked Syst., 2012, vol. 45, no. 26, pp. 240–245. doi: 10.3182/20120914-2-US-4030.00024. Crossref
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:
The copyright to this article is transferred to BRIN if and when the article is accepted for publication. The undersigned hereby transfers any and all rights in and to the paper including without limitation all copyrights to BRIN. The undersigned hereby represents and warrants that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required. The undersigned represents that he/she has the power and authority to make and execute this assignment. The copyright transfer form can be downloaded here.
The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s) where applicable. After submission of this agreement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.


