Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-11T00:28:29.608Z Has data issue: false hasContentIssue false
  • English
  • Français

Coverage control of mobile agents using multi-step broadcast control

Published online by Cambridge University Press:  28 February 2022

Shalini Darmaraju Open the ORCID record for Shalini Darmaraju [Opens in a new window] ,
Md Abdus Samad Kamal ,
Madhavan Shanmugavel  and
Chee Pin Tan
Shalini Darmaraju
Affiliation:
School of Engineering, Monash University Malaysia, Selangor, Malaysia
Md Abdus Samad Kamal
Affiliation:
Graduate School of Science and Technology, Gunma University, Gunma, Japan
Madhavan Shanmugavel
Affiliation:
Department of Mechatronics Engineering, SRM Institute of Science and Technology, Tamil Nadu, India
Chee Pin Tan*
Affiliation:
School of Engineering, Monash University Malaysia, Selangor, Malaysia
*
*Corresponding author. E-mail: tan.chee.pin@monash.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper proposes a novel multi-step broadcast control (MBC) scheme to deploy a group of autonomous mobile agents for accomplishing coverage tasks in a bounded region. Traditional broadcast control (BC) schemes use a one-to-all communication framework to transmit a uniform signal to all agents, making it cost-effective compared with any all-to-all communication-based scheme for a multi-agent system. However, as BC schemes are based on a single-step view of the environment for decision-making, the environment’s varying distribution density is not known immediately to the agents, resulting in suboptimal performance. To overcome this drawback, this paper proposes an MBC scheme, where agents use a predictive multi-step view and are able to detect the varying densities in the environment ahead of time. The local controller output is estimated using a weighted averaging technique which assigns a higher weight to immediate steps; this feature compensates for any decrease in prediction accuracy as the number of steps increases. We demonstrate the effectiveness of the proposed MBC scheme using a coverage task over a region with uneven population density. Compared to existing BC schemes, the proposed MBC scheme shows superior convergence characteristics in task accomplishment and deployment efficiency.

Keywords

broadcast controlmulti-agent systemarea coverage controlwireless coverage
Type
Research Article
Information
Robotica , Volume 40 , Issue 9 , September 2022 , pp. 3290 - 3305
Copyright
© The Author(s), 2022. 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

Lewis, F. L., Zhang, H., Hengster-Movric, K. and Das, A., Cooperative Control of Multi-Agent Systems , Communications and Control Engineering (Springer, London, 2014).Google Scholar
Cao, Y., Yu, W., Ren, W. and Chen, G., “An overview of recent progress in the study of distributed multi-agent coordination,” IEEE Trans. Ind. Inf. 9(1), 427–438 (2013).Google Scholar
Haghighi, R. and Cheah, C., “Multi-group coordination control for robot swarms,” Automatica 48(10), 2526–2534, (2012).CrossRefGoogle Scholar
Rubenstein, M., Cornejo, A. and Nagpal, R., “Robotics. Programmable self-assembly in a thousand-robot swarm,” Science (New York, NY) 345(6198), 795–799 (2014).CrossRefGoogle Scholar
Ota, J., “Multi-agent robot systems as distributed autonomous systems,” Adv. Eng. Inf. 20(1), 59–70 (2006).CrossRefGoogle Scholar
Tan, Y. and Zheng, Z.-y., “Research advance in swarm robotics,” Defence Technol. 9(1), 18–39 (2013).CrossRefGoogle Scholar
Raj, J., Raghuwaiya, K., Sharma, B. and Vanualailai, J., “Motion control of a flock of 1-trailer robots with swarm avoidance,” Robotica 39(11), 1926–1951 (2021).Google Scholar
Raj, J., Raghuwaiya, K. S. and Vanualailai, J., “Novel lyapunov-based autonomous controllers for quadrotors,” IEEE Access 8, 47393–47406 (2020).CrossRefGoogle Scholar
Pradhan, B., Nandi, A., Hui, N. B., Roy, D. S. and Rodrigues, J. J., “A novel hybrid neural network-based multirobot path planning with motion coordination,” IEEE Trans. Veh. Technol. 69(2), 1319–1327 (2019).CrossRefGoogle Scholar
Chung, S.-J., Paranjape, A. A., Dames, P., Shen, S. and Kumar, V., “A survey on aerial swarm robotics,” IEEE Trans. Rob. 34(4), 837–855 (2018).CrossRefGoogle Scholar
Xie, J. and Liu, C.-C., “Multi-agent systems and their applications,” J. Int. Council Electr. Eng. 7(1), 188–197 (2017).CrossRefGoogle Scholar
Trotta, A., Felice, M. D., Montori, F., Chowdhury, K. R. and Bononi, L., “Joint coverage, connectivity, and charging strategies for distributed UAV networks,” IEEE Trans. Rob. 34(4), 883–900 (2018).CrossRefGoogle Scholar
Rizk, Y., Awad, M. and Tunstel, E. W., “Decision making in multi agent systems: A survey,” IEEE Trans. Cognit. Dev. Syst. 10(3), 514–529 (2018).Google Scholar
Ali, M. A. and Mailah, M., “Path planning and control of mobile robot in road environments using sensor fusion and active force control,” IEEE Trans. Veh. Technol. 68(3), 2176–2195 (2019).CrossRefGoogle Scholar
Queralta, J. P., Taipalmaa, J., Pullinen, B. C., Sarker, V. K., Gia, T. N., Tenhunen, H., Gabbouj, M., Raitoharju, J. and Westerlund, T., “Collaborative multi-robot search and rescue: Planning, coordination, perception, and active vision,” IEEE Access 8, 191617–191643 (2020).CrossRefGoogle Scholar
Cortes, J., Martinez, S., Karatas, T. and Bullo, F., “Coverage control for mobile sensing networks,” IEEE Trans. Rob. Autom. 20(2), 243–255 (2004).CrossRefGoogle Scholar
Huang, S., Teo, R. S. H. and Leong, W. L., “Review of Coverage Control of Multi Unmanned Aerial Vehicles,” 2017 11th Asian Control Conference (ASCC) (2017) pp. 228–232.Google Scholar
Mohseni, F., Doustmohammadi, A. and Menhaj, M. B., “Distributed receding horizon coverage control for multiple mobile robots,” IEEE Syst. J. 10(1), 198–207 (2016).CrossRefGoogle Scholar
Schwager, M., Rus, D. and Slotine, J.-J., “Decentralized, adaptive coverage control for networked robots,” Int. J. Rob. Res. 28(3), 357–375 (2009).CrossRefGoogle Scholar
Kwok, A. and Martìnez, S., “Unicycle coverage control via hybrid modeling,” IEEE Trans. Autom. Control 55(2), 528–532 (2010).Google Scholar
Schwager, M., Rus, D. and Slotine, J.-J., “Unifying geometric, probabilistic, and potential field approaches to multi-robot deployment,” Int. J. Rob. Res. 30(3), 371–383 (2011).CrossRefGoogle Scholar
Hussein, I. I. and Stipanovic, D. M., “Effective coverage control for mobile sensor networks with guaranteed collision avoidance,” IEEE Trans. Control Syst. Technol. 15(4), 642–657 (2007).CrossRefGoogle Scholar
Ruan, L., Wang, J., Chen, J., Xu, Y., Yang, Y., Jiang, H., Zhang, Y. and Xu, Y., “Energy-efficient multi-UAV coverage deployment in UAV networks: A game-theoretic framework,” China Commun. 15(10), 194–209 (2018).CrossRefGoogle Scholar
Arslan, O., Guralnik, D. P. and Koditschek, D. E., “Coordinated robot navigation via hierarchical clustering,” IEEE Trans. Rob. 32(2), 352–371 (2016).CrossRefGoogle Scholar
Dimarogonas, D. V., Frazzoli, E. and Johansson, K. H., “Distributed event-triggered control for multi-agent systems,” IEEE Trans. Autom. Control 57(5), 1291–1297 (2011).Google Scholar
Bandyopadhyay, S., Chung, S.-J. and Hadaegh, F. Y., “Probabilistic and distributed control of a large-scale swarm of autonomous agents,” IEEE Trans. Rob. 33(5), 1103–1123 (2017).CrossRefGoogle Scholar
Li, H., Feng, C., Ehrhard, H., Shen, Y., Cobos, B., Zhang, F., Elamvazhuthi, K., Berman, S., Haber-land, M. and Bertozzi, A. L., “Decentralized Stochastic Control of Robotic Swarm Density: Theory, Simulation, and Experiment,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2017), pp. 4341–4347.Google Scholar
Elamvazhuthi, K. and Berman, S., “Optimal Control of Stochastic Coverage Strategies for Robotic Swarms,” 2015 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2015) pp. 1822–1829.CrossRefGoogle Scholar
Azuma, S.-i., Yoshimura, R. and Sugie, T., “Broadcast control of multi-agent systems,” Automatica 49(8), 2307–2316 (2013).CrossRefGoogle Scholar
Ito, Y., Kamal, M. A. S., Yoshimura, T. and Azuma, S.-i., “Pseudo-perturbation-based broadcast control of multi-agent systems,” Automatica 113, 108769 (2020).CrossRefGoogle Scholar
Fortune, S., “Voronoi Diagrams and Delaunay Triangulations,” In: Computing in Euclidean geometry (World Scientific, 1992) pp. 193–233.CrossRefGoogle Scholar
Ito, Y., Kamal, M. A. S., Yoshimura, T. and Azuma, S.-i., “Coordination of connected vehicles on merging roads using pseudo-perturbation-based broadcast control,” IEEE Trans. Intell. Transp. Syst. 20(9), 3496–3512 (2018).CrossRefGoogle Scholar
Ito, Y., Kamal, M. A. S., Yoshimura, T. and Azuma, S.-i., “Multi-vehicle Coordination on Merging Roads Based on Pseudo-Perturbation-based Broadcast Control,” Proceedings of 2018 American Control Conference (2018).CrossRefGoogle Scholar
Robinson, C., Dynamical Systems: Stability, Symbolic Dynamics, and Chaos (CRC Press, Boca Raton, FL, 1998).CrossRefGoogle Scholar
Spall, J. C., “Multivariate stochastic approximation using a simultaneous perturbation gradient approximation,” IEEE Trans. Autom. Control 37(3), 332–341 (1992).Google Scholar
Li, K., Yuan, C., Wang, J. and Dong, X., “Four-direction search scheme of path planning for mobile agents,” Robotica 38(3), 531–540 (2020).CrossRefGoogle Scholar
Chen, L., de Marina, H. G. and Cao, M., “Maneuvering formations of mobile agents using designed mismatched angles,” IEEE Trans. Autom. Control (2021) Early access, 17 March 2021. doi: 10.1109/TAC.2021.3066388.CrossRefGoogle Scholar
Flores-Resendiz, J. and Aranda-Bricaire, E., “A general solution to the formation control problem without collisions for first-order multi-agent systems,” Robotica 38(6), 1123–1137 (2020).CrossRefGoogle Scholar
Yousuf, B. M., Khan, A. S. and Noor, A., “Multi-agent tracking of non-holonomic mobile robots via non-singular terminal sliding mode control,” Robotica 38(11), 1984–2000 (2020).Google Scholar
Güzel, M. S., Ajabshir, V. B., Nattharith, P., Gezer, E. C. and Can, S., “A novel framework for multi-agent systems using a decentralized strategy,” Robotica 37(4), 691–707 (2019).CrossRefGoogle Scholar