Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-25T13:29:29.814Z Has data issue: false hasContentIssue false
  • English
  • Français

Fast Convergence of Multi-quadrotor Cooperation Using Weighted-Neighbor-based Control

Published online by Cambridge University Press:  26 February 2021

Zhicheng Hou Open the ORCID record for Zhicheng Hou [Opens in a new window]  and
Gong Zhang
Zhicheng Hou*
Affiliation:
Guangzhou Institute of Advanced Technology, Chinese Academy of Sciences, Beijing, China E-mail: [email protected] Sorbonne universités, Université de technologie de Compiègne, CNRS, UMR 7253 Heudiasyc, 60200 Compiègne, France
Gong Zhang
Affiliation:
Guangzhou Institute of Advanced Technology, Chinese Academy of Sciences, Beijing, China E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

In this paper, a weighted-neighbor-based cooperation control of multi-quadrotor systems is investigated. A formation tracking problem is treated, where the reference formation trajectory (RFT) is not given a priori. The RFT is only available to some of the quadrotors (i.e. the leaders). In order to attain the fast convergence of the agents, we propose an algorithm to calculate the neighbors’ weights in decentralized way. Then, the weights are used to compose the formation controller. Compared to the widely used average-neighbor-based control method, the proposed control protocol can increase the convergence speed of the cooperation error. Since the formation control is improved in topological scale, the utilization of the proposed algorithms can be extended on any multi-robot systems. We show the improvement of the proposed control protocol by theoretical proof, simulation, and real-time experiments.

Keywords

Multi-robot systemsWeighted neighborsQuadrotorsFast converging
Type
Article
Information
Robotica , Volume 39 , Issue 11 , November 2021 , pp. 2061 - 2078
Copyright
© The Author(s), 2021. 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

Villa, D. K. D., BrandÃčo, A. S. and Sarcinelli-Filho, M., “Path-Following and Attitude Control of a Payload Using Multiple Quadrotors,” 2019 19th International Conference on Advanced Robotics (ICAR), Belo Horizonte, Brazil (2019) pp.535–540.Google Scholar
Wang, Z., Singh, S., Pavone, M. and Schwager, M., “Cooperative Object Transport in 3D with Multiple Quadrotors Using No Peer Communication,” 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD (2018) pp.1064–1071.Google Scholar
Michael, N., Fink, J. and Kumar, V., “Cooperative manipulation and transportation with aerial robots,” Auton. Robots 30(1), 73–86 (2011).CrossRefGoogle Scholar
Parlangeli, G. and Valcher, M. E., “Accelerating consensus in high-order leader-follower networks,” IEEE Control Syst. Lett. 2(3), 381–386 (2018).CrossRefGoogle Scholar
Sarlette, A., “Adding a single state memory optimally accelerates symmetric linear maps,” IEEE Trans. Autom. Control 61(11), 3533–3538 (2016).CrossRefGoogle Scholar
Liu, C., Zhang, H., Xiao, G. and Sun, S., “Integral reinforcement learning based decentralized optimal tracking control of unknown nonlinear large-scale interconnected systems with constrained-input,” Neurocomputing 23, 1–11 (2019).Google Scholar
Liu, Q., Wang, Z., He, X. and Zhou, D. H., “On Kalman-consensus filtering with random link failures over sensor networks,” IEEE Trans. Autom. Control 63(8), 2701–2708 (2018).CrossRefGoogle Scholar
Hou, Z. and Fantoni, I., “Interactive leader-follower consensus of multiple quadrotors based on composite nonlinear feedback control,” IEEE Trans. Control Syst. Technol. 26(5), 1732–1743 (2018).CrossRefGoogle Scholar
Kruzick, S. and Moura, J. M. F., “Optimal filter design for signal processing on random graphs: Accelerated consensus,” IEEE Trans. Signal Process. 66(5), 1258–1272 (2018).CrossRefGoogle Scholar
Xiao, L. and Boyd, S., “Fast Linear Iterations for Distributed Averaging,” 42nd IEEE International Conference on Decision and Control, Maui, HI (2003) pp. 4997–5002.Google Scholar
Duan, X., He, J., Cheng, P. and Chen, J., “Exploiting a mobile node for fast discrete time average consensus,” IEEE Trans. Control Syst. Technol. 24(6), 1993–2001 (2016).Google Scholar
Turpin, M., Michael, N. and Kumar, V., “CAPT: Concurrent assignment and planning of trajectories for multiple robots,” Int. J. Rob. Res. 33(1), 98–112 (2014).CrossRefGoogle Scholar
Grocholsky, B., Kumar, V. and Durrant-Whyte, H., “Anonymous cooperation in robotic sensor networks,” J. Biomech. 27(6), 827–833 (2004).Google Scholar
Prencipe, G. and Santoro, N., “Distributed Algorithms for Autonomous Mobile Robots,” Fourth IFIP International Conference on Theoretical Computer Science-TCS (2006) pp. 47–62.Google Scholar
Franchi, A., Oriolo, G. and Stegagno, P., “Mutual localization in multi-robot systems using anonymous relative measurements,” Int. J. Rob. Res. 11(32), 1102–1122 (2013).Google Scholar
Turpin, M., Michael, N. and Kumar, V.. “Trajectory design and control for aggressive formation flight with quadrotors,” Auton. Robots 33, 143–156 (2012).CrossRefGoogle Scholar
Falconi, R., Gowal, S. and Martinoli, A., “Graph Based Distributed Control of Non-Holonomic Vehicles Endowed with Local Positioning Information Engaged in Escorting Missions,” 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK (2010) pp. 3207–3214.Google Scholar
Shafi, S. Y., Arcak, M. and El Ghaoui, L., “Graph weight allocation to meet Laplacian spectral constraints,” IEEE Trans. Autom. Control 57(7), 1872–1877 (2012).CrossRefGoogle Scholar
Pan, L., Shao, H., Mesbahi, M., Xi, Y. and Li, D., “Bipartite consensus on matrix-valued weighted networks,” IEEE Trans. Circ. Syst. II Exp. Briefs 66(8), 1441–1445 (2019).Google Scholar
Hou, Z. and Fantoni, I., “Distributed Leader-Follower Formation Control for Multiple Quadrotors with Weighted Topology,” 2015 10th System of Systems Engineering Conference (SoSE), San Antonio, TX (2015) pp. 256–261.CrossRefGoogle Scholar
Fax, J. A. and Murray, R. M., “Information flow and cooperative control of vehicle formations,” IEEE Trans. Autom. Control 49(9), 1465–1476 (2004).CrossRefGoogle Scholar
Hou, Z. and Fantoni, I., “Composite Nonlinear Feedback-Based Bounded Formation Control of Multi-Quadrotor Systems,” 2016 European Control Conference (ECC), Aalborg (2016) pp. 1538–1543.CrossRefGoogle Scholar
Ni, W. and Cheng, D., “Leader-following consensus of multi-agent systems under fixed and switching topologies,” Syst. Control Lett. 59(3–4), 209–217 (2010).CrossRefGoogle Scholar