Skip to main content
SpringerLink
Log in

A Distributed Architecture for Onboard Tightly-Coupled Estimation and Predictive Control of Micro Aerial Vehicle Formations

  • Conference paper
  • First Online:
Distributed Autonomous Robotic Systems (DARS 2022)

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 28))

Included in the following conference series:

  • 119 Accesses

Abstract

This paper presents a distributed estimation and control architecture for leader-follower formations of multi-rotor micro aerial vehicles. The architecture involves multi-rate extended Kalman filtering and nonlinear model predictive control in order to optimize the system performance while satisfying various physical constraints of the vehicles, such as actuation limits, safety thresholds, and perceptual restrictions. The architecture leverages exclusively onboard sensing, computation, and communication resources, and it has been designed for enhanced robustness to perturbations thanks to its tightly-coupled components. The architecture has initially been tested and calibrated in a high-fidelity robotic simulator and then validated with a real two-vehicle system engaged in formation navigation and reconfiguration tasks. The results not only show the high formation performance of the architecture while satisfying numerous constraints, but also indicate that it is possible to achieve full navigation and coordination autonomy in presence of severe resource constraints as those characterizing micro aerial vehicles.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Ebel, H., Ardakani, E., Eberhard, P.: Distributed model predictive formation control with discretization-free path planning for transporting a load. Robot. Auton. Syst. 96, 211–223 (2017)

    Article  Google Scholar 

  2. Oh, K., Park, M., Ahn, H.: A survey of multi-agent formation control. Automatica 53, 424–440 (2015)

    Article  MathSciNet  Google Scholar 

  3. Eren, U., Prach, A., Koçer, B., Raković, S., Kayacan, E., Açıkmeşe, B.: MPC in aerospace systems: current state and opportunities. J. Guid. Control. Dyn. 40, 1541–1566 (2017)

    Article  Google Scholar 

  4. Zhou, X., et al.: Swarm of micro flying robots in the wild. Sci. Robot. 7, eabm5954 (2022)

    Google Scholar 

  5. Petráček, P., Walter, V., Báča, T., Saska, M.: Bio-inspired compact swarms of unmanned aerial vehicles without communication and external localization. Bioinspir. Biomim. 16, 026009 (2020)

    Article  Google Scholar 

  6. Alonso-Mora, J., Montijano, E., Nägeli, T., Hilliges, O., Schwager, M., Rus, D.: Distributed multi-robot formation control in dynamic environments. Auton. Robot. 43, 1079–1100 (2019)

    Article  Google Scholar 

  7. Soria, E., Schiano, F., Floreano, D.: Distributed Predictive Drone Swarms in Cluttered Environments. IEEE Robot. Autom. Lett. 7, 73–80 (2022)

    Article  Google Scholar 

  8. Allamraju, R., et al.: Active perception based formation control for multiple aerial vehicles. IEEE Robot. Autom. Lett. 4, 4491–4498 (2019)

    Article  Google Scholar 

  9. Hafez, A., Marasco, A., Givigi, S., Iskandarani, M., Yousefi, S., Rabbath, C.: Solving multi-UAV dynamic encirclement via model predictive control. IEEE Trans. Control Syst. Technol. 23, 2251–2265 (2015)

    Article  Google Scholar 

  10. Yuan, Q., Zhan, J., Li, X.: Outdoor flocking of quadcopter drones with decentralized model predictive control. Int. Soc. Autom. Trans. 71, 84–92 (2017)

    Google Scholar 

  11. Van Parys, R., Pipeleers, G.: Distributed MPC for multi-vehicle systems moving in formation. IEEE Robot. Autonom. Syst. 97, 144–152 (2017)

    Article  Google Scholar 

  12. Abichandani, P., Levin, K., Bucci, D.: Decentralized formation coordination of multiple quadcopters under communication constraints. In: IEEE International Conference on Robotics and Automation, pp. 3326–3332 (2019)

    Google Scholar 

  13. Erunsal, I., Ventura, R., Martinoli, A. NMPC for formations of multi-rotor micro aerial vehicles: an experimental approach. In: International Symposium on Experimental Robotics, pp. 449–461 (2020)

    Google Scholar 

  14. Thakur, D., Tao, Y., Li, R., Zhou, A., Kushleyev, A., Kumar, V.: Swarm of inexpensive heterogeneous micro aerial vehicles. In: International Symposium on Experimental Robotics, pp. 413–423 (2020)

    Google Scholar 

  15. Olson, E.: AprilTag: a robust and flexible visual fiducial system. In: IEEE International Conference on Robotics and Automation, pp. 3400–3407 (2011)

    Google Scholar 

  16. Fossen, T.: Handbook of marine craft hydrodynamics and motion control. John Wiley and Sons (2011)

    Google Scholar 

  17. Mahony, R., Kumar, V., Corke, P.: Multirotor aerial vehicles: modeling, estimation, and control of quadrotor. IEEE Robot. Autom. Mag. 19, 20–32 (2012)

    Article  Google Scholar 

  18. Omari, S., Hua, M., Ducard, G., Hamel, T.: Nonlinear control of VTOL UAVs incorporating flapping dynamics. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2419–2425 (2013)

    Google Scholar 

  19. Jazwinski, A.: Stochastic processes and filtering theory Jazwinski. AH Academic Press (1970)

    Google Scholar 

  20. Quan, Q.: Introduction to multicopter design and control. Springer (2017)

    Google Scholar 

  21. Dias, D.: Distributed State Estimation and Control of Autonomous Quadrotor Formations Using Exclusively Onboard Resources. (EPFL-IST PhD Thesis, No. 9224) (2019)

    Google Scholar 

  22. https://docs.px4.io/. Autopilot control. Accessed July 2022

  23. Magni, L., Scattolini, R.: Stabilizing decentralized model predictive control of nonlinear systems. Automatica 42, 1231–1236 (2006)

    Article  MathSciNet  Google Scholar 

  24. Szeliski, R.: Computer vision: algorithms and applications. Springer Science & Business Media (2010)

    Google Scholar 

  25. Gros, S., Zanon, M., Quirynen, R., Bemporad, A., Diehl, M.: From linear to nonlinear MPC: bridging the gap via the real-time iteration. Int. J. Control 93, 62–80 (2020)

    Article  MathSciNet  Google Scholar 

  26. Ferreau, H., Kraus, T., Vukov, M., Saeys, W., Diehl, M.: High-speed moving horizon estimation based on automatic code generation. In: IEEE Conference On Decision And Control, pp. 687–692 (2012)

    Google Scholar 

  27. Michel, O.: Webots: professional mobile robot simulation. Int. J. Adv. Rob. Syst. 1, 39–42 (2004)

    Google Scholar 

  28. Xu, H., et al.: Omni-swarm: a decentralized omnidirectional visual-inertial-uwb state estimation system for aerial swarms. IEEE Trans. Robot. (2022)

    Google Scholar 

  29. https://github.com/NVIDIA-AI-IOT/isaac_ros_apriltag. Accessed Sept 2022

Download references

Acknowledgment

This work has been partially sponsored by the FCT grant [PD/BD/135151/2017], the FCT doctoral program RBCog and the FCT project [UIDB/50009/2013]

Author information

Authors and Affiliations

  1. Distributed Intelligent Systems and Algorithms Laboratory, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    I. Kagan Erunsal & Alcherio Martinoli

  2. Institute for Systems and Robotics, Instituto Superior Técnico, Lisbon, Portugal

    I. Kagan Erunsal & Rodrigo Ventura

Authors
  1. I. Kagan Erunsal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rodrigo Ventura
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alcherio Martinoli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to I. Kagan Erunsal .

Editor information

Editors and Affiliations

  1. University of Franche-Comté, Montbéliard, France

    Julien Bourgeois

  2. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Jamie Paik

  3. CNRS, University of Franche-Comté, Besancon, France

    Benoît Piranda

  4. Harvard University, Boston, MA, USA

    Justin Werfel

  5. University of Bristol, Bristol, UK

    Sabine Hauert

  6. Boston University, Boston, MA, USA

    Alyssa Pierson

  7. University of Lübeck, Lubeck, Germany

    Heiko Hamann

  8. The Chinese University of Hong Kong, Shenzhen, Shenzhen, China

    Tin Lun Lam

  9. Kyoto University, Kyoto, Japan

    Fumitoshi Matsuno

  10. University of Illinois System, Urbana, IL, USA

    Negar Mehr

  11. University of Franche-Comté, Montbéliard, France

    Abdallah Makhoul

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Erunsal, I.K., Ventura, R., Martinoli, A. (2024). A Distributed Architecture for Onboard Tightly-Coupled Estimation and Predictive Control of Micro Aerial Vehicle Formations. In: Bourgeois, J., et al. Distributed Autonomous Robotic Systems. DARS 2022. Springer Proceedings in Advanced Robotics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-031-51497-5_12

Download citation

Publish with us

Policies and ethics

Search

Navigation