Predictive control of aerial swarms in cluttered environments

ABSTRACT Classical models of aerial swarms often describe global coordinated motion as the combination of local interactions that happen at the individual level. Mathematically, these

interactions are represented with potential fields. Despite their explanatory success, these models fail to guarantee rapid and safe collective motion when applied to aerial robotic swarms

flying in cluttered environments of the real world, such as forests and urban areas. Moreover, these models necessitate a tight coupling with the deployment scenarios to induce consistent

swarm behaviours. Here, we propose a predictive model that incorporates the local principles of potential field models in an objective function and optimizes those principles under the

knowledge of the agents’ dynamics and environment. We show that our approach improves the speed, order and safety of the swarm, it is independent of the environment layout and is scalable in

the swarm speed and inter-agent distance. Our model is validated with a swarm of five quadrotors that can successfully navigate in a real-world indoor environment populated with obstacles.

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BEING VIEWED BY OTHERS UNDERSTANDING COLLECTIVE BEHAVIOR IN BIOLOGICAL SYSTEMS THROUGH POTENTIAL FIELD MECHANISMS Article Open access 29 January 2025 SWARM NAVIGATION OF CYBORG-INSECTS IN

UNKNOWN OBSTRUCTED SOFT TERRAIN Article Open access 06 January 2025 SYNERGISTIC MORPHOLOGY AND FEEDBACK CONTROL FOR TRAVERSAL OF UNKNOWN COMPLIANT OBSTACLES WITH AERIAL ROBOTS Article Open

access 26 March 2024 DATA AVAILABILITY Complementary data for reproducing the experiments are available in the Supplementary Information. Simulation and hardware experimental data that

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ACKNOWLEDGEMENTS We thank A. De Bortoli, M. Pfister and F. Schilling for helpful discussions. This work was supported by the Swiss National Science Foundation under grant no. 200020_188457.

This work was also partially funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement ID 871479 AERIAL-CORE. AUTHOR INFORMATION AUTHORS AND

AFFILIATIONS * Laboratory of Intelligent Systems (LIS), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland Enrica Soria, Fabrizio Schiano & Dario Floreano Authors *

Enrica Soria View author publications You can also search for this author inPubMed Google Scholar * Fabrizio Schiano View author publications You can also search for this author inPubMed 

Google Scholar * Dario Floreano View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS All authors contributed to the conception of the project

and were involved in the analysis of the results. E.S. designed, implemented and performed software and hardware experiments of the NMPC algorithm for the navigation of drone swarms in

cluttered environments. All authors contributed to the writing of the manuscript. CORRESPONDING AUTHORS Correspondence to Enrica Soria, Fabrizio Schiano or Dario Floreano. ETHICS

DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PEER REVIEW INFORMATION _Nature Machine Intelligence_ thanks George Nikolakopoulos, Shan

Luo and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in

published maps and institutional affiliations. SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION SUPPLEMENTARY VIDEO 1 Simulation experiments. SUPPLEMENTARY VIDEO 2 Hardware experiments.

RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Soria, E., Schiano, F. & Floreano, D. Predictive control of aerial swarms in cluttered environments.

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