Predictive control of aerial swarms in cluttered environments
Predictive control of aerial swarms in cluttered environments"
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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
support the findings of this study can be downloaded from ref. 54. CODE AVAILABILITY The code that supports the findings of this study can be downloaded from ref. 55. REFERENCES * Couzin, I.
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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.
_Nat Mach Intell_ 3, 545–554 (2021). https://doi.org/10.1038/s42256-021-00341-y Download citation * Received: 23 September 2020 * Accepted: 08 April 2021 * Published: 17 May 2021 * Issue
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