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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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Fig. 1: Experimental set-up of drone swarm flight in cluttered environments.
Fig. 2: Comparison of the PF and NMPC aerial swarms in simulation experiments.
Fig. 3: Comparison of the PF and NMPC swarm deployment in environments with different obstacle densities.
Fig. 4: Scalability of the NMPC swarm in inter-agent distance and speed.
Fig. 5: Real-world experiment with the NMPC swarm.

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.

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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.

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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.

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Correspondence to Enrica Soria, Fabrizio Schiano or Dario Floreano.

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The authors declare no competing interests.

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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.

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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

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