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Good Fences Make Good Neighbors

Using Formally Verified Safe Trajectories to Design a Predictive Geofence Algorithm

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NASA Formal Methods (NFM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12673))

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Abstract

For AI-controlled mobile platforms, avoiding collisions with walls and boundaries is an important safety requirement. This is a problem especially for fast-moving aerial vehicles, such as fixed-wing aircraft, that cannot be brought to a stop in an emergency. To enable geographic confinement of such AI-controlled vehicles, we present a formally verified algorithm for predicting geofence violations and selecting a safe maneuver that will keep the vehicle within the designated operations area. The algorithm is based on a higher-order dynamics model that generalizes circular turns using linearly changing centripetal acceleration and allows handling of uncertainty in model parameters. The proposed algorithm was implemented along with extensions to handle non-determinism, and flight-tested on an autonomous aircraft.

Defense Nuclear Facilities Safety Board—The views expressed herein are solely those of the authors, and no official support or endorsement by the Defense Nuclear Facilities Safety Board or the U.S. Government is intended or should be inferred.

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Notes

  1. 1.

    For our purposes, we will focus on geofencing for keep-in regions only.

  2. 2.

    All theorems have been formalized and verified in Coq theorem prover, and are available at https://bitbucket.org/ykouskoulas/egeof-proofs. These proofs rely on the property – admitted as an axiom – that the shortest distance between two points is a straight line.

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Acknowledgements

This work was supported by the US Air Force Research Laboratory’s Strategic Development Planning and Experimentation Office under contract number HQ0034-19-D-0006. The authors would also like to thank Dr.’s Christopher Eaton, Edward White, and Reed Young for their leadership and fostering of this work. Additionally, we thank the entire team, especially Dorothy Kirlew and Andrea Jensenius, for their dedication in making the flight-testing of this approach possible.

Author information

Authors and Affiliations

  1. Johns Hopkins University Applied Physics Laboratory, Laurel, USA

    Yanni Kouskoulas, Rosa Wu, Joshua Brulé, Daniel Genin, Aurora Schmidt & T. J. Machado

  2. Defense Nuclear Facilities Safety Board, Washington, D.C., USA

    Rosa Wu

  3. Department of Mathematics, New Mexico State University, Las Cruces, USA

    T. J. Machado

Authors
  1. Yanni Kouskoulas
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  2. Rosa Wu
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  3. Joshua Brulé
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  4. Daniel Genin
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  5. Aurora Schmidt
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  6. T. J. Machado
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Corresponding author

Correspondence to Aurora Schmidt .

Editor information

Editors and Affiliations

  1. NASA Langley Research Center, Hampton, VA, USA

    Aaron Dutle

  2. National Institute of Aerospace, Hampton, VA, USA

    Mariano M. Moscato

  3. National Institute of Aerospace, Hampton, VA, USA

    Laura Titolo

  4. NASA Langley Research Center, Hampton, VA, USA

    César A. Muñoz

  5. National Institute of Aerospace, Hampton, VA, USA

    Ivan Perez

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Kouskoulas, Y., Wu, R., Brulé, J., Genin, D., Schmidt, A., Machado, T.J. (2021). Good Fences Make Good Neighbors. In: Dutle, A., Moscato, M.M., Titolo, L., Muñoz, C.A., Perez, I. (eds) NASA Formal Methods. NFM 2021. Lecture Notes in Computer Science(), vol 12673. Springer, Cham. https://doi.org/10.1007/978-3-030-76384-8_14

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  • DOI: https://doi.org/10.1007/978-3-030-76384-8_14

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