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An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots

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Swarm Intelligence (ANTS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13491))

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Abstract

The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present sources accurately and complete the predefined inspection coverage threshold.

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Acknowledgements

We thank Dr. Harald Wild from ETH Zürich for his help with the ANSYS simulations. This work was supported by a Swiss National Science Foundation (SNSF) postdoctoral fellowship award P400P2_191116, an Office of Naval Research (ONR) grant N00014-22-1-2222, and a National Aeronautics and Space Administration (NASA) grant 80NSSC21K0353.

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Authors and Affiliations

  1. Harvard University, Boston, MA, USA

    Bahar Haghighat, Johannes Boghaert, Zev Minsky-Primus, Julia Ebert & Radhika Nagpal

  2. Princeton University, Princeton, NJ, USA

    Bahar Haghighat & Radhika Nagpal

  3. Swiss Federal Institute of Technology in Zürich (ETHZ), Zürich, Switzerland

    Johannes Boghaert

  4. Massachusetts Institute of Technology (MIT), Cambridge, MA, USA

    Fanghzheng Liu & Martin Nisser

  5. MIT Media Lab Space Exploration Initiative, Cambridge, MA, USA

    Ariel Ekblaw

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  1. Bahar Haghighat
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Correspondence to Bahar Haghighat .

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Editors and Affiliations

  1. Université Libre de Bruxelles, Brussels, Belgium

    Marco Dorigo

  2. University of Lübeck, Lübeck, Germany

    Heiko Hamann

  3. University of Manchester, Manchester, UK

    Manuel López-Ibáñez

  4. University of Málaga, Málaga, Spain

    José García-Nieto

  5. Stellenbosch University, Stellenbosch, South Africa

    Andries Engelbrecht

  6. Worcester Polytechnic Institute, Worcester, MA, USA

    Carlo Pinciroli

  7. Université Libre de Bruxelles, Brussels, Belgium

    Volker Strobel

  8. Université Libre de Bruxelles, Brussels, Belgium

    Christian Camacho-Villalón

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Haghighat, B. et al. (2022). An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2022. Lecture Notes in Computer Science, vol 13491. Springer, Cham. https://doi.org/10.1007/978-3-031-20176-9_2

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  • DOI: https://doi.org/10.1007/978-3-031-20176-9_2

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