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Three-dimensional coverage path planning via viewpoint resampling and tour optimization for aerial robots

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Abstract

This paper presents a new algorithm for three-dimensional coverage path planning for autonomous structural inspection operations using aerial robots. The proposed approach is capable of computing short inspection paths via an alternating two-step optimization algorithm according to which at every iteration it attempts to find a new and improved set of viewpoints that together provide full coverage with decreased path cost. The algorithm supports the integration of multiple sensors with different fields of view, the limitations of which are respected. Both fixed-wing as well as rotorcraft aerial robot configurations are supported and their motion constraints are respected at all optimization steps, while the algorithm operates on both mesh- and occupancy map-based representations of the environment. To thoroughly evaluate this new path planning strategy, a set of large-scale simulation scenarios was considered, followed by multiple real-life experimental test-cases using both vehicle configurations.

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Acknowledgments

This work has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Grant Agreement No.644128, AEROWORKS.

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Authors and Affiliations

  1. ETH Zurich, Leonhardstrasse 21, Zurich, Switzerland

    Andreas Bircher, Mina Kamel, Kostas Alexis, Michael Burri, Philipp Oettershagen, Sammy Omari, Thomas Mantel & Roland Siegwart

  2. University of Nevada, Reno, NV, USA

    Kostas Alexis

Authors
  1. Andreas Bircher
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  2. Mina Kamel
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  3. Kostas Alexis
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  4. Michael Burri
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  5. Philipp Oettershagen
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  6. Sammy Omari
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  7. Thomas Mantel
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  8. Roland Siegwart
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Correspondence to Kostas Alexis.

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Bircher, A., Kamel, M., Alexis, K. et al. Three-dimensional coverage path planning via viewpoint resampling and tour optimization for aerial robots. Auton Robot 40, 1059–1078 (2016). https://doi.org/10.1007/s10514-015-9517-1

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