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Incremental Learning of Traversability Cost for Aerial Reconnaissance Support to Ground Units

  • Miloš PrágrEmail author
  • Petr Čížek
  • Jan Faigl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11472)

Abstract

In this paper, we address traversability cost estimation using exteroceptive and proprioceptive data collected by a team of aerial and ground vehicles. The main idea of the proposed approach is to estimate the terrain traversability cost based on the real experience of the multi-legged walking robot with traversing different terrain types. We propose to combine visual features with the real measured traversability cost based on proprioceptive signals of the utilized hexapod walking robot as a ground unit. The estimated traversability cost is augmented by extracted visual features from the onboard robot camera, and the features are utilized to extrapolate the learned traversability model for an aerial scan of new environments to assess their traversability cost. The extrapolated traversability cost can be utilized in the high-level mission planning to avoid areas that are difficult to traverse but not visited by the ground units. The proposed approach has been experimentally verified with a real hexapod walking robot in indoor and outdoor scenarios.

Notes

Acknowledgements

This work has been supported by the Czech Science Foundation (GAČR) under research Project No. 18-18858S.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Electrical EngineeringCzech Technical University in PraguePragueCzech Republic

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