Enhanced Stochastic Mobility Prediction on Unstructured Terrain Using Multi-output Gaussian Processes

  • Sin Ting Lui
  • Thierry Peynot
  • Robert Fitch
  • Salah Sukkarieh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)

Abstract

Outdoor robots such as planetary rovers must be able to navigate safely and reliably in order to successfully perform missions in remote or hostile environments. Mobility prediction is critical to achieving this goal due to the inherent control uncertainty faced by robots traversing natural terrain. We propose a novel algorithm for stochastic mobility prediction based on multi-output Gaussian process regression. Our algorithm considers the correlation between heading and distance uncertainty and provides a predictive model that can easily be exploited by motion planning algorithms. We evaluate our method experimentally and report results from over 30 trials in a Mars-analogue environment that demonstrate the effectiveness of our method and illustrate the importance of mobility prediction in navigating challenging terrain.

Keywords

Mobility prediction Learning Planetary rover Stochastic motion planning 

Notes

Acknowledgments

This work was supported in part by the Australian Centre for Field Robotics (ACFR) and the NSW State Government.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Sin Ting Lui
    • 1
  • Thierry Peynot
    • 1
    • 2
  • Robert Fitch
    • 1
  • Salah Sukkarieh
    • 1
  1. 1.Australian Centre for Field RoboticsThe University of SydneySydneyAustralia
  2. 2.School of Electrical Engineering and Computer ScienceQueensland University of TechnologyBrisbaneAustralia

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