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Autonomous Robots

, Volume 40, Issue 7, pp 1267–1278 | Cite as

Modeling curiosity in a mobile robot for long-term autonomous exploration and monitoring

  • Yogesh GirdharEmail author
  • Gregory Dudek
Article

Abstract

This paper presents a novel approach to modeling curiosity in a mobile robot, which is useful for monitoring and adaptive data collection tasks, especially in the context of long term autonomous missions where pre-programmed missions are likely to have limited utility. We use a realtime topic modeling technique to build a semantic perception model of the environment, using which, we plan a path through the locations in the world with high semantic information content. The life-long learning behavior of the proposed perception model makes it suitable for long-term exploration missions. We validate the approach using simulated exploration experiments using aerial and underwater data, and demonstrate an implementation on the Aqua underwater robot in a variety of scenarios. We find that the proposed exploration paths that are biased towards locations with high topic perplexity, produce better terrain models with high discriminative power. Moreover, we show that the proposed algorithm implemented on Aqua robot is able to do tasks such as coral reef inspection, diver following, and sea floor exploration, without any prior training or preparation.

Keywords

Autonomous exploration Topic modeling Marine robotics Long-term autonomy 

Notes

Acknowledgments

This work was supported by the Natural Sciences and Engineering Research Council (NSERC) through the NSERC Canadian Field Robotics Network (NCFRN). Yogesh Girdhar is currently supported by the Postdoctoral Scholar Program at the Woods Hole Oceanographic Institution, with funding provided by the Devonshire Foundation and the J. Seward Johnson Fund, and FQRTN Postdoctoral Fellowship. Authors would like to thank Julian Straub for helpful discussion; Philippe Giguere, Ioannis Rekleitis, Florian Shkurti, and Juan Camillio Gamboa for help in conducting field trials.

Supplementary material

Supplementary material 1 (mp4 45134 KB)

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Applied Ocean Physics & EngineeringWoods Hole Oceanographic InstitutionWoods HoleUSA
  2. 2.School of Computer ScienceMcGill UniversityMontrealCanada

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