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


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.


Autonomous exploration Topic modeling Marine robotics Long-term autonomy 



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)


  1. Bellingham, J. G., & Rajan, K. (2007). Robotics in remote and hostile environments. Science (New York, NY), 318(5853), 1098–1102.CrossRefGoogle Scholar
  2. Bender, A., Williams, S. B., & Pizarro, O. (2013). Autonomous Exploration of large-scale benthic environments. In IEEE International conference on robotics and automation (ICRA) (pp. 390–396).Google Scholar
  3. Binney, J., Krause, A., & Sukhatme, G. S. (2013). Optimizing waypoints for monitoring spatiotemporal phenomena. The International Journal of Robotics Research, 32(8), 873–888.CrossRefGoogle Scholar
  4. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. The Journal of Machine Learning Research, 3, 993–1022.zbMATHGoogle Scholar
  5. Bourgault, F., Makarenko, A. A., Williams, S. B., Grocholsky, B., & Durrant-Whyte, H. F. (2002). Information based adaptive robotic exploration. In International conference on intelligent robots and systems, 2002. IEEE/RSJ (pp. 540–545).Google Scholar
  6. Choset, H., & Philippe, P. (1998). Coverage path planning the boustrophedon cellular decomposition. Field and Service Robotics.Google Scholar
  7. Das, J., Py, F., Maughan, T., OReilly, T., Messie, M., Ryan, J., et al. (2012). Coordinated sampling of dynamic oceanographic features with underwater vehicles and drifters. The International Journal of Robotics Research, 31(5), 626–646.CrossRefGoogle Scholar
  8. Dudek, G., Giguere, P., Prahacs, C., Saunderson, S., Sattar, J., Torres-Mendez, L.-A., et al. (2007). AQUA: An amphibious autonomous robot. Computer, 40(1), 46–53.CrossRefGoogle Scholar
  9. Girdhar, Y., Giguere, P., & Dudek, G. (2013). Autonomous adaptive exploration using realtime online spatiotemporal topic modeling. The International Journal of Robotics Research, 33(4), 645–657.CrossRefGoogle Scholar
  10. Grabowski, R., Khosla, P., & Choset, H. (2003). Autonomous exploration via regions of interest. In Proceedings of the IEEE/RSJ International conference on intelligent robots and systems (IROS 2003) (IEEE, Vol. 2, pp. 1691–1696).Google Scholar
  11. Hollinger, G. A., Englot, B., Hover, F. S., Mitra, U., & Sukhatme, G. S. (2012). Active planning for underwater inspection and the benefit of adaptivity. The International Journal of Robotics Research, 32(1), 3–18.CrossRefGoogle Scholar
  12. Kollar, T., & Roy, N. (2008a). Efficient optimization of information-theoretic exploration in SLAM. In AAAI, (Vol. 8, pp. 1369–1375).Google Scholar
  13. Kollar, T., & Roy, N. (2008b). Trajectory optimization using reinforcement learning for map exploration. The International Journal of Robotics Research, 27(2), 175–197.CrossRefGoogle Scholar
  14. Lienou, M., Maitre, H., & Datcu, M. (2010). Semantic annotation of satellite images using latent dirichlet allocation. Geoscience and Remote Sensing Letters, IEEE, 7(1), 28–32.CrossRefGoogle Scholar
  15. Mannadiar, R., & Rekleitis, I. (May 2010). Optimal coverage of a known arbitrary environment. In 2010 IEEE International conference on robotics and automation (IEEE, pp. 5525–5530).Google Scholar
  16. Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (November 2011). ORB: An efficient alternative to SIFT or SURF. In 2011 International conference on computer vision (pp. 2564–2571). Barcelona, IEEE.Google Scholar
  17. Sattar, J., Dudek, G., Chiu, O., Rekleitis, I., Giguère, P., Mills, A., et al. (September 2008). Enabling autonomous capabilities in underwater robotics. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems, (IROS) (pp. 3628–3634). France: Nice.Google Scholar
  18. Sim, R., Dudek, G., & Roy, N. (2004). Online control policy optimization for minimizing map uncertainty during exploration. In Proceedings of the ICRA’04. 2004 IEEE International Conference on Robotics and Automation (Vol. 2, pp. 1758–1763). IEEE.Google Scholar
  19. Smith, R. N., Schwager, M., Smith, S. L., Jones, B. H., Rus, D., & Sukhatme, G. S. (2011). Persistent ocean monitoring with underwater gliders: Adapting sampling resolution. Journal of Field Robotics, 28(5), 714–741.CrossRefGoogle Scholar
  20. Stachniss, C., Grisetti, G., & Burgard, W. (2005). Information gain-based exploration using rao-blackwellized particle filters. Robotics: Science and Systems, 2.Google Scholar
  21. Thomas, L. (2004). Griffiths and Mark Steyvers. Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl 1), 5228–5235.Google Scholar
  22. Xu, A., Viriyasuthee, C., & Rekleitis, I. (May 2011). Optimal complete terrain coverage using an unmanned aerial vehicle. In 2011 IEEE International conference on robotics and automation (pp. 2513–2519), IEEE.Google Scholar
  23. Yamauchi, B. (1997). A frontier-based approach for autonomous exploration. In Proceedings 1997 IEEE International symposium on computational intelligence in robotics and automation CIRA’97. Towards new computational principles for robotics and automation (pp. 146–151). IEEE Computer Society Press.Google Scholar

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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