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Paladyn, Journal of Behavioral Robotics

Editor-in-Chief: Schöner, Gregor

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2081-4836
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Intelligent Robot Navigation using View Sequences and a Sparse Distributed Memory

Mateus Mendes / A. Paulo Coimbra / Manuel M. Crisóstomo
Published Online: 2011-04-27 | DOI: https://doi.org/10.2478/s13230-011-0010-z

Abstract

Different approaches have been tried to navigate robots, including those based on visual memories. The Sparse Distributed Memory (SDM) is a kind of associative memory based on the properties of high dimensional binary spaces. It exhibits characteristics such as tolerance to noise and incomplete data, ability to work with sequences and the possibility of one-shot learning. Those characteristics make it appealing to use for robot navigation. The approach presented in this work was to navigate a robot using sequences of visual memories stored into a SDM. The robot makes intelligent decisions, such as selecting only relevant images to store during path learning, adjusting memory parameters to the level of noise and inferring new paths from learnt trajectories. The method of encoding the information may influence the tolerance of the SDM to noise and saturation. The present paper reports novel results of the limits of the model under different typical navigation problems. An algorithm to build a topological map of the environment based on the visual memories is also described.

Keywords: Sparse Distributed Memory; SDM; Robot Navigation; Data Encoding; Vision-based Navigation

References

  • [1] Steven Johnson. Mind wide open. Scribner, New York, 2004.Google Scholar

  • [2] Jeff Hawkins and Sandra Blakeslee. On Intelligence. Times Books, New York, 2004.Google Scholar

  • [3] Pentti Kanerva. Sparse Distributed Memory. MIT Press, Cambridge, 1988.Google Scholar

  • [4] Robert M. Harnish. Minds, brains, computers: an historical introduction to the foundations of cognitive science. Wiley-Blackwell, 2002.Google Scholar

  • [5] Rajesh P.N. Rao and Olac Fuentes. Hierarchical learning of navigational behaviors in an autonomous robot using a predictive sparse distributed memory. Machine Learning, 31(1-3):87-113, April 1998.Google Scholar

  • [6] Michiko Watanabe, Masashi Furukawa, and Yukinori Kakazu. Intelligent agv driving toward an autonomous decentralized manufacturing system. Robotics and computer-integrated manufacturing, 17(1-2):57-64, February-April 2001.Google Scholar

  • [7] Mateus Mendes, A. Paulo Coimbra, and Manuel Crisóstomo. AI and memory: Studies towards equipping a robot with a sparse distributed memory. In Proceedings of the IEEE International Conference on Robotics and Biomimetics, pages 1743-1750, Sanya, China, December 2007.Google Scholar

  • [8] Mateus Mendes, Manuel Crisóstomo, and A. Paulo Coimbra. Robot navigation using a sparse distributed memory. In Proceedings of the 2008 IEEE International Conference on Robotics and Automation, Pasadena, California, USA, May 2008.Google Scholar

  • [9] Yoshio Matsumoto, Kazunori Ikeda, Masayuki Inaba, and Hirochika Inoue. Exploration and map acquisition for view-based navigation in corridor environment. In Proc. of the Int. Conference on Field and Service Robotics, pages 341-346, 1999.Google Scholar

  • [10] Yoshio Matsumoto, Masayuki Inaba, and Hirochika Inoue. View-based navigation using an omniview sequence in a corridor environment. In Machine Vision and Applications, 2003.Google Scholar

  • [11] Hiroshi Ishiguro and Saburo Tsuji. Image-based memory of environment. In in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, 1996.Google Scholar

  • [12] Niall Winters and José Santos-Victor. Mobile robot navigation using omni-directional vision. In In Proc. 3rd Irish Machine Vision and Image Processing Conference (IMVIP'99), pages 151-166, 1999.Google Scholar

  • [13] Min Meng and Avinash C. Kak. Mobile robot navigation using neural networks and nonmetrical environment models. In IEEE Control Systems, pages 30-39, 1993.Google Scholar

  • [14] Mateus Mendes, Manuel Crisóstomo, and A. Paulo Coimbra. Electronic Engineering and Computing Technology, chapter Encoding Data to use with a Sparse Distributed Memory. Springer, Netherlands, April 2010.Google Scholar

  • [15] Bohdana Ratitch and Doina Precup. Sparse distributed memories for on-line value-based reinforcement learning. In ECML, 2004.Google Scholar

  • [16] Rajesh P. N. Rao and Dana H. Ballard. Object indexing using an iconic sparse distributed memory. Technical Report 559, The University of Rochester, Computer Science Department, Rochester, New York, July 1995.Google Scholar

  • [17] Stephen B. Furber, John Bainbridge, J. Mike Cumpstey, and Steve Temple. Sparse distributed memory using n-of-m codes. Neural Networks, 17(10):1437-1451, 2004.Google Scholar

  • [18] Yoshio Matsumoto, Masayuki Inaba, and Hirochika Inoue. View-based approach to robot navigation. In Proc. of 2000 IEEE/RSJ Int. Conference on Intelligent Robots and Systems (IROS 2000), 2000.Google Scholar

  • [19] Mateus Mendes, Manuel Crisóstomo, and A. Paulo Coimbra. Assessing a sparse distributed memory using different encoding methods. In Proceedings of the 2009 International Conference of Computational Intelligence and Intelligent Systems, pages 37-42, London, UK, July 2009.Google Scholar

  • [20] Joy Bose. A scalable sparse distributed neural memory model. Master's thesis, University of Manchester, Faculty of Science and Engineering, Manchester, UK, 2003.Google Scholar

  • [21] Louis A. Jaeckel. An alternative design for a sparse distributed memory. Technical report, Research Institute for Advanced Computer Science, NASA Ames Research Center, July 1989.Google Scholar

About the article

Received: 2010-08-20

Accepted: 2011-03-04

Published Online: 2011-04-27

Published in Print: 2010-12-01


Citation Information: Paladyn, Journal of Behavioral Robotics, ISSN (Online) 2081-4836, DOI: https://doi.org/10.2478/s13230-011-0010-z.

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© Mateus Mendes et al.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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