Table of Contents
Advances in Artificial Intelligence
Volume 2010, Article ID 765876, 20 pages
http://dx.doi.org/10.1155/2010/765876
Research Article

Bootstrap Learning and Visual Processing Management on Mobile Robots

Department of Computer Science, Texas Tech University, Lubbock, TX 79409, USA

Received 1 October 2009; Accepted 10 November 2009

Academic Editor: Alfons Schuster

Copyright © 2010 Mohan Sridharan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. “Hokuyo laser,” 2010, http://www.hokuyo-aut.jp/products/.
  2. “Videre design camera,” 2010, http://www.videredesign.com/vision/stereo_products.htm.
  3. B. W. Minten, R. R. Murphy, J. Hyams, and M. Micire, “Low-order-complexity vision-based docking,” IEEE Transactions on Robotics and Automation, vol. 17, no. 6, pp. 922–930, 2001. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Pineau, M. Montemerlo, M. Pollack, N. Roy, and S. Thrun, “Towards robotic assistants in nursing homes: challenges and results,” Robotics and Autonomous Systems, vol. 42, no. 3-4, pp. 271–281, 2003. View at Publisher · View at Google Scholar · View at Scopus
  5. DARPA, “The DARPA urban robot challenge,” 2007, http://www.darpa.mil/grandchallenge/index.asp/.
  6. S. Thrun, “Stanley: the robot that won the DARPA grand challenge,” Journal of Field Robotics, vol. 23, no. 9, pp. 661–692, 2006. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Thrun, M. Beetz, M. Bennewitz et al., “Probabilistic algorithms and the interactive museum tourguide robot minerva,” International Journal of Robotics Research, vol. 19, no. 11, pp. 972–999, 2000. View at Google Scholar
  8. S. Thrun, D. Fox, W. Burgard, and F. Dellaert, “Robust monte carlo localization for mobile robots,” Artificial Intelligence, vol. 128, no. 1-2, pp. 99–141, 2001. View at Publisher · View at Google Scholar · View at Scopus
  9. DARPA, “The DARPA grand challenge,” 2005, http://www.grandchallenge.org/.
  10. S. Se, D. Lowe, and J. Little, “Vision-based mapping with backward correction,” in Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS '02), vol. 1, pp. 153–158, Lausanne, Switzerland, October 2002. View at Scopus
  11. M. Sridharan and P. Stone, “Structure-based color learning on a mobile robot under changing illumination,” Autonomous Robots, vol. 23, no. 3, pp. 161–182, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Sridharan and P. Stone, “Global action selection for illumination invariant color modeling,” in Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS '07), pp. 1671–1676, San Diego, Calif, USA, November 2007. View at Publisher · View at Google Scholar
  13. M. Sridharan, J. Wyatt, and R. Dearden, “HiPPo: hierarchical POMDPs for planning information processing and sensing actions on a robot,” in Proceedings of the 18th International Conference on Automated Planning and Scheduling (ICAPS '08), pp. 346–354, Sydney, Australia, September 2008.
  14. P. Stone, M. Sridharan, D. Stronger et al., “From pixels to multi-robot decision-making: a study in uncertainty,” Robotics and Autonomous Systems, vol. 54, no. 11, pp. 933–943, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. H. Kitano, M. Asada, I. Noda, and H. Matsubara, “Robot world cup,” Robotics and Automation, vol. 16, no. 6, p. 700, 1998. View at Google Scholar
  16. N. Hawes, A. Sloman, J. Wyatt et al., “Towards an integrated robot with multiple cognitive functions,” in Proceedings of the 22nd National Conference on Artificial Intelligence (AAAI '07), vol. 2, pp. 1548–1553, Vancouver, Canada, July 2007. View at Scopus
  17. CoSy, “Cognitive systems for cognitive assistants,” 2008, http://www.cognitivesystems.org/.
  18. D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603–619, 2002. View at Publisher · View at Google Scholar · View at Scopus
  19. J. Shi and J. Malik, “Normalized cuts and image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888–905, 2000. View at Publisher · View at Google Scholar · View at Scopus
  20. B. Sumengen, B. S. Manjunath, and C. Kenney, “Image segmentation using multi-region stability and edge strength,” in Proceedings of the IEEE International Conference on Image Processing (ICIP '03), vol. 3, pp. 429–432, Barcelona, Spain, September 2003. View at Scopus
  21. V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic active contours,” International Journal of Computer Vision, vol. 22, no. 1, pp. 61–79, 1997. View at Google Scholar · View at Scopus
  22. N. Paragios and R. Deriche, “Geodesic active regions for supervised texture segmentation,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV '99), vol. 2, pp. 926–932, Kerkyra, Greece, September 1999.
  23. P. F. Felzenszwalb and D. P. Huttenlocher, “Efficient graph-based image segmentation,” International Journal of Computer Vision, vol. 59, no. 2, pp. 167–181, 2004. View at Publisher · View at Google Scholar · View at Scopus
  24. J. Shi and J. Malik, “Motion segmentation and tracking using normalized cuts,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV '98), pp. 1154–1160, Bombay, India, January 1998.
  25. D. Hoiem, A. Efros, and M. Hebert, “Recovering surface layout from an image,” International Journal of Computer Vision, vol. 75, no. 1, pp. 151–172, 2007. View at Publisher · View at Google Scholar · View at Scopus
  26. W. Uther, S. Lenser, J. Bruce, M. Hock, and M. Veloso, “Cm-pack'01: fast legged robot walking, robust localization, and team behaviors,” in Proceedings of the 5th International RoboCup Symposium, Seattle, Wash, USA, August 2001.
  27. S. Chen, M. Siu, T. Vogelgesang et al., RoboCup-2001: The Fifth RoboCup Competitions and Conferences, Springer, Berlin, Germany, 2002.
  28. D. Cohen, Y. H. Ooi, P. Vernaza, and D. D. Lee, RoboCup-2003: The Seventh RoboCup Competitions and Conferences, Springer, Berlin, Germany, 2004.
  29. Y. B. Lauziere, D. Gingras, and F. P. Ferrie, “Autonomous physics-based color learning under daylight,” in Proceedings of the EUROPTO Conference on Polarization and Color Techniques in Industrial Inspection, vol. 3826, pp. 86–100, Munich, Germany, June 1999.
  30. T. Gevers and A. W. M. Smeulders, “Color-based object recognition,” Pattern Recognition, vol. 32, no. 3, pp. 453–464, 1999. View at Google Scholar · View at Scopus
  31. D. Cameron and N. Barnes, “Knowledge-based autonomous dynamic color calibration,” in Proceedings of the 7th RoboCup International Symposium (RoboCup '03), Padua, Italy, July 2003.
  32. M. Jungel, “Using layered color precision for a self-calibrating vision system,” in Proceedings of the 8th International RoboCup Symposium (RoboCup '04), Lisbon, Portugal, July 2004.
  33. G. Finlayson, S. Hordley, and P. Hubel, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 11, pp. 1209–1221, 2001. View at Publisher · View at Google Scholar · View at Scopus
  34. E. H. Land, “The retinex theory of color constancy,” Scientific American, vol. 237, pp. 108–129, 1977. View at Google Scholar
  35. G. Buchsbaum, “A spatial processor model for object colour perception,” Journal of the Franklin Institute, vol. 310, no. 1, pp. 1–26, 1980. View at Google Scholar · View at Scopus
  36. D. H. Brainard and B. A. Wandell, “Analysis of the retinex theory of color vision,” Journal of the Optical Society of America A, vol. 3, no. 10, pp. 1651–1661, 1986. View at Google Scholar · View at Scopus
  37. D. Forsyth, “A novel algorithm for color constancy,” International Journal of Computer Vision, vol. 5, no. 1, pp. 5–35, 1990. View at Publisher · View at Google Scholar · View at Scopus
  38. G. Finlayson and S. Hordley, “Improving gamut mapping color constancy,” IEEE Transactions on Image Processing, vol. 9, no. 10, pp. 1774–1783, 2000. View at Publisher · View at Google Scholar · View at Scopus
  39. D. H. Brainard and W. T. Freeman, “Bayesian color constancy,” Journal of the Optical Society of America A, vol. 14, no. 7, pp. 1393–1411, 1997. View at Google Scholar · View at Scopus
  40. Y. Tsin, R. T. Collins, V. Ramesh, and T. Kanade, “Bayesian color constancy for outdoor object recognition,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '01), vol. 1, pp. I1132–I1139, Kauai, Hawaii, USA, December 2001. View at Scopus
  41. S. Lenser and M. Veloso, “Automatic detection and response to environmental change,” in Proceedings of the IEEE International Conference of Robotics and Automation (ICRA '03), vol. 1, pp. 1416–1421, Taipei, Taiwan, May 2003. View at Scopus
  42. F. Anzani, D. Bosisio, M. Matteucci, and D. G. Sorrenti, “On-line color calibration in non-stationary environments,” in Proceedings of the 9th International RoboCup Symposium (RoboCup '05), pp. 396–407, Osaka, Japan, July 2005.
  43. D. Schulz and D. Fox, “Bayesian color estimation for adaptive vision-based robot localization,” in Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS '04), vol. 2, pp. 1884–1889, Sendai, Japan, September 2004. View at Scopus
  44. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, John Wiley & Sons, New York, NY, USA, 2nd edition, 2000.
  45. M. Sridharan and P. Stone, “Color learning and illumination invariance on mobile robots: a survey,” Robotics and Autonomous Systems, vol. 75, no. 1, pp. 1–38, 2009. View at Google Scholar
  46. M. Ghallab, D. Nau, and P. Traverso, Automated Planning: Theory and Practice, Morgan Kaufmann, San Francisco, Calif, USA, 2004.
  47. R. A. Brooks, “A robust layered control system for a mobile robot,” Robotics and Automation, vol. 2, no. 1, pp. 14–23, 1986. View at Google Scholar · View at Scopus
  48. J. E. Laird, A. Newell, and P. Rosenbloom, “SOAR: an architecture for general intelligence,” Artificial Intelligence, vol. 33, no. 3, pp. 1–64, 1987. View at Google Scholar · View at Scopus
  49. J. R. Anderson, D. Bothell, M. D. Byrne, S. Douglass, C. Lebiere, and Y. Qin, “An integrated theory of the mind,” Psychological Review, vol. 111, no. 4, pp. 1036–1060, 2004. View at Publisher · View at Google Scholar · View at Scopus
  50. D. Draper, S. Hanks, and D. Weld, “A probabilistic model of action for least-commitment planning with information gathering,” in Proceedings of the 10th Conference on Uncertainty in Artificial Intelligence (UAI '94), Seattle, Wash, USA, July 1994.
  51. S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Prentice-Hall, Upper Saddle River, NJ, USA, 2nd edition, 2003.
  52. R. P. A. Patrick and F. Bacchus, “Extending the knowledge-based approach to planning with incomplete information and sensing,” in Proceedings of the 14th International Conference on Automated Planning and Scheduling (ICAPS '04), pp. 2–11, Whistler, Canada, June 2004.
  53. M. Brenner and B. Nebel, “Continual planning and acting in dynamic multiagent environments,” Journal of Autonomous Agents and Multi-Agent Systems, vol. 19, no. 3, pp. 297–331, 2009. View at Publisher · View at Google Scholar · View at Scopus
  54. J. Hoffmann and B. Nebel, “The FF planning system: fast plan generation through heuristic search,” Journal of Artificial Intelligence Research, vol. 14, pp. 253–302, 2001. View at Google Scholar · View at Scopus
  55. R. Clouard, A. Elmoataz, C. Porquet, and M. Revenu, “Borg: a knowledge-based system for automatic generation of image processing programs,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 2, pp. 128–144, 1999. View at Google Scholar · View at Scopus
  56. S. Chien, F. Fisher, and T. Estlin, “Automated software module reconfiguration through the use of artificial intelligence planning techniques,” IEE Proceedings: Software, vol. 147, no. 5, pp. 186–192, 2000. View at Publisher · View at Google Scholar · View at Scopus
  57. M. Thonnat and S. Moisan, “What can program supervision do for program reuse?” IEE Proceedings: Software, vol. 147, no. 5, pp. 179–185, 2000. View at Google Scholar
  58. S. Moisan, “Program supervision: yakl and pegase+ reference and user manual,” Rapport de Recherche 5066, INRIA, Sophia Antipolis, France, December 2003. View at Google Scholar
  59. T. Darrell, “Reinforcement learning of active recognition behaviors,” Tech. Rep. 1997-045, Interval Research Corp., Palo Alto, Calif, USA, 1997. View at Google Scholar
  60. L. Li, V. Bulitko, R. Greiner, and I. Levner, “Improving an adaptive image interpretation system by leveraging,” in Proceedings of the 8th Australian and New Zealand Conference on Intelligent Information Systems, Sydney, Australia, December 2003.
  61. J. Vogel and N. de Freitas, “Target-directed attention: sequential decision-making for gaze planning,” in Proceedings of the International Conference on Robotics and Automation (ICRA '08), pp. 2372–2379, Pasadena, Calif, USA, May 2008.
  62. C. Kreucher, K. Kastella, and A. Hero, “Sensor management using an active sensing approach,” IEEE Transactions on Signal Processing, vol. 85, no. 3, pp. 607–624, 2005. View at Publisher · View at Google Scholar · View at Scopus
  63. A. O. I. Hero, D. A. Castanon, D. Cochran, and K. Kastella, Foundations and Applications of Sensor Management, Springer, New York, NY, USA, 2008.
  64. A. Krause, A. Singh, and C. Guestrin, “Near-optimal sensor placements in gaussian processes: theory, efficient algorithms and empirical studies,” Tech. Rep. CMU-ML-07-108, Carnegie Mellon University, 2007. View at Google Scholar
  65. A. Krause, A. Singh, and C. Guestrin, “Near-optimal sensor placements in gaussian processes: theory, efficient algorithms and empirical studies,” Journal of Machine Learning Research, vol. 9, pp. 235–284, 2008. View at Google Scholar · View at Scopus
  66. J. Pineau and S. Thrun, “High-level robot behavior control using POMDPs,” in Proceedings of the 8th National Conference on Artificial Intelligence (AAAI '02), Edmonton, Canada, July 2002.
  67. T. Dietterich, “The MAXQ method for hierarchical reinforcement learning,” in Proceedings of the 15th International Conference on Machine Learning (ICML '98), Madison, Wis, USA, July 1998.
  68. E. A. Hansen and R. Zhou, “Synthesis of hierarchical finite-state controllers for POMDPs,” in Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS '03), pp. 113–122, Trento, Italy, June 2003.
  69. A. F. Foka and P. E. Trahanias, “Real-time hierarchical POMDPs for autonomous robot navigation,” in Proceedings of the IJCAI Workshop on Reasoning with Uncertainty in Robotics, Edinburgh, Scotland, July 2005.
  70. J. M. Porta, M. T. J. Spaan, and N. Vlassis, “Robot planning in partially observable continuous domains,” in Robotics: Science and Systems, 2005. View at Google Scholar
  71. J. Pineau and G. Gordon, “POMDP planning for robust robot control,” in Proceedings of the 12th International Symposium on Robotics Research, San Fransisco, Calif, USA, October 2005.
  72. G. Theocharous, K. Murphy, and L. P. Kaelbling, “Representing hierarchical POMDPs as DBNs for multi-scale robot localization,” in Proceedings of the IEEE International Conference on Robotics and Automation ( ICRA '04), pp. 1045–1051, New Orleans, La, USA, April 2004. View at Scopus
  73. M. Toussaint, L. Charlin, and P. Poupart, “Hierarchical POMDP controller optimization by likelihood maximization,” in Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence (UAI '08), Helsinki, Finland, July 2008.
  74. B. Efron and R. J. Tibshirani, An Introduction to Bootstrap, Chapman and Hall, New York, NY, USA, 1993.
  75. P. S. Maybeck, Stochastic Models, Estimation and Control, Academic Press, New York, NY, USA, 1979.
  76. M. Sridharan and P. Stone, “Color learning on a mobile robot: towards full autonomy under changing Illumination,” in Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI '07), Hyderabad, India, January 2007.
  77. C. M. Bishop, Pattern Recognition and Machine Learning, Springer, New York, NY, USA, 2008.
  78. M. Sridharan and X. Li, “Learning sensor models for autonomous information fusion on a humanoid robot,” in Proceedings of the IEEE-RAS International Conference on Humanoid Robots (ICHR '09), Kobe, Japan, June 2009.
  79. D. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004. View at Publisher · View at Google Scholar · View at Scopus
  80. L. Kaelbling, M. Littman, and A. Cassandra, “Planning and acting in partially observable stochastic domains,” Artificial Intelligence, vol. 101, no. 1-2, pp. 99–134, 1998. View at Google Scholar · View at Scopus
  81. M. Sridharan, J. Wyatt, and R. Dearden, “E-HiPPo: extensions to hierarchical POMDP-based visual planning on a robot,” in Proceedings of the 27th Workshop of the UK Planning and Scheduling Special Interest Group (PlanSIG '08), Edinburgh, UK, December 2008.
  82. T. Smith and R. Simmons, “Point-based POMDP algorithms: improved analysis and implementation,” in Proceedings of the 21st Conference in Uncertainty in Artificial Intelligence (UAI '05), Edinburgh, UK, July 2005.
  83. M. Sridharan, J. Wyatt, and R. Dearden, “POMDP-based planning for visual processing management on a mobile robot,” in Proceedings of the 5th International Cognitive Vision Workshop (ICVW '09), Saint Louis, Mo, USA, October 2009.
  84. L. Itti, C. Koch, and E. Niebur, “A model of saliency-based visual attention for rapid scene analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 11, pp. 1254–1259, 1998. View at Google Scholar · View at Scopus
  85. “ZMDP planning code,” 2008, http://www.cs.cmu.edu/~trey/zmdp.
  86. A. J. Davison, I. D. Reid, N. D. Molton, and O. Stasse, “MonoSLAM: real-time single camera SLAM,” Pattern Analysis and Machine Intelligence, vol. 29, no. 6, pp. 1052–1067, 2007. View at Publisher · View at Google Scholar · View at Scopus
  87. P. Felzenszwalb and D. Huttenlocher, “Efficient matching of pictorial structures,” in Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR '00), Hilton Head, SC, USA, June 2000.