Skip to main content
SpringerLink
Log in
Book cover

Ambient Intelligence for Scientific Discovery pp 263–285Cite as

Behavior-Based Indoor Navigation

  • Chapter

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3345))

Abstract

Ambience provides large amounts of heterogeneous data that can be used for diverse purposes, including indoor navigation in semi-structured environments. Indoor navigation is a very active research field due to its large number of possible applications: mobile guides for museums or other public buildings [36], office post delivering, assistance to people with disabilities and elderly people [34], etc.

The idea of using indoor navigation techniques to develop mobile guides is not new. Among the pioneers, Polly, a mobile robot acting as a guide for the MIT AI Lab [35], and Minerva, an autonomous guide developed for the National Museum of American History in Washington [69], are well known. A particular case are mobile guides for blind people which experienced a notable interest in the last years [40]. Another interesting application field is devoted to smart wheelchairs, which are provided with navigation aids for people with severe motor restrictions [64,75]. All these applications share the need for a navigation system, even if its implementation may be different for each of them. For instance, the navigation system may act over the power stage of a smart wheelchair or may communicate with the user interface of a mobile navigation assistant in a museum. Evidently the implication of the user is different in each system, leading to diverse levels of human-system integration. Therefore, there are two key issues in the design of mobile guides: navigation strategy and user interface. Even if most of the mentioned systems use maps for navigation [36], there exist alternative, behavior-based systems, that use a procedural way to represent knowledge. Therefore, the selection of the approach not only conditions the navigational architecture but also the design of the human interface.

This chapter analyzes alternatives for navigation models and focuses on how properties of the environment can be intelligently exploited for indoor navigation tasks. In addition, it describes, in detail, an illustrative example based on behavior decomposition. Its navigational characteristics and influence upon the human interface design are also discussed.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abascal, J., Cagigas, D., Garay, N., Gardeazabal, L.: Interfacing users with very severe mobility restrictions with a semi-automatically guided wheelchair. ACM SIGCAPH Newsletter 63, 16–20 (1999)

    Google Scholar 

  2. Abascal, J., Cagigas, D., Garay, N., Gardeazabal, L.: Mobile interface for a smart wheelchair. In: Paternó, F. (ed.) Mobile HCI 2002. LNCS, vol. 2411, pp. 373–377. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  3. Abascal, J., Cagigas, D., Garay, N., Gardeazabal, L.: Mobile interfaces for people with severe motor restrictions. In: Stephanidis, C. (ed.) Universal Access in HCI. Inclusive Design in the Information Society, vol. 4, pp. 289–293. Lawrence Erlbaum Associates, Mahwah (2003)

    Google Scholar 

  4. Aloimonos, J.: Active Perception. Lawrence Erlbaum Assoc Inc., Mahwah (1993)

    Google Scholar 

  5. Arkin, R.C.: Behavior-Based Robotics. MIT Press, Cambridge (1998)

    Google Scholar 

  6. Arkin, R.C.: Motor schema-based mobile robot navigation. International Journal of Robotics Research 8(4), 92–112 (1989)

    Article  Google Scholar 

  7. Astigarraga, A., Lazkano, E., Rañó, I., Sierra, B., Zarautz, I.: SORGIN: a software framework for behavior control implementation. In: CSCS14, vol. 1, pp. 243–248 (2003)

    Google Scholar 

  8. Astigarraga, A., Lazkano, E., Sierra, B., Rañó, I.: Active landmark perception. In: MMAR-2004 (2004) (in press)

    Google Scholar 

  9. Ballard, D.H., Brown, C.M.: Principles of animate vision. Image Understanding 56, 3–21 (1992)

    Article  MATH  Google Scholar 

  10. Bennett, A.T.D.: Do animals have cognitive maps? The journal of experimental biology, 219–224 (1997)

    Google Scholar 

  11. Borenstein, J., Everett, B., Feng, L.: Navigating Mobile Robots: Systems and Techniques. A. K. Peters (1996)

    Google Scholar 

  12. Brooks, R.: The role of learning in autonomous robots. In: Proceedings of the Fourth Annual Workshop on Computational Learning Theory, pp. 5–10. Morgan Kaufmann, San Mateo (1991)

    Google Scholar 

  13. Brooks, R.A.: A robust layered control system for a mobile robot. IEEE Journal of robotics and automation RA–26, 14–23 (1986)

    Google Scholar 

  14. Brooks, R.A.: Planning is just a way of avoiding figuring out what to do next. Technical report, MIT Artificial Intelligence Laboratory (1987)

    Google Scholar 

  15. Brooks, R.A., Connell, J.H.: Asynchronous distributed control system for a mobile robot. In: Proceedings of the SPIE’s Cambridge Symposyum on Optical and Optoelectronic Engineering, pp. 77–84 (1986)

    Google Scholar 

  16. Brooks, R.A.: A robot that walks: Emergent behaviors from a carefully evolved network. Technical Report AI MEMO 1091. MIT (1989)

    Google Scholar 

  17. Cagigas, D., Abascal, J.: Hierarchical path search with partial materialization of costs for a smart wheelchair. Journal of Intelligent and Robotic Systems 39(4), 409–431 (2004)

    Article  Google Scholar 

  18. Cassandra, A.R., Kaelbling, L.P., Kurien, J.A.: Acting under uncertainty: discrete bayesian models for mobile-robot navigation. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robot and Systems (1996)

    Google Scholar 

  19. Chung, H., Ojeda, L., Borenstein, J.: Sensor fusion for mobile robot deadreckoning with a precision-calibrated fiber optic gyroscope. In: proceedings of the 2001 IEEE International Conference on Robotics and Automation, ICRA 2001, vol. 3, pp. 3588–3593 (2001)

    Google Scholar 

  20. Civit, A., Abascal, J.: Tetranauta: A weheelchair controller for users with very severe mobility restrictions. In: Placencia, I., Ballabio, E. (eds.) Improving the quality of Life for the European Citizen, Technology for Inclusive Design and Equality. IOS Press, Amsterdam (1998)

    Google Scholar 

  21. Connell, J.H.: Minimalist Mobile Robotics. A Colony-Style Architecture for an Artificial Creature. Academic Press, inc., London (1990)

    Google Scholar 

  22. DeSouza, G.N., Kak, A.C.: Vision for mobile robot navigation: A survey. IEEE Transactions on pattern analysis and Machine Intelligence 24(2), 237–267 (2002)

    Article  Google Scholar 

  23. Duckett, T., Nehmzow, U.: Performance comparison of landmark recognition systems for navigating mobile robots. In: AAAI (2000)

    Google Scholar 

  24. Sharkey, N. (ed.): Special issue: Robot learning: the new wave. Robotics and Autonomous Systems 22(3-4) (1997)

    Google Scholar 

  25. Elfes, A.: Using occupancy grids for mobile robot perception and navigation. Computer 22, 46–57 (1989)

    Article  Google Scholar 

  26. Everett, H.R.: Sensors for Mobile Robots. Theory and Applications. A. K. Peters, Ltd., Wellesley (1995)

    Google Scholar 

  27. Fox, D., Burgard, W., Thrun, S.: Active markov localization for mobile robots. Robotics and Autonomous Systems 25(1), 195–207 (1998)

    Article  Google Scholar 

  28. Fox, D., Thrun, S., Burgard, W., Dellaert, F.: Sequential Monte Carlo methods in practice. In: Particle filters for mobile robot localization, pp. 470–498. Springer, Heidelberg (2002)

    Google Scholar 

  29. Gat, E.: On three-layer architectures. In: Kortenkamp, D., Bonasso, R.P. (eds.) Artificial Intelligence and Mobile Robots. Case Studies of Succesful Robot Systems, pp. 195–210. MIT Press, Cambridge (1997)

    Google Scholar 

  30. Gelenbe, E. (ed.): Special issue: Biologically inspired autonomous systems. Robotics and Autonomous Systems 22(1) (1997)

    Google Scholar 

  31. Gerkey, B.P., Vaughan, R.T., Howard, A.: The Player/Stage project: tools for multi-robot and distributed sensor systems. In: Proc. of the International Conference on Advanced Robotics (ICAR), pp. 317–323 (2003)

    Google Scholar 

  32. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison–Wesley, London (1989)

    MATH  Google Scholar 

  33. González-Baños, H.H., Latombe, J.C.: Navigation strategies for exploring indoor environments. International Journal of Robotics Research 21(10-11), 829–848 (2002)

    Google Scholar 

  34. Goodman, J., Gray, P., Khammampad, K., Brewster, S.: Using landmarks to support older people in navigation. In: Brewster, S., Dunlop, M.D. (eds.) Mobile HCI 2004. LNCS, vol. 3160, pp. 38–48. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  35. Horswill, I.: Polly: A vision-based artificial agent. In: Proceedings of the 11th Conference of the American Association for Artificial Intelligence (AAAI 1993) (1993)

    Google Scholar 

  36. Kray, C., Baus, J., Cheverst, K.: A survey of map-based mobile guides. In: Zipf, A., Meng, L., Reichenbacher, T. (eds.) Map-based mobile services- Theories, Methods and Implementations. Springer, Heidelberg (2004)

    Google Scholar 

  37. Kalman, R.R.: A new approach to linear filtering and prediction problems. Transactions of the ASME journal of Basic Engineering 82, 35–45 (1960)

    Google Scholar 

  38. Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. International Journal of Robotics Research 5(1), 90–98 (1996)

    Google Scholar 

  39. Kohavi, R., Sommerfield, D., Dougherty, J.: Data mining using MLC++: A machine learning library in C++. International Journal on Artificial Intelligence Tools 6(4), 537–566 (1997), http://www.sgi.com/Technology/mlc

    Article  Google Scholar 

  40. Kulyukin, V., Gharpure, C.P., Sute, P., De Graw, N., Nicholson, J.: A robotic wayfinding system for the visually impaired. In: Proceedings of the Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI 2004) (2004)

    Google Scholar 

  41. Latombe, J.C.: Motion planning: a journey of robots, molecules, digital actors and other artifacts. International Journal of Robotics Research 18(11), 1119–1128 (1999)

    Article  Google Scholar 

  42. Latombe, J.C.: Robot Motion Planning. Kluwer Academic, Dordrecht (1991)

    Google Scholar 

  43. Lazkano, E., Astigarraga, A., Sierra, B., Rañó, I.: On the adequateness of emergency exit panel and corridor identification as pilot scheme for a mobile robot. In: Intelligent Autonomous Systems 8, vol. 1, pp. 915–924 (2004)

    Google Scholar 

  44. Leonard, J.J., Durrant-Whyte, H.F.: Directed sonar sensing for mobile robot navigation. Kluwer Academic Publishers, Dordrecht (1992)

    MATH  Google Scholar 

  45. Levitt, T.S., Lawton, D.T.: Qualitative navigation for mobile robots. Artificial Intelligence 44(3), 305–360 (1990)

    Article  Google Scholar 

  46. Maes, P.: The dynamic of action selection. In: Proceedings of the 1989 International Joint Conference on Artificial Intelligence, Detroit, pp. 991–997 (1989)

    Google Scholar 

  47. Mallot, H.A., Franz, M.A.: Biomimetic robot navigation. Robotics and Autonomous System 30, 133–153 (2000)

    Article  Google Scholar 

  48. Martin, M.C., Moravec, H.: Robot evidence grids. Technical Report CMU-RITR-96-06, Robotics Institute. Carnagie Mellon University (1996)

    Google Scholar 

  49. Matarić, M.: A distributed model for mobile robot environment-learning and navigation. Master’s thesis, MIT Artificial Intelligence Laboratory (1990)

    Google Scholar 

  50. Matarić, M.: Behavior-based control: Main properties and implications. In: Proceedings of the IEEE International Conference on Robotics and Automation. Workshop on Architectures for Intelligent Control Systems, pp. 46–54 (1992)

    Google Scholar 

  51. Matarić, M.: Integration of representation into goal-driven behavior-based robotics. IEEE transactions on robotics and automation 8(3), 2, 304–331 (1992)

    Google Scholar 

  52. Meyer, J.-A.: From natural to artificial life: Biomimetic mechanisms in animat design. Robotics and Autonomous Systems 22, 3–21 (1997)

    Article  Google Scholar 

  53. Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)

    MATH  Google Scholar 

  54. Murthy, S.K., Kasif, S., Salzberg, S.: A system for induction of oblique decision trees. Journal of Artificial Intelligence Research 2, 1–33 (1994), ftp://blaze.cs.jhu.edu/pub/oc1

  55. Nehmzow, U., Walker, K.: Is the behavior of a mobile robot chaotic? In: Proceedings of the Artificial Intelligence and Simulated Behavior (2003)

    Google Scholar 

  56. Nilsson, N.: Shakey the robot. Technical Report 323, SRI International (1984)

    Google Scholar 

  57. Nourbakhsh, I.: Dervish: An office-navigating robot. In: Kortenkamp, D., Bonassi, R.P., Murphy, R. (eds.) Artificial Intelligence and Mobile Robots. Case Studies of Succesful Robot Systems, pp. 73–90. The AAAI Press, MIT Press (1998)

    Google Scholar 

  58. Piegl, L.A., Tiller, W.: The NURBS Book. Springer, Heidelberg (1997)

    Google Scholar 

  59. Pirjanian, P.: Multiple Objective Action Selection and Behavior Fusion using Voting. PhD thesis, Institute of Electronic Systems, Aalborg University, Denmark (1998)

    Google Scholar 

  60. Pirjanian, P.: Behavior coordination mechanisms – state of the art. Technical Report iris-99-375, Institute of Robotics and Intelligent Systems, USC (1999)

    Google Scholar 

  61. Rekleitis, I.M.: A particle filter tutorial for mobile robot localization. Technical Report TR-CIM-04-02, Centre for Intelligent Machines, McGill University, 3480 University St., Montreal, Québec, CANADA H3A 2A7 (2004)

    Google Scholar 

  62. Rosenblatt, J.K.: DAMN: A distributed architecture for mobile navigation. In: Proc. of the AAAI Spring Symp. on Lessons Learned from Implememted Software Architectures for Physical Agents, Stanford, CA, pp. 167–178 (1995)

    Google Scholar 

  63. Rosencrantz, M., Gordon, G., Thrun, S.: Decentralized sensor fusion with distributed particle filters. In: Proceedings of the 19th Annual Conference on Uncertainty in Artificial Intelligence (UAI 2003), pp. 493–500. Morgan Kaufmann Publishers, San Francisco (2003)

    Google Scholar 

  64. Longhi, S., Fioretti, S., Leo, T.: A navigation system for increasing the autonomy and the security of powered wheelchairs. IEEE Transactions on Rehabilitation Engineering 8(4), 490–498 (2000)

    Article  Google Scholar 

  65. Simmons, R., Koenig, S.: Probabilistic robot navigation in partially observable environments. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 1080–1087 (1995)

    Google Scholar 

  66. Thorpe, C.F.: Path relaxation: Path planning for a mobile robot. Technical Report CMU-RI-TR-84-5, The Robotics Institute. Carnagie-Mellon University (1984)

    Google Scholar 

  67. Thrun, S.: Learning maps for indoor mobile robot navigation. Artificial Intelligence 99(1), 21–71 (1998)

    Article  MATH  Google Scholar 

  68. Thrun, S.: Robotic mapping: A survey. In: Lakemeyer, G., Nebel, B. (eds.) Exploring Artificial Intelligence in the New Millennium, pp. 1–35. Morgan Kaufmann, San Francisco (2003)

    Google Scholar 

  69. Thrun, S., Bennewitz, M., Burgard, W., Cremers, A.B., Dellaert, F., Fox, D., Hahnel, D., Rosenberg, C., Roby, N., Schutle, J., Schultz, D.: Minerva: A second generation mobile tour-guide robot. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 1999) (1999)

    Google Scholar 

  70. Thrun, S., Fox, D., Burgard, W., Dellaert, F.: Robust monte carlo localization for mobile robots. Artificial Intelligence 128(1-2), 99–141 (2001)

    Google Scholar 

  71. Trullier, O., Wiener, S.I., Berthoz, A., Meyer, J.A.: Biologically-based artificial navigation systems: Review and prospects. Progress in Neurobiology 51, 483–544 (1997)

    Article  Google Scholar 

  72. Webb, B.: Can robots make good models of biological behavior? Behavioral and Brain Sciences 24(6) (2001)

    Google Scholar 

  73. Wu, H., Siegel, M., Stiefelhagen, R., Yang, J.: Sensor fusion using dempstershafer theory. In: proceedings of the IEEE Instrumentation and Measurement Technology Conference (2002)

    Google Scholar 

  74. Yamauchi, B., Schultz, A., Adams, W.: Mobile robot exploration and mapbuilding with continuous localization. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 3715–3720 (1998)

    Google Scholar 

  75. Yanco, H.A.: Wheelesley, a robotic wheelchair system: Indoor navigation and user interface. In: Mittal, V.O., Yanco, H.A., Aronis, J., Simpson, R.C. (eds.) Assistive Technology and Artificial Intelligence. LNCS (LNAI), vol. 1458, pp. 256–268. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Euskal Herriko Unibertsitatea, The University of the Basque Country, Spain

    Julio Abascal, Elena Lazkano & Basilio Sierra

Authors
  1. Julio Abascal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elena Lazkano
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Basilio Sierra
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Ambient Intelligence Lab, CIC-2218, Carnegie Mellon University, 4720 Forbes Avenue, 15213, Pittsburgh, PA, USA

    Yang Cai

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Abascal, J., Lazkano, E., Sierra, B. (2005). Behavior-Based Indoor Navigation. In: Cai, Y. (eds) Ambient Intelligence for Scientific Discovery. Lecture Notes in Computer Science(), vol 3345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32263-4_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-32263-4_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24466-0

  • Online ISBN: 978-3-540-32263-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation