JACIII Vol.12 No.2 pp. 206-213
doi: 10.20965/jaciii.2008.p0206


A Universal Autonomous Robot Navigation Method

Annamária R. Várkonyi-Kóczy

Dept. of Measurement and Information Systems, Budapest University of Technology, and Economics Integrated Intelligent Systems Japanese-Hungarian Laboratory, Magyar tudósok körútja 2., H-1117 Budapest, Hungary

September 7, 2007
November 7, 2007
March 20, 2008
mobile/indoor robots, vision-based obstacle detection, autonomous/local/grobal navigation, potential field based guiding, A* algorithm
Recently, autonomous navigation has become an important research topic. There are a lot of applications where the need for autonomous robots is obvious, either because the real or virtual human presence is impossible, dangerous, or expensive, or the tasks to be solved are against the human nature. In most of the applications where robots are to be used, the conditions/environment change along the time that results in an ever-increasing need for universal methods, which are general enough to be used at a wide range of problems. In this paper, a universal, hybrid navigation method is proposed, which is able to work in cases of known, partially known, dynamically changing, or unknown environments. The model consists of two parts which are able to co-operate or to work alone. The modules combine two techniques that deal with a priori information and sensory data separately, thus blends the intelligence and optimality of global navigation methods with the reactivity and low complexity of local ones. The first, global navigation module, based on a priori information, chooses intermediary goals for the local navigation module, for which the so called A* algorithm is used. The second part, carrying out the (local) navigation relying on sensory data, applies a fuzzy-neural representation of an improved potential field based guiding navigation tool. Vision based obstacle detection is implemented by difference detection based on a combination of RGB and HSV representations of the pixels.
Cite this article as:
A. Várkonyi-Kóczy, “A Universal Autonomous Robot Navigation Method,” J. Adv. Comput. Intell. Intell. Inform., Vol.12 No.2, pp. 206-213, 2008.
Data files:
  1. [1] M. Sugeno, H. Winston, I. Hirano, and S. Kotsu, “Intelligent Control of an Unmanned Helicopter Based on Fuzzy Logic,” Proc. of the NATO ASI Conf. on Soft Computing and Its Application, Antalya, Turkey, 1996.
  2. [2] D. Wettergreen, C. Gaskett, and A. Zelinsky, “Development of a Visually-Guided Autonomous Underwater Vehicle,” Proc. of the IEEE Conf. on OCEANS98, 1998.
  3. [3] H. Blåsvær, P. Pirjanian, and H. I. Christensen, “AMOR - An Autonomous Mobile Robot Navigation System,” Proc. of the IEEE Int. Conf. on Systems, Man, and Cybernetics, 3, pp. 2266-2271, 1994.
  4. [4] J. Latombe, “Robot Motion Planning,” Kluwer Academic Publishers, Boston, MA, USA, 1991.
  5. [5] J. Borenstein and Y. Koren, “The Vector Field Histogram - Fast Obstacle Avoidance for Mobile Robots,” IEEE Transactions on Robotics and Automation, Vol.7, No.3, pp. 278-288, 1991.
  6. [6] A. Saffiotti, E. H. Ruspini, and K. Konolige, “Using Fuzzy Logic for Mobile Robot Control,” In: Practical applications of fuzzy technologies. (H.-J. Zimmermann (Ed.)), Kluwer Academic Publishers, pp. 185-206, 1999.
  7. [7] G. Cheng and A. Zelinsky, “Goal-oriented Behaviour-based Visual Navigation,” Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA’98), Leuven, Belgium, 1998.
  8. [8] E. Fabrizi and A. Saffiotti, “Extracting Topology-Based Maps From Gridmaps,” Proc. of the IEEE Int. Conf. on Robotics and Automation, ICRA’2000, pp. 2972-2978, San Francisco, CA, USA, 2000.
  9. [9] M. Bertozzi, A. Broggi, and A. Fascioli, “Real-Time Obstacle Detection using Stereo Vision,” Proc. of the VIII European Signal Processing Conf., EUSIPCO-96, pp. 1463-1466, Trieste, Italy, 1996.
  10. [10] K. Steinkraus and L. P. Kaelbling, “Optical Flow for Obstacle Detection in Mobile Robots,” Artificial Intelligence Laboratory, MIT, 2001.
  11. [11] M. Grünewald and J. Sitte, “A Resource-Efficient Approach to Obstacle Avoidance via Optical Flow,” Proc. of the 5th Int. Heinz Nixdorf Symposium: Autonomous Minirobots for Research and Edutainment (AMIRE), HNI-Verlags-schriftenreihe 97, pp. 205-214, Heinz Nixdorf Institute, 2001.
  12. [12] L. M. Lorigo, R. A. Brooks, and W. E. L. Grimson, “Visually-Guided Obstacle Avoidance in Unstructured Environments,” Proc. of the IEEE Conf. on Intelligent Robots and Systems, Grenoble, France, 1997.
  13. [13] C. Gaskett, L. Fletcher, and A. Zelinsky, “Reinforcement Learning for a Vision Based Mobile Robot,” Robotic Systems Laboratory, The Australian National University, 2000.
  14. [14] A. Saffiotti, “Fuzzy Logic in Autonomous Robotics: Behavior Coordination,” Proc. of the 6th IEEE Int. Conf. on Fuzzy Systems, pp. 573-578, Barcelona, Spain, 1997.
  15. [15] S. W. Soliday, “A Subsystem Approach to Developing a Behavioral Based Hybrid Navigation System For Autonomous Vehicles,” Master’s Thesis, Department of Electrical Engineering, North Carolina A&T University, Greensboro, NC, USA, 1995.
  16. [16] E. Fabrizi and A. Saffiotti, “Behavioral Navigation on Topology-Based Maps,” Proc. of the 8th Int. Symposium on Robotics with Applications, Maui, Hawaii, USA, 2000.
  17. [17] M. Visontai, Sz. Szabó, P. Baranyi, A. R. Várkonyi-Kóczy, and L. Kiss, “3-Dimensional Potential Based Guiding,” Proc. of the IEEE Conf. on Intelligent Engineering Systems, INES2000, pp. 306-309, Portorož, Slovenia, 2000.
  18. [18] M. Visontai, Sz. Szabó, A. R. Várkonyi-Kóczy, P. Baranyi, G. Samu, and L. Kiss, “Complexity Problem of the Potential Based Guiding,” Proc. of the IFAC Symposium on Artificial Intelligence and Real-Time Control, AIRTC2000, pp. 145-149. Budapest, Hungary, 2000.
  19. [19] P. Korondi, A. R. Várkonyi-Kóczy, Sz. Kovács, P. Baranyi, and M. Sugiyama, “Virtual Training of Potential Function Based Guiding Styles,” Proc. of the Joint 9th IFSA World Congress and 20th NAFIPS Int. Conf., IFSA/NAFIPS 2001, pp. 2529-2534, 2001.
  20. [20] L. Kiss, A. R. Várkonyi-Kóczy, and P. Baranyi, “Autonomous Navigation in a Known Dynamic Environment,” proc. of the 12th IEEE Int. Conf. on Fuzzy Systems, FUZZ-IEEE’2003, pp. 266-271, St. Louis, USA, 2003.
  21. [21] J. Gasós and A. Saffiott, “Integrating Fuzzy Geometric Maps and Topological Maps for Robot Navigation,” Proc. of the 3rd Int. Symposium on Soft Computing (SOCO), Genova, Italy, 1999.
  22. [22] S. Thrun, J. Gutmann, D. Fox, W. Burgard, and B. Kuipers, “Integrating Topological and Metric Maps for Mobile Robot Navigation: A Statistical Approach,” Proc. of the 15th National Conf. on Artificial Intelligence (AAAI), pp. 989-995. Madison, WI, USA, AAAI/MIT Press, 1998.
  23. [23] S. Thrun, “Learning Maps for Indoor Mobile Robot Navigation,” Artificial Intelligence, Vol.99, pp. 21-71, 1998.
  24. [24] S. J. Russell and P. Norvig, “Artificial Intelligence – A Modern Approach,” Prentice-Hall, 1995.
  25. [25] A. Stentz, “Map-Based Strategies for Robot Navigation in Unknown Environments,” Proc. of the AAAI Spring Symposium on Planning with Incomplete Information for Robot Problems, 1996.
  26. [26] A. Stentz, “Optimal and Efficient Path Planning for Partially-Known Environments,” Proc. Of the IEEE Int. Conf. on Robotics and Automation , San Diego, CA, Vol.4, pp. 3310-3317, May 1994.
  27. [27] C. Tovey, S. Greenberg, and S. Koenig, “Improved analysis of D*,” Proc. of the IEEE Int. Conf. on Robotics and Automation, ICRA’2003, Vol.3, pp. 3371- 3378, 2003.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jun. 03, 2024