Abstract
Autonomous control and navigation of mobile robotic vehicles are fundamental enabling technologies for automation in a variety of operating domains ranging from industrial environments to remote planetary surfaces. The engineering problem to be solved generally consists of achieving real-time sensor-based motion control among obstacles in the environment while performing useful tasks throughout its accessible regions. In many instances, mobile robots are required to do so using limited resources (e.g. power, computation, sensors, etc.) that are resident on-board the vehicle. Traditional approaches have been based on functional decomposition of tasks, which employed computationally intensive planning algorithms and explicit pre-determined world models. The resulting serial execution of sensing, modeling, planning and acting produced intelligent behavior, but at the great expense of real-time performance.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
R.C. Arkin. Motor schema based navigation for a mobile robot. In IEEE International Conference on Robotics and Automation, pages 264–271, 1987.
S. Berman, M.A.A. de Oliveira, Y. Edan, and M. Jamshidi. Hierarchical fuzzy behavior-based control of a multi-agent robotic system. In 7th IEEE mediterranean Conference on Decision and Control, Haifa, Israel, June 1999.
A. Bonarini. Some methodological issues about designing autonomous agents which learn their behaviors: the ELF experience. In R. Trappl, editor, 12th European Meeting of Cybernetics and Systems Research, pages 1435–1442. World Scientific Publishing, Singapore, 1994.
A. Bonaxini. Learning to coordinate fuzzy behaviors for autonomous agents. In 2nd European Congress on Intelligent Techniques and Soft Computing EUFIT ’94, pages 475–479, 1994.
A. Bonaxini and F. Basso. Learning the suitability of simple behaviors to obtain composite behaviors for autonomous agents. In 1st On-Line Workshop on Soft Computing (WSC1), URL: http://www.bioele.nuee.nagoya-u.ac.jp/wscl/, Aug 1996.
J. Borenstein and L. Feng. UMBmark — a method for measuring, comparing, and correcting dead-reckoning errors in mobile robots. Technical Report UM-MEAM-94–22, The University of Michigan, Ann Arbor, MI, Dec. 1994.
R.A. Brooks. A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation, RA-2(1): 14–23, 1986.
L. Correia and A. Steiger-Gargao. A useful autonomous vehicle with a hierarchical behavior control. In J.J. Merelo F. Moran, A. Moreno and P. Chacon, editors, Advances in Artificial Life, 3rd European Conference on Artificial Life, pages 625–639. Springer, Granada, Spain, 1995.
D. Driankov, H. Hellendoorn, and M. Reinfrank. An Introduction to Fuzzy Control. Springer-Verlag, Berlin, Germany, 1993.
H.R. Everett. Sensors for Mobile Robots: Theory and application. A K Peters, Ltd., Wellesley, MA, 1995.
D. Gachet, M.A. Salichs, L. Moreno, and J.R. Pimentel. Learning emergent tasks for an autonomous mobile robot. In IEEE International Conference on Intelligent Robots and Systems, pages 290–297, 1994.
S.G. Goodridge and M.G. Kay. Multi-layered fuzzy behavior fusion for reactive control of autonomous robots. In D. Driankov and A. Saffiotti, eds, Fuzzy Logic Techniques for Autonomous Vehicle Navigation, Physica-Verlag, Heidelberg, New York, 2000, pages 179–204.
S.G. Goodridge and R.C. Luo. Fuzzy behavior fusion for reactive control of an autonomous mobile robot: MARGE. In IEEE International Conference on Robotics and Automation, pages 1622–1627, 1994.
B.E. Hallam, J.R.P. Halperin, and J.C.T. Hallam. An ethological model for implementation in mobile robots. Adaptive Behavior, 3(l):51–79, Summer 1994.
B. Hallam and G. Hayes. Comparing robot and animal behaviour. DAI Research Paper No.598, University of Edinburgh, 1994.
F. Hoffmann. The role of fuzzy logic control in evolutionary robotics. In D. Driankov and A. Saffiotti, eds, Fuzzy Logic Techniques for Autonomous Vehicle Navigation, Physica-Verlag, Heidelberg, New York, 2000, pages 119–148.
F. Hoffmann, O. Malki, and G. Pfister. Evolutionary algorithms for learning of mobile robot controllers. In 1st On-Line Workshop on Soft Computing (WSCl), URL: http://www.bioele.nuee.nagoya-u.ac.jp/wscl/, Aug 1996.
K.C. Koh H.R. Beom and H.S. Cho. Behavioral control in mobile robot navigation using fuzzy decision making approach. In IEEE International Conference on Intelligent Robots and Systems, pages 1938–1945, 1994.
J.R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, 1992.
E.H. Mamdani. Twenty years of fuzzy control: Experiences gained and lessons learnt. In IEEE International Conference on Fuzzy Systems, pages 339–344, 1993.
M. Mataric and D. Cliff. Challenges in evolving controllers for physical robots. Technical Report CS-95–184, Computer Science Department, Brandeis University, Waltham, MA, November 1995.
D.J. McFarland and T. Bosser. Intelligent Behavior in Animals and Robots. MIT Press, Cambridge, MA, 1993.
F. Michaud, G. Lachiver, and C.T. Le Dinh. A new control architecture combining reactivity, planning, deliberation and motivation for situated autonomous agent. In IEEE International Conference on Fuzzy Systems, pages 258–264, 1996.
L. Moreno, E. Moraleda, M.A. Salichs, J.R. Pimentel, and A. de la Escalera. Fuzzy supervisor for behavioral control of autonomous systems. In International Conference on Industrial Electronics, Control, and Instrumentation IECON ‘ 93, pages 258–261, 1993.
M. de Oliveira and R. Barrera. A micro-controller-based hierarchical fuzzy controller for cooperative multiagent autonomous robots. In 7th International Symposium on Robotics with Applications, 3rd World Automation Congress, Anchorage, Alaska, May 1998.
M. de Oliveira, S. Berman, E. Tunstel, and M. Jamshidi. Remote surface exploration with soft-computing based cooperative rovers. In 8th Intl. Symposium on Robotics with Applications, l±th World Automation Congress, Maui, Hawaii, June 2000.
N. Pfluger, J. Yen, and R. Langari. A defuzzification strategy for a fuzzy logic controller employing prohibitive information in command formulation. In IEEE International Conference on Fuzzy Systems, pages 717–723, 1992.
F.G. Pin and S.R. Bender. Adding memory processing behaviors to the fuzzy behaviorist approach (FBA): Resolving limit cycle problems in autonomous mobile robot navigation. International Journal of Intelligent Automation and Soft Computing, 5(1):31–41, 1999.
F.G. Pin and Y. Watanabe. Resolving conflict between behaviors using suppression and inhibition. In D. Driankov and A. Saffiotti, eds, Fuzzy Logic Techniques for Autonomous Vehicle Navigation, Physica-Verlag, Heidelberg, New York, 2000, pages 151–178.
P. Pirjanian and M. Mataric. Multiple objective vs. fuzzy behavior coordination. In D. Driankov and A. Saffiotti, eds, Fuzzy Logic Techniques for Autonomous Vehicle Navigation, Physica-Verlag, Heidelberg, New York, 2000, 235–254.
A. Saffiotti, K. Konolige, and E.H. Ruspini. A multi-valued-logic approach to integrating planning and control. Artificial Intelligence, 76(1–2):481–526, 1995.
A. Saffiotti, E.H. Ruspini, and K. Konolige. Blending reactivity and goal-directedness in a fuzzy controller. In IEEE International Conference on Fuzzy Systems, pages 134–139, 1993.
H. Seraji. Traversability index: A new concept for planetary rovers. In IEEE International Conference on Robotics and Automation, Detroit, MI, 1999, pages 2006–2013.
J.E.R. Staddon. Adaptive Behavior and Learning. Cambridge University Press, New York, 1983.
L. Steels. Mathematical analysis of behavior systems. In Proceedings of the Perarc Conference, pages 88–95, Lausanne, Switzerland, Sep 1994.
H. Surmann and L. Peters. MORI A - a robot with fuzzy controlled behaviour. In D. Driankov and A. Saffiotti, eds, Fuzzy Logic Techniques for Autonomous Vehicle Navigation, Physica-Verlag, Heidelberg, New York, 2000, pages 343–366.
Togai Infralogic, Inc., Irvine, CA. Fuzzy-C Expert Systems User’s Guide, 1990.
E. Tunstel. Mobile robot autonomy via hierarchical fuzzy behavior control. In 6th International Symposium on Robotics and Manufacturing, 2nd World Automation Congress, pages 837–842, Montpellier, Prance, May 1996.
E. Tunstel. Fuzzy behavior modulation with threshold activation for autonomous vehicle navigation. In 18th International Conference of the North American Fuzzy Information Processing Society, New York, NY, 1999, pages 776–780.
E. Tunstel, H. Danny, T. Lippincott, and M. Jamshidi. Fuzzy behavior-based navigation for planetary microrovers. In NASA University Research Centers Technical Conference, pages 729–734, Albuquerque, NM, Feb. 1997.
E. Tunstel, H. Danny, T. Lippincott, and M. Jamshidi. Adaptive fuzzy-behavior hierarchy for autonomous navigation. In IEEE International Conference on Robotics and Automation, pages 829–834, Albuquerque, NM, Apr. 1997.
E. Tunstel, T. Lippincott, and M. Jamshidi. Behavior hierarchy for autonomous mobile robots: Fuzzy-behavior modulation and evolution. International Journal of Intelligent Automation and Soft Computing, 3(l):37–49, 1997.
R.R. Yager and D.P. Filev. Essentials of Fuzzy Modeling and Control. Wiley and Sons, New York, 1994.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Tunstel, E.W. (2001). Fuzzy-Behavior Synthesis, Coordination, and Evolution in an Adaptive Behavior Hierarchy. In: Driankov, D., Saffiotti, A. (eds) Fuzzy Logic Techniques for Autonomous Vehicle Navigation. Studies in Fuzziness and Soft Computing, vol 61. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1835-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-7908-1835-2_9
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-2479-7
Online ISBN: 978-3-7908-1835-2
eBook Packages: Springer Book Archive