Skip to main content
SpringerLink
Log in

Neuro-fuzzy Control of Autonomous Robotics

  • Reference work entry
Encyclopedia of Complexity and Systems Science

  • 227 Accesses

Definition of the Subject

Autonomous robots are robots which can perform desired tasks inunstructured environments without requiring continuous human guidance. Most of the times, the dynamics of the robot itself can be describedanalytically. Unfortunately, in many robotic applications, it is difficult if not impossible to obtain a precise mathematical model of theenvironment and its interaction with the robot through actuators and sensors. The lack of complete and precise knowledge about the environment limits theapplicability of conventional control system design to the domain of autonomous robotics. Some of the requirements for a robot to successfullyachieve autonomy are the possibility to acquire knowledge about the environment and itself, to reason under uncertainty and to have learning capabilitiesin order to adapt to the environment based on accumulated experience.

Efficient control algorithms for autonomous robots should imitate the way humans are operating manned or similar...

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

Access this chapter

Log in via an institution

Institutional subscriptions

Abbreviations

Robot:

The word ‘robot’ was introduced by the Czech playwright Capek in his 1920 play Rossum's Universal Robots. The word ‘robota’ in Czech means simply ‘work’. Although there is no definition accepted by everyone, in this chapter a robot is considered to be a human-built machine that is mobile, can sense and interact with the environment, and has the necessary intelligence in order to handle unforeseen circumstances autonomously. Most important than all, it has to do a useful task.

Autonomy:

Independence of control, self‐sufficiency. Applied to robots, it implies the ability of the robot to find solutions by itself to the various problems that might appear while completing the assigned task.

Fuzzy logic:

The idea of fuzzy logic was first advanced by Dr. Lotfi Zadeh of the University of California at Berkeley in the 1960s. It came from the fact that natural language can not be easily translated in the absolute terms of 0 and 1. Fuzzy logic includes 0 and 1 as extreme cases of truth (that are representations of certainty or facts), but also includes the various states of truth in between (partial truth). As an example, using binary logic it can be said that “the target is on the left side of the robot” or “the target is not on the left side of the robot”, while using fuzzy logic a more precise description can be given, like “the target is 20% on the left side of the robot”.

Neural networks:

A neural network can be described as a system composed of many simple processing elements operating in parallel whose function is determined by network structure, connection strengths, and the processing performed at computing elements or nodes. Some of its advantages are: non‐linear mappings, adaptation and learning, ease of implementation and self‐organization.

Control:

When talking about controlling systems, control refers to the process of changing the input of the system such that its output reaches a desired value. Most of the time, the control is done in a closed loop, where the output of the system is continuously compared to the reference and the necessary control command is applied to the inputs of the system in order to reduce the error at the output.

Behavior:

Small independent decision‐making process that fully implements a control policy for one specific sub-task. Usually multiple behaviors coexist and are enabled or disabled by an arbiter, depending on which is useful in a particular situation.

Mapping:

Mapping is split into two main processes that are dependent on each other: map‐learning and localization. The first has to store the information from the robot sensors into a suitable internal representation (map). The latter has to estimate the position of the various objects on the map. Building the map needs localization, but in the same time localization requires a map.

Path planning:

The process where, given a complete description of the geometry of a robot and the static environment populated with obstacles, a collision‐free path must be found such that the robot can move from an initial position and orientation to a goal position and orientation.

Bibliography

Primary Literature

  1. Andrews JR, Hogan N (1983) Impedance Control as a Framework for Implementing Obstacle Avoidance in a Manipulator. In: Hardt DE, Book W (eds) Control of Manufacturing Processes and Robotic Systems. ASME, Boston, pp 243–251

    Google Scholar 

  2. Beom HR, Cho HS (1995) A sensor‐based navigation for a mobile robot using fuzzy logic and reinforcement learning. IEEE Trans Syst Man Cybern 25:464–477

    Google Scholar 

  3. Brooks RA (1986) A Robust Layered Control System For A Mobile Robot. IEEE J Robot Autom RA-2:14–23

    MathSciNet  Google Scholar 

  4. Burke RE (2001) Spinal Reflexes. In: Levitan I (ed) Encyclopedia of Life Sciences. Wiley, Chichester

    Google Scholar 

  5. Chatterjee A, Matsuno F (2006) Improving EKF-based solutions for SLAM problems in Mobile Robots employing Neuro–Fuzzy Supervision. 3rd International IEEE Conference Intelligent Systems, London, pp 683–689

    Google Scholar 

  6. Clerc M, Kennedy J (2002) The particle swarm‐explosion, stability and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73

    Google Scholar 

  7. Colombetti M, Dorigo M (1996) Behavior analysis and training- A methodology for behavior engineering. IEEE Trans Syst Man Cybern B 26:365–380

    Google Scholar 

  8. Dissanayake MWMG, Newman P, Clark S, Durrant–Whyte HF (2001) A solution to the simultaneous localization and map building (SLAM) problem. IEEE Trans Robot Autom 17(3):229–241

    Google Scholar 

  9. Donnart J, Meyer J (1996) Learning reactive and planning rules in a motivationally autonomous animation. IEEE Trans Syst Man Cybern B 26:381–395

    Google Scholar 

  10. Donoghue JP, Sanes JN (2001) Motor System Organization. In: Levitan I (ed) Encyclopedia of Life Sciences. Wiley, Chichester

    Google Scholar 

  11. Fitzgerald RJ (1971) Divergence of the Kalman filter. IEEE Trans Autom Control AC-16(6) 736–747

    Google Scholar 

  12. Homaifar A, McCormick E (1995) Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms. IEEE Trans Fuzzy Syst 3:129–139

    Google Scholar 

  13. Hebert T (1998) Navigation of an autonomous vehicle using a combined electrostatic potential field/fuzzy inference approach.Ph.D. dissertation, University of Southwestern Louisiana, Lafayette

    Google Scholar 

  14. Juang CF, Lin CT (1998) An on-line self‐constructing neural fuzzy inference network and its applications. IEEE Trans Fuzzy Syst 6:12–32

    Google Scholar 

  15. Khatib O (1985) Real-time obstacle avoidance for manipulators and mobile robots. IEEE Int Conf Robotics and Automation, St. Louis, MO, pp 500–505

    Google Scholar 

  16. Lefebvre DR, Saridis GN (1991) A Computer Architecture for Intelligent Machines. Intelligent Robotic Systems for Space Exploration. Rensselaer Polytechnic Institute Troy, New York, pp 31–43

    Google Scholar 

  17. Lewis FL (1986) Optimal Estimation: With an Introduction to Stochastic Control Theory. Wiley–Interscience, New York

    Google Scholar 

  18. Lewis FL, Jagannathan S, Yesildirek A (1998) Neural Network Control of Robot Manipulators and Nonlinear Systems. Taylor & Francis, Inc., Bristol

    Google Scholar 

  19. Loebis D, Sutton R, Chudley J, Naeem W (2004) Adaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system. Control Eng Pract 12:1531–1539

    Google Scholar 

  20. Mehra RK (1970) On the identification of variances and adaptive Kalman filtering. IEEE Trans Autom Control AC-15(2):175–184

    MathSciNet  ADS  Google Scholar 

  21. Ojeda L, Borenstein J (2002) FLEXnav: Fuzzy Logic Expert Rule-based Position Estimation for Mobile Robots on Rugged Terrain. Proceedings of the 2002 IEEE International Conference on Robotics and Automation, Washington DC,pp 317–322

    Google Scholar 

  22. Park J, Sandberg IW (1991) Universal approximation using radial‐basis‐function networks. Neural Comput 3:246–257

    Google Scholar 

  23. Ruspini EH (1991) Truth as utility: a conceptual synthesis. In: Proc 7th Conf Uncertainty in Artificial Intelligence, Los Angeles, CA

    Google Scholar 

  24. Ruspini EH (1990) Fuzzy logic in the Flakey robot. Proc Int Conf Fuzzy Logic and Neural Networks (IIZUKA), Iizuka, Japan, pp 767–770

    Google Scholar 

  25. Saffiotti A (1997) The Uses of Fuzzy Logic in Autonomous Robotics: a catalogue raisonne. In: Degli Antoni G (ed) Soft Computing I(4). Springer, Berlin, pp 180–197

    Google Scholar 

  26. Saffiotti A, Konolige K, Ruspini EH (1995) A multivalued‐logic approach to integrating planning and control. Artif Intell 76:(1–2):481–526

    Google Scholar 

  27. Saffiotti A, Ruspini EH, Konolige K (1993) Blending reactivity and goal‐directedness in a fuzzy controller. In: Proceedings of the IEEE Int Conf Fuzzy Systems, San Francisco, California, pp 134–139

    Google Scholar 

  28. Saridis GN, Stephanou HE (1977) A hierarchical approach to the control of a prosthetic arm. IEEE Trans Syst Man Cybern SMC-7(6):407–420

    Google Scholar 

  29. Thrun S, Burgard W, Fox D (2005) Probabilistic Robotics. MIT Press, Cambridge

    MATH  Google Scholar 

  30. Valavanis KP, Hebert T, Kolluru R, Tsourveloudis NC (2000) Mobile robot navigation in 2-D dynamic environments using electrostatic potential fields. IEEE Trans Syst Man Cybern A 30:187–197

    Google Scholar 

  31. Welch G, Bishop G (2004) An introduction to the Kalman filter. Technical Report TR 95-041, University of North Carolina, Department ofComputer Science, Chapel Hill

    Google Scholar 

  32. Wang LX (1992) Fuzzy systems are universal approximators. In: Proceedings of the 1st IEEE Conference on Fuzzy Systems, San Diego, pp 1163–1170

    Google Scholar 

  33. Werbos PJ (1998) Backpropagation: basics and new developments. The handbook of brain theory and neural networks. MIT Press, Cambridge, pp 134–139

    Google Scholar 

  34. Ye C, Yung NHC, Wang D (2003) A fuzzy controller with supervised learning assisted reinforcement learning algorithm for obstacle avoidance. IEEE Trans Syst Man Cybern B 33(1):17–27

    Google Scholar 

  35. Yen J, Pfluger N (1995) A Fuzzy Logic Based Extension to Payton and Rosenblatt's Command Fusion Method for Mobile Robot Navigation. IEEE Trans Syst Man Cybern 25(6):971–978

    Google Scholar 

  36. Zadeh LA (1973) Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans Syst Man Cybern SMC-3:28–44

    MathSciNet  Google Scholar 

Books and Reviews

  1. Ge SS, Lewis FL (2006) Autonomous Mobile Robots: Sensing, Control, Decision Making and Applications. Autom Control Eng Ser 22:229–265

    Google Scholar 

  2. Tzafestas SG (1999) Advances in Intelligent Autonomous Systems. Microprocessor–Based and Intelligent Systems Engineering Series, vol 18. Springer, New York

    Google Scholar 

  3. Bekey GA (2005) Autonomous Robots: From Biological Inspiration to Implementation and Control. Intelligent Robotics and Autonomous Agents Series, MIT Press, Cambridge

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Automation & Robotics Research Institute, University of Texas at Arlington, Fort Worth, USA

    Petru Emanuel Stingu & Frank L. Lewis

Authors
  1. Petru Emanuel Stingu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Frank L. Lewis
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. RAMTECH LIMITED, 122 Escalle Lane, Larkspur, CA, 94939, USA

    Robert A. Meyers Ph. D. (Editor-in-Chief) (Editor-in-Chief)

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag

About this entry

Cite this entry

Stingu, P.E., Lewis, F.L. (2009). Neuro-fuzzy Control of Autonomous Robotics. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, New York, NY. https://doi.org/10.1007/978-0-387-30440-3_357

Download citation

Publish with us

Policies and ethics

Search

Navigation