Behavior-based neuro-fuzzy controller for mobile robot navigation

@article{Rusu2002BehaviorbasedNC,
  title={Behavior-based neuro-fuzzy controller for mobile robot navigation},
  author={P. Rusu and Emil M. Petriu and Thomas E. Whalen and Aurel Cornell and Hans J. W. Spoelder},
  journal={IMTC/2002. Proceedings of the 19th IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.00CH37276)},
  year={2002},
  volume={2},
  pages={1617-1622 vol.2}
}
  • P. Rusu, E. Petriu, H. Spoelder
  • Published 7 August 2002
  • Computer Science
  • IMTC/2002. Proceedings of the 19th IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.00CH37276)
The paper discusses a neuro-fuzzy controller for sensor-based mobile robot navigation in indoor environments. The control system consists of a hierarchy of robot behaviors. 
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References

SHOWING 1-10 OF 15 REFERENCES
Autonomous navigation using an adaptive hierarchy of multiple fuzzy-behaviors
  • E. Tunstel, H. Danny, Tanya Lippincott, M. Jamshidi
  • Computer Science
    Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation'
  • 1997
TLDR
An architecture for hierarchical behavior control is presented in which control decisions result from a consensus of behavioral recommendations, and performance and robustness is demonstrated by implementation on a mobile robot with significant mechanical imperfections.
Behavior Hierarchy for Autonomous Mobile Robots: Fuzzy-Behavior Modulation and Evolution
TLDR
An approach to behavior coordination is described with emphasis on evolution of fuzzy coordination rules using the genetic programming (GP) paradigm, both conventional GP and steady-state GP are applied to evolve a fuzzy-behavior for sensor-based goal-seeking.
The uses of fuzzy logic in autonomous robot navigation
TLDR
This paper focuses on four issues: how to design robust behavior-producing modules; how to coordinate the activity of several such modules;How to use data from the sensors; and how to integrate high-level reasoning and low-level execution.
Behavior-based Control: Examples from Navigation, Learning, and Group Behavior
This paper describes the main properties of behavior-based approaches to control. Diier-ent approaches to designing and using behaviors as basic units for control, representation, and learning are
Behaviour-based control: examples from navigation, learning, and group behaviour
TLDR
Different approaches to designing and using behaviours as basic units for control, representation, and learning are illustrated on three empirical examples of robots performing navigation and path-finding, group behaviours, andlearning behaviour selection.
A robust layered control system for a mobile robot
  • R. Brooks
  • Computer Science
    IEEE J. Robotics Autom.
  • 1986
TLDR
A new architecture for controlling mobile robots is described, building a robust and flexible robot control system that has been used to control a mobile robot wandering around unconstrained laboratory areas and computer machine rooms.
An Behavior-based Robotics
TLDR
Following a discussion of the relevant biological and psychological models of behavior, the author covers the use of knowledge and learning in autonomous robots, behavior-based and hybrid robot architectures, modular perception, robot colonies, and future trends in robot intelligence.
Design of fuzzy systems using neurofuzzy networks
TLDR
A systematic approach for fuzzy system design based on a class of neural fuzzy networks built upon a general neuron model that encodes the knowledge learned in the form of if-then fuzzy rules and processes data following fuzzy reasoning principles is introduced.
ANFIS: adaptive-network-based fuzzy inference system
  • J. Jang
  • Computer Science
    IEEE Trans. Syst. Man Cybern.
  • 1993
The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive
'Autonomu navigabn using an adaptive hierarchy of multiple fuzzy khaviors,
  • Proc. of Intl. Symp. on Computational Intelligence in Robotics and Automation,pp
  • 1997
...
...