Behavior-based neuro-fuzzy controller for mobile robot navigation

  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)},
  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. 
Multivalued adaptive neuro-fuzzy controller for robot vehicle - art. no. 1406
This paper presents the implementation of a multivalued neurofuzzy controller for autonomous ground vehicle (AGVs) in indoor environments that consists of a hierarchy of mobile robot usingMultivalued adaptive neuro- fuzzy inference system behaviors.
An autonomous mobile robot control method based on hierarchical fuzzy behaviors
The paper proposes a new hierarchical fuzzy behavior-based control architecture of an autonomous mobile robot using the information extracted from its sensors in unknown environments, and focuses on
Behavior Fusion of Robot Navigation Using a Fuzzy Neural Network
  • K. Song, Jean-Yuan Lin
  • Computer Science
    2006 IEEE International Conference on Systems, Man and Cybernetics
  • 2006
This paper designs three behaviors for robot navigation, including obstacle avoidance, wall following, and goal seeking, and implements these primitive behaviors by using fuzzy-logic control approaches and using the proposed behavior-fusion neural network.
New Controller for Mobile Robots Based on Fuzzy Logic and Genetic Algorithms
This paper shows a new model of controller for navigation of mobile robots in static environments with fixed obstacles based on fuzzy logic, based on modern theories concerning genetic algorithms.
Neuro-fuzzy Controlled Autonomous Mobile Robotics System
  • K. Mutib, E. Mattar
  • Computer Science
    2011 UkSim 13th International Conference on Computer Modelling and Simulation
  • 2011
A Neurofuzzy navigation strategy for sensor-based mobile robotics system that learns, trains, and adapts itself to the environment to meet various intelligence requirements for the free navigation and obstacle avoidance.
Path following behavior for an autonomous mobile robot using neuro-fuzzy controller
The objective of this paper is to elaborate an intelligent control system for the path following behavior by mobile robot using a neuro-fuzzy controller that refers to the way of applying learning techniques offered by neural networks for fuzzy systems parameter identification.
Fuzzy Logic-Genetic Algorithm-Neural Network for Mobile Robot Navigation: A Survey
A Comprehensive survey on navigation of mobile robot using fuzzy logic and genetic algorithm is presented. Navigation of mobile robot needs to find the collision free path in the environment with
An analysis of the neuro-fuzzy controllers and algorithms for their generation and the combination of neural networks with fuzzy logic offers the opportunity for resolving the difficulties of the proper generation of fuzzy controller.
Autonomous robotic sensor agents
This paper discusses development aspects of a network of intelligent wireless autonomous robotic sensor agents deployed in the field for an active investigation of complex environmental parameters.
Fuzzy-Based Controller for Differential Drive Mobile Robot Obstacle Avoidance
A new methodology for the avoidance of one or more obstacles, and for the navigation of a differential-drive mobile robot, based on fuzzy logic with virtual fuzzy magnets that was generalized and successfully applied in a multiple-obstacle navigation scenario.


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
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
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
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
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
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
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
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