Behavior-based neuro-fuzzy controller for mobile robot navigation

Sch. of Inf. Technol. & Eng., Univ. of Ottawa, Ont., Canada
IEEE Transactions on Instrumentation and Measurement (Impact Factor: 1.79). 09/2003; 52(4):1335 - 1340. DOI: 10.1109/TIM.2003.816846
Source: IEEE Xplore


This 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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    • "The mechanism for orchestrating the behaviours is called arbitration. Approaches for arbitration proposed in literature include, fuzzy (Mo et al., (2013), Seraji et al., (2002), Yang et al., (2004)), Neuro-fuzzy (Rusu et al., (2003), Li et al., (1997)) and regularization (Egerstedt et al., (1999)). Though, these methods can circumvent static obstacles successfully; the result cannot be extended to dynamic obstacles in unknown environment. "

    International Journal of Heavy Vehicle Systems 10/2015; · 0.23 Impact Factor
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    • "In [1], a neuro-fuzzy reasoning algorithm, having the advantage of greatly reducing the number of fuzzy rules, was proposed to fulfill the navigation task of mobile robots. In [2], a behavior-based neuro-fuzzy controller for mobile robot system was addressed for the navigation problem. In this work, a neuro-fuzzy method was applied to implement the behavioral function. "
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    ABSTRACT: This paper aims to investigate the formation control of multi-robot systems, where the kinematic model of a differentially driven wheeled mobile robot is considered. Based on the graph-theoretic concepts and locally distributed information, an adaptive neural fuzzy formation controller is designed with the capability of on-line learning. The learning rules of controller parameters can be derived from the analyzing of Lyapunov stability. In addition to simulations, the proposed techniques are applied to an experimental multi-robot platform for performance validations. From simulation and experimental results, the proposed adaptive neural fuzzy protocol can provide better formation responses compared to conventional consensus algorithms.
    International Journal of Fuzzy Systems 09/2013; 15(3):259-370. · 1.10 Impact Factor
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    • "Recently, Neuro-fuzzy logic controllers are found well suited for controlling mobile robots, Rusu et. al. [5]. This is because, they are talented of building inferences even under certain uncertainty and unclear conditions, Kim, and Trivedi [9]. "
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    ABSTRACT: This article discusses a Neurofuzzy navigation strategy for sensor-based mobile robotics system. ATransputer computation power is used to carry out complicated needed computation (reading sensors data, deciding actions, outputting wheels data, ... system monitoring). Robot control mythology was run on a parallel computing environment known as Transputers. The Transputer embedded real-time controller was used onboard the robot to meet various intelligence requirements for the free navigation and obstacle avoidance. The control system consists of a hierarchy of robot behaviors. The mobile behavior control system was based on the use of a number of Transputers processors. Behavior methodology was based on the utilization of a structure of a five layers Neuro-fuzzy system that learns, trains, and adapts itself to the environment.
    Computer Modelling and Simulation (UKSim), 2011 UkSim 13th International Conference on; 05/2011
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