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

ABSTRACT 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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    • "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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    • "of FC for mobile robot navigation or wall-following control in unknown environments. However, precise input-output training data for supervised learning should be collected in advance in studies [22] [23] [24] [25] [28] [30] [31]. In the EGPSO-based FC navigation method, no training data are collected in advance. "
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    ABSTRACT: This paper proposes an evolutionary-group-based particle-swarm-optimization (EGPSO) algorithm for fuzzy-controller (FC) design. The EGPSO uses a group-based framework to incorporate crossover and mutation operations into particle-swarm optimization. The EGPSO dynamically forms different groups to select parents in crossover operations, particle updates, and replacements. An adaptive velocity-mutated operation (AVMO) is incorporated to improve search ability. The EGPSO is applied to design all of the free parameters in a zero-order Takagi-Sugeno-Kang (TSK)-type FC. The objective of EGPSO is to improve fuzzy-control accuracy and design efficiency. Comparisons with different population-based optimizations of fuzzy-control problems demonstrate the superiority of EGPSO performance. In particular, the EGPSO-designed FC is applied to mobile-robot navigation in unknown environments. In this application, the robot learns to follow object boundaries through an EGPSO-designed FC. A simple learning environment is created to build this behavior without an exhaustive collection of input-output training pairs in advance. A behavior supervisor is proposed to combine the boundary-following behavior and the target-seeking behavior for navigation, and the problem of dead cycles is considered. Successful mobile-robot navigation in simulation and real environments verifies the EGPSO-designed FC-navigation approach.
    IEEE Transactions on Fuzzy Systems 05/2011; 19(2-19):379 - 392. DOI:10.1109/TFUZZ.2011.2104364 · 8.75 Impact Factor
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    • "Depending on the definition of the problem, the literature offers several approaches based on different methodologies. Relative to the environment, these approaches can be classified depending on whether the vehicle moves in a known, partially known, or totally unknown environment, see Kermiche et al. (2006), Rusu et al. (2003) and Pradhan et al. (2009), respectively . Further, the environment can be stationary or dynamic – with moving obstacles (Hui and Pratihar, 2009), or a moving target (Glasius et al., 1995). "
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    ABSTRACT: This paper presents a new methodology for the avoidance of one or more obstacles, and for the navigation of a differential-drive mobile robot. The approach is based on fuzzy logic with virtual fuzzy magnets, and represents a reactive controller for navigation through an unknown environment to the ultimate target. Relative parameters of the obstacle in the robot's way are determined at the preprocessing stage and the algorithm is therefore applicable to obstacles of different sizes. The algorithm, designed to avoid a single stationary obstacle, was generalized and successfully applied in a multiple-obstacle navigation scenario. The efficiency of the algorithm is illustrated by computer simulations using the kinematic model of a mobile robot.
    7th IFAC Symposium on Intelligent Autonomous Vehicle, University of Salento, Lecce, Italy; 09/2010
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