Behavior-based neuro-fuzzy controller for mobile robot navigation
Sch. of Inf. Technol. & Eng., Univ. of Ottawa, Ont., CanadaIEEE 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. "
- "Fuzzy systems provide a framework to represent imprecise information and to reason with this kind of information, while neural networks enhance fuzzy systems with the capability of learning from input-output data; learning is used to adapt parameters of the fuzzy system as membership functions or rules. Some example representative application areas of NFS are: pattern recognition  , robotics  , nonlinear system identification  , adaptive signal processing  , etc.. In addition, new potential applications can be found in the field of ubiquitous computing and ambient intelligence . "
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- "In , 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 , 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. "
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.