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A New Intelligent Motion Planning for Mobile Robot Navigation using Multiple Adaptive Neuro-Fuzzy Inference System |
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PP: 2527-2535 |
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Author(s) |
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Prases K. Mohanty,
Dayal R. Parhi,
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Abstract |
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| Nowadays intelligent tools such as fuzzy inference system (FIS), artificial neural network (ANN) and adaptive neuro-fuzzy
inference system (ANFIS) are mainly considered as effective and suitable methods for modeling an engineering system. This paper
presents a new hybrid technique based on the combination of fuzzy inference system and artificial neural network for addressing
navigational problem of autonomous mobile robot. First we developed an adaptive fuzzy controller with four input parameters, two
output parameters and three parameters each. Afterwards each adaptive fuzzy controller acts as a single takagi-sugeno type fuzzy
inference system, where inputs are front obstacle distance (FOD), left obstacle distance (LOD), right obstacle distance (ROD) (from
robot), heading angle (HA) (angle to target) and output corresponds to the wheel velocities ( Left wheel and right wheel) for the
mobile robot. The effectiveness, feasibility and robustness of the proposed navigational controller have been demonstrated by means
of simulation experiments. The real time experimental results were verified with simulation experiments, showing that the proposed
navigational algorithm consistently performs better results to navigate the mobile robot safely in a completely or partially unknown
environment. |
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