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This paper presents new hybrid control architecture-based Interval Type-2 Neuro-Fuzzy (IT2NF) for embedded mobile robot navigation where event-driven control is used to handle the dynamically changing of the environment. The proposed hybrid control architecture combining behavior-based reactive navigation and model-based environmental classification has been developed. Weightless Neural Network (WNNs) in charge of environmental classification, this strategy does not only enable the mobile robot to avoid local minimum points, but also eliminates the requirement for prior detailed modeling of the environment. Then, IT2FLC based reactive behavior is utilized to perform mobile robot navigation task use environmental pattern classification.
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