Providing sufficient intelligence for embedded controller in mobile robot is desirable, to allow its interaction and operation under the conditions of imprecision and uncertainty present in the real environment with limited computational resources. This books presents an experimental work of enhanced solution, interval type-2 fuzzy logic controller (IT2FLC) that is proven to handle the uncertainties that give better performance than T1FLC. Nevertheless, IT2FLC involves extra computational overhead, associated with the computation of type-reduced fuzzy set procedure, reducing the robust performance especially when operating on embedded platform. Hence, an integration of IT2FLC and weightless neural network (WNNs), called interval type-2 neuro-fuzzy controller (IT2NFC) is proposed which is capable of learning, classifying and optimizing the rule-base to reduce the the computational overhead and improve the robustness.