Soft-computing-based embedded design of an intelligent wall/lane-following vehicle

Soft computing techniques are generally well suited for vehicular control, systems that are usually modeled by highly nonlinear differential equations and working in unstructured environments. To demonstrate their applicability in real-world applications, two intelligent controllers based on fuzzy logic and artificial neural network are designed for performing a wall-following task. Based on performance and flexibility considerations, the two controllers are implemented onto a reconfigurable hardware platform, namely a field-programmable gate array. As comparative studies of these two embedded hardware controllers designed for the same vehicular application are limited in literature, this research also presents an evaluation of the two controllers, comparing them in terms of hardware resource requirements, operational speeds, and trajectory tracking errors in following different predefined trajectories.