Vision-Based Hierarchical Fuzzy Controller and Real Time Results for a Wheeled Autonomous Robot

Abstract

Motion Control 52 solve the truck backer-upper problem, rooted in computational intelligence, can be divided into two groups. The first group of methods seeks the solution through self tuning using neural networks, genetic algorithms or a combination of both. The second group of solutions, based on fuzzy logic, regards the controller as an emulator of human operator. The problem has become an acknowledged benchmark in non-linear control and as an example of a self-learning system in neural networks was proposed by Careful experiments of their approach showed that the computational effort is very high Thousands (about 20000) of backup cycles are needed before the network learns. Moreover the backpropagation algorithm does not converge for some sets of training samples. Numerous other techniques have been used, including genetic programming (Koza, 1992) Neuro-genetic controller (Schoenauer, & Ronald, 1994) and simplified neural network solution through problem decomposition Very interesting contribution is A simplified version of the control problem has been extensively investigated in the field of fuzzy control (Ramamoorthy &