Time-optimal, collision-free navigation of a car-like mobile robot using neuro-fuzzy approaches

Neuro-fuzzy approaches are developed, in the present work, to determine time-optimal, collision-free path of a car-like mobile robot navigating in a dynamic environment. A fuzzy logic controller (FLC) is used to control the robot and the performance of the FLC is improved by using three different neuro-fuzzy (NN-FLC) approaches. The performances of these neuro-fuzzy approaches are compared among themselves and with those of three other approaches, such as default behavior, manually-constructed FLC and potential field method, through computer simulations. The neuro-fuzzy approaches are found to perform better than the other approaches, in most of the test scenarios. Moreover, the performances of both the genetic algorithm (GA)-optimized NN-FLC (Mamdani Approach) as well as GA-optimized NN-FLC (Takagi and Sugeno Approach) are seen to be comparable. It is also interesting to note that the CPU times of all these approaches are found to be low. Thus, they might be suitable for on-line implementations.