Fuzzy-differential evolution algorithm for planning time-optimal trajectories of a unicycle mobile robot on a predefined path

Abstract

An evolutionary technique with a Fuzzy Inference System (FIS) is offered for planning time-optimal trajectories on a predefined Visibility Graph Method Dijkstra (VGM-D) path of a Nomad 200 mobile robot (MR). First of all, the segmented trajectory is generated by the VGM-D algorithm. Line and curve segments are the components of the trajectory. The number of intersections of the segmented VGM-D path determines the curve segments number. It is assumed that, at each curve segment, translation velocity v t is taken as constant. The Differential Evolution (DE) algorithm finds v t values of all the curve segments, which minimize the trajectory tracking time. Line segments lengths are used to calculate the constraints of the problem according to the Nomad 200's limitations on the translation velocity and acceleration/deceleration. The structures of the curve segments are modeled by FIS to decrease the DE's execution time. Another FIS model is used to define the upper bound of the translation velocities on the curve segments for the same purpose. Both FIS models are trained by the adapted-network-based fuzzy inference system (ANFIS). Experiments are successfully implemented on the Nomad 200 MR.