Hybrid genetic algorithm based smooth global-path planning for a mobile robot

The goal of the global path planning task is to produce an optimal safe path for robot respect to the given map. There are many global path planning methods that have been studied. In this paper, we propose a novel Hybrid Genetic Algorithm (HGA) that is used to generating smooth paths for differential wheeled robots. The main idea of HGA is to provide the dynamic mutation rate and switchable global-local search method to the mutation operator of ordinary genetic algorithm. By deploying these modifications, the premature convergence of the generic genetic algorithm and the high time-consuming fitness calculation of the memetic algorithm are reduced. HGA also takes care of chromosome length (defined by the size of a set of points that construct a path) by applying the population replacement method. Our algorithm satisfies the important criteria in the path planning task: safe and minimum-length. Based on continuous-curvature Piecewise Cubic Bezier Curve, HGA directly provides the smoothed paths for differential wheeled robots. Therefore, our proposed algorithm does not need any third-party algorithm for smoothing planned path. Several experiments regarding the proposed algorithm on our robot and its results are analyzed and presented.