Robotic path planning using evolutionary momentum-based exploration

In this paper we propose a new algorithm to solve the problem of robotic path planning in static environment where the source and destination are given. A grid-based map has been used to represent the robotic world. The basic algorithm is built on an evolutionary approach, where the path evolves along with generations with each generation adding to the maximum possible complexity of the path. Along with complexity we optimize the total path length as well as the minimum distance from the obstacle in the robotic path. It may be seen that the requirement of evolutionary parameter number of individuals as well as the maximum complexity is less at start and more at the later stages of the algorithm. We use a Gaussian increase in these values whose parameter may be adjusted to control the time and output. Seven Genetic Operators have been implemented that include selection, crossover, soft mutation, hard mutation, insert, delete and elite. The phenotype representation consists of the coordinate where the robot is supposed to make a turn. This happens by the traversal of the path using these points by the Evolutionary Algorithm. Momentum determines the speed of the algorithm in this traversal.