Path planning using genetic algorithms for mini-robotic tasks

Abstract

We present a genetic algorithm-based method to optimize trajectory planning for mini-robotic tasks. Codifying a number of motion primitive parameters into computational chromosomes does this. Each trajectory is composed of a fixed number N of straight segments. We search with a genetic algorithm the length and direction parameters of the N path segments that let us to arrive a target position from the current robot position. We show design choices of the genetic operators (selection, mutation and fitness function) used in our genetic algorithm implementation. We present simulations of our method and experimentation on a mini-robotic platform is implemented.