Implementation of a genetic algorithm for routing an autonomous robot

Abstract

A practical implementation of a genetic algorithm for routing a real autonomous robot through a changing environment is described. Moving around in a production plant the robot collects information about its environment and stores it in a temporal map, which is virtually a square grid, taking account of changing obstacles. The evolutional optimizer continuously searches for short paths in this map using string representations of paths as chromosomes. The main features of the implementation include physical realization, random walk exploration, temporal mapping, and dedicated genetic operators.