A Genetic Robot Path Planner with Fuzzy Logic Adaptation

Abstract

The paper develops a combined genetic algorithm and fuzzy logic approach to path planning for a mobile robot operating in rough environments. Path planning consists of a description of the environment using a fuzzy logic framework, and a two-stage planner. A global planner determines the path that optimizes a combination of terrain roughness and path curvature. A local planner uses sensory information, and in case of detection of previously unknown and unaccounted for obstacles, performs an on-line planning to get around the newly discovered obstacle. The fuzzy adaptation of the genetic operators is achieved by adjusting the probabilities of the genetic operators based on a diversity measure of the population and traversability measure of the path. Path planning for an articulate rover in a rugged Mars terrain is presented to demonstrate the effectiveness of the proposed path planner.