Autonomous local path planning for a mobile robot using a genetic algorithm

This work presents results of our work in development of a genetic algorithm based path-planning algorithm for local obstacle avoidance (local feasible path) of a mobile robot in a given search space. The method tries to find not only a valid path but also an optimal one. The objectives are to minimize the length of the path and the number of turns. The proposed path-planning method allows a free movement of the robot in any direction so that the path-planner can handle complicated search spaces.

1. Introduction

During the last century, automation has become an extremely fast growing phenomenon impacting almost all facets of everyday life. Recently, robots have become a major part of this trend. Therefore, autonomously navigating robots have become increasingly important [1]–[3]. Motion planning [2] is one of the important tasks in intelligent control of an autonomous mobile robot [7]–[9]. The work presented here is part of a larger project to build an autonomous path-planning robot. This research is motivated by earlier work in this field of interest [4]–[6] by the same research team. This paper presents the research and simulation results of a genetic algorithm based path-planning software. The algorithm uses an improved, modified version of previous encoding techniques [4]–[6].