Genetic algorithm using sub-path codes for mobile robot path planning

Abstract EN

In this paper, a new method for finding global optimal path planning is proposed using a Genetic Algorithm (GA).

A map of known static environment as well as a start node and a target node connecting an optimal path which is required to be found are given beforehand.

The chosen nodes in a known static environment are connected by sub-paths among each other.

Each path is represented by a series of subpaths which connect the sequential nodes to form this path.

Each sub-path radiating from each node is labeled by an integer.

The chromosome code of a path is a string of series integers that represent the labels of sub-paths which are passed through traveling from start node to target node.

Two factors are integrated into a fitness function of the proposed genetic algorithm: the feasibility of collision avoidance path and the shortest distance of path.

Two examples of known static environment maps are taken in this study with different numbers of obstacles and nodes.

Simulation results show the effectiveness and feasibility of the proposed GA using sub-path codes to find optimum path planning for mobile robot.