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.