Mobile Robot Path Planning Based on Genetic Algorithm and Immune Genetic Algorithm

Abstract

Based on the Genetic Algorithm (GA) and Immune Genetic Algorithm (IGA), this paper discusses the mobile robot path planning with different velocity constraints. If the cost function for the optimal robotic path planning is defined as the distance travelled by a robot, the optimal solution thus means the shortest path. However, in some cases, a mobile robot may move in different velocities due to the different terrain conditions. Under such cases, the shortest path will not necessarily represent the shortest time. GA and IGA are utilized to solve the optimal robotic path planning issue considering obstacle avoidance and velocity constraints. Four different terrain conditions are applied in this study, and four different moving velocities are assumed, respectively. Simulation results indicate that both GA and IGA will work effectively and efficiently to get the optimal path for a robotic navigation. Between them, IGA can get the optimal results with fewer iterations.