Multiple objective Genetic Algorithms for autonomous mobile robot path planning optimization

This paper describes the use of a Genetic Algorithm (GA) for the problem of Offline Point-to-Point Autonomous Mobile Robot Path Planning". The problem consist of generating "valid" paths or trajectories, for the robot to use to move from a starting position to a destination across a flat map of a terrain, represented by a 2 dimensional grid, with obstacles and dangerous ground that the Robot must evade. This means that the GA optimizes possible paths based on two criteria: length and difficulty. First, we decided to use a Conventional GA to evaluate its ability to solve this problem (using only one criteria for optimization), and due to the fact that we want to optimize paths under the two criteria or objectives, then we extended the Conventional GA to implement the ideas of Pareto optimality, making it a Multiple Objective Genetic Algorithm (MOGA). We present useful performance measures and simulation results of the Conventional GA and of the MOGA that show that both Genetic Algorithms are effective tools for solving the point-to-point robot path planning problem.