Automated Robot Path Planning in an Environment with Obstacles and Gain Points Using Genetic Algorithm

Abstract

Robot path planning is a well-studied problem in robotics and autonomous systems. In literature, various issues related to robot path planning in an environment with obstacles are discussed where the objectives are to minimize the distance the robot covers to reach the destination, time, cost, etc. In this paper we consider an environment having obstacles and gain points which provide some profit for the robot if it passes through it. Here the objective is to maximize the gain points and minimize the total distance the robot covers to reach the destination without passing through the obstacles. We can not find a deterministic polynomial time algorithm to meet these two objectives. We proposed a genetic algorithm-based approach to find the solution to this problem. We implemented the algorithm in Python and obtained some experimental results described in the paper.