A Comprehensive and Comparative Study Of Maze-Solving Techniques by Implementing Graph Theory

Abstract

This paper presents an efficient maze solving algorithm. IEEE has launched a competition named where an autonomous robot or mice solves an unknown maze. The mouse find its way from the starting position to the central area of the maze without any intervention. To solve the maze, the mice implements one of many different searching algorithms such as the DFS, flood fill, BFS, flood fill. Several algorithms which originate from graph theory (GT) and non-graph theory (NGT) are currently being used to program the robot or mice. To compare the algorithms efficiency, they are simulated artificially and a comprehensive study is done by interpreting the statistics of interest. I. Introduction A maze is a puzzled way which consists of different branch of passages where the aim of the micro mouse is to reach the destination by finding the most efficient route within the shortest possible time. Micro mouse is a small wheeled-robot that consisting infrared sensors, motors and controller and act. Artificial Intelligence plays a vital role in defining the best possible way of solving any maze effectively. Graph theory appears as an efficient tool while designing proficient maze solving techniques. Graph is a representation or collection of sets of nodes and edges. This concept is deployed in solving unknown maze consisting of multiple cells. Depending on the number of cells, maze dimension may be 8x8, 16x16 or 32x32. The IEEE standard maze has 16×16 square cells, and each cells length and width are both 18 centimeters. Each cell can be considered as a node which is isolated by walls or edges. By incorporating intelligent procedure with the existing graph theory algorithms, some Micro mouse Algorithms have been developed i.e. flooding, DFS (Depth First Search) in Micro mouse. Analytical approach is taken to evaluate BFS (Breadth First Search). To increase the efficiency of flood fill algorithm, some changes have been made and eventually modified flood-fill appears. The performance and outcome of these algorithms are also analyzed and compared. With adequate reference and observation it is obvious that graph theory technique is the most efficient in solving Micro mouse mazes than other techniques. A more generalized algorithm (not originated from graph theory) is described in section III. Section IV is based on the simulation and analysis of these algorithms. Section V is based on comparison and decision taking. Finally some conclusions have been drawn by interpreting the simulation result.