Artificial Intelligence
Performance Analysis of Micromouse Algorithms
Supervised by Prof. Dr. Md. Ashraful Hoque, Professor, Department of Electrical and Electronic Engineering, IUT.
-An autonomous maze solving robot which is designed to find a predetermined destination of an unknown maze is commonly known as “Micromouse”. An ideal algorithm should be able to solve the maze in the shortest possible time. The algorithm also should have the potential to solve all possible maze combination. Several maze solving algorithms have been developed and repeatedly being used. However each algorithm has its own constraint and it is required to analyze their performance. Our research is focused on the performance analysis and simulation of traditional micromouse algorithms. We have done the simulation and juxtaposed the simulated outcome to focus the merits and demerits of these algorithms. The results have been compared based on some factors such as time, turn, cells traversed etc. Based on the analysis of the results we have come out with some suggestions about how the algorithms behave in different maze-environment.
A Comprehensive and Comparative Study of Maze-Solving Techniques by Implementing Graph Theory
- Solving a 3-D square maze through an autonomous robot is gaining immense popularity among the robotics aspirants. IEEE has established a set of rule for this and launched a competition named “Micromouse” where an autonomous robot or mice solves an unknown maze. Without deploying Artificial Intelligence technique it’s not possible to do this task efficiently. Several algorithms which originate from graph theory (GT) and non graph theory (NGT) are currently being used to program the robot or mice. In this paper we have elucidated how graph theory can be used to solve mazes. With adequate investigation it is verified how graph theory dominates over non graph theory algorithms. The process of generating maze solving algorithm from graph theory is also explained. To compare the algorithms efficiency, they are simulated artificially and a comprehensive study is done by interpreting the statistics of interest. The simulation results lead us towards a conclusion about the nature, behavior and efficiency of these algorithms. Upon considering all the regulating factors which can alter the performance of an algorithm, some proposals have been drawn. It will be helpful to any micro mouse aspirant while choosing an algorithm to design the robot.
