A Low-Cost Approach to Maze Solving with Image-Based Mapping

Abstract

This paper proposes a method for solving mazes, with a special focus on navigation using image processing. The objective of this study is to demonstrate that a robot can successfully navigate a maze using only two-wheel encoders, enabled by appropriate control strategies. This method significantly simplifies the structure of mobile robots, which typically suffer from increased energy consumption due to the need to carry onboard sensors and power supplies. Through experimental analysis, it was observed that although the encoder-only solution requires more advanced control knowledge, it can be more efficient than the alternative approach that combines encoders with a gyroscope. In order to develop an efficient maze-solving system, control theory techniques were integrated with image processing and neural networks in order to analyze images in which various obstacles were transformed into maze walls. This approach led to the training of a neural network designed to detect key points within the maze.