Abstract—This article addresses the development of an optimized algorithm for maze solving using image processing techniques and resistive grids. Since the diagonal square geometric cell is used as basic resistive cell, the proposed algorithm optimizes the process of finding the shortest obstacle-free route from initial node to the target node into an irregular maze plagued with irregular obstacles. Depending on the dimensions of the robot, a node matrix with the same size as the maze-image is generated and the distance between each node is equal to the largest dimension of the robot. The maze image can be scaled from 1 m × 1 m to 40 m × 40 m. The equation system is improved by building it from the collision-free node matrix instead the full resistive grid. Nodal analysis formulates the admittance matrix and Kirchhoff's current law then determines the shortest path within the reduced resistive network, after removing nodes and branches absorbed by irregular obstacles. An undirected graph decision algorithm avoids singular matrices during formulation. Thus, the omnidirectional robot follows the collision-free shortest path from start to end node in the main sub-network. As a result, travel time, distance traveled and battery consumption of an omnidirectional mobile robot used as test vehicle are minimized. The proposed algorithm is validated using three case studies cases. Compared to the results obtained by directly using the square resistive cell, the experimental results demonstrate that the optimized algorithm works better and more efficiently, reducing the following for the largest maze: CPU-time = 7.76 s, travel time = 146.26 s, dis-tance = 31.99 m and battery usage = 2.42%.

Keywords—circuit analysis, omnidirectional robot, resistive grid, maze-solving, microcontrollers

Cite: C. Sánchez-López, C. Muñiz-Montero, J. Arellano-Hernández, C. Hernández-Mejía, and D. Torres-Muñoz, "Optimized Maze-Solving Algorithm for Mobile Robots," International Journal of Mechanical Engineering and Robotics Research, Vol. 15, No. 4, pp. 379-390, 2026. doi: 10.18178/ijmerr.15.4.379-390

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).