A comparative analysis of the Probabilistic Roadmap Method and the A* algorithm in autonomous mobile robot navigation

Abstract

Path planning is fundamental in autonomous navigation, especially in industrial environments with priorities for time efficiency and optimal resource management. This work examines the Probabilistic Roadmap Method (PRM) and the A* algorithm, comparing their path generation time, trajectory distance, and tracking time to determine which performs better under these metrics. For the evaluation, both algorithms were implemented in MATLAB, and the CoppeliaSim simulator was used to model the movement of a differential robot in different environments inspired by industrial work cells. Both algorithms stand out for their versatility, as path generation is independent of the type of robot, allowing their application in differential, Ackermann, and omnidirectional robots with minimal adjustments to navigation. Choosing the right path-planning algorithm can significantly enhance the performance of robotic systems by reducing operational delays and optimizing efficiency in industrial environments. Understanding the differences between these algorithms is crucial for improving navigation in structured spaces, such as industrial work cells, where safety and efficiency are critical factors. By providing a detailed analysis of their performance, this research contributes to developing more efficient robotic systems, particularly in scenarios where precise and reliable navigation is essential.