Evaluating Dijkstra and A* Pathfinding Algorithms for Mobile Robots in Warehouse Environments Using CoppeliaSim

Abstract

In modern warehouse automation, mobile robots are essential for enhancing operational efficiency by autonomously navigating to pick and transport items. Effective path planning is crucial for these robots to move through complex environments, avoid obstacles, and minimize travel time. This study evaluates two prominent path planning algorithms Dijkstra and A*—implemented on a mobile robot within a simulated 3D warehouse environment using CoppeliaSim. Three distinct rack locations were analyzed to assess the performance of both algorithms concerning path optimality, computational efficiency, and real-time applicability. Simulation results indicate that while both algorithms successfully generated safe and accurate paths, A* outperformed Dijkstra in terms of speed and path efficiency. A*'s heuristic-driven approach resulted in lower computational load and faster execution time, making it more suitable for real-time warehouse operations where responsiveness is critical. The insights gained provide valuable guidance for robotics engineers and developers in selecting appropriate path planning strategies for autonomous navigation in industrial settings.