Enhancing Industrial Automation: Robot Motion Optimization with RoboDK

Abstract

The growing adoption of Industry 4.0 has increased the demand for efficient robotic systems capable of optimizing production speed, precision, and operational flexibility in modern manufacturing. Simulation-driven offline programming has become a critical approach for evaluating robotic performance before physical deployment, minimizing operational risks and improving productivity. This study investigates the effectiveness of three trajectory planning techniques—Joint Interpolation (MoveJ), Linear Interpolation (MoveL), and Python-based custom trajectory scripting—using RoboDK simulation with the Dobot Magician robotic manipulator. Each method was analyzed by measuring cycle time performance during task execution to determine its suitability for industrial automation applications. Comparative results indicate that while MoveJ and MoveL provide reliable baseline motion strategies, the Python-scripted custom trajectory offers superior optimization by significantly reducing total cycle time. The reduction in execution time highlights the practical importance of customized path planning in improving manufacturing throughput, particularly in Industry 4.0 environments where time efficiency directly impacts productivity and cost-effectiveness. This research demonstrates that integrating RoboDK simulation with Python-based path optimization can enhance robotic operational efficiency and provides a practical framework for improving trajectory planning in low-cost industrial robotic platforms.