An enhanced motion planning method for industrial robots based on the digital twin concept - Rui, Fan (2025)
With the rapid advancement of global digital transformation and smart manufacturing strategies, industrial robots and Digital Twin(DT) technologies have emerged as core drivers in promoting intelligent manufacturing upgrades, enhancing production efficiency, and optimizing workforce allocation. Motion planning for industrial robots based on DT technology helps improve trajectory accuracy, optimize operational efficiency, and reduce physical debugging risks. This study proposes a novel motion planning method for six-degree-of-freedom industrial robots based on DT technology. By integrating an improved Artificial potential field method, A* algorithm, and a synergistic approach combining 3-5-3 polynomial interpolation with particle swarm optimization, we effectively address the challenges of dynamic obstacle avoidance and trajectory optimization. The research establishes an accurate kinematic model using Denavit-Hartenberg parameters and develops an efficient simplified envelope collisiondetection method. Experimental validation was conducted on a DT verification system comprising MATLAB, PQFactory (a Chinese virtual debugging software), and a physical robot workstation. The experimental results demonstrate that the proposed method achieves an 85% success rate in obstacle avoidance while significantly reducing trajectory execution time by 44.52%. The system maintains high-precisionsynchronization between virtual and physical counterparts, with positioning errorsconstrained within 0.224% for the end-effector and 0.300% for joint angles. Compared to traditional virtual simulation techniques, the DT approach employed in this study, featuring real-time data interaction, multi-physics accurate modeling, and closed-loop feedback mechanisms, not only substantially improves motion planning accuracy and execution efficiency but also provides an innovative solution for developing highly adaptable, efficient, and reliable intelligent manufacturing robotic systems. These findings establish a solid technical foundation for industrial robot applications in complex environments.