TY  - EJOU
AU  - Wang, Hongxiao 
AU  - Liu, Hongshen 
AU  - Zhang, Dingsen 
AU  - Zhang, Ziye 
AU  - Yue, Yonghui 
AU  - Chen, Jie 

TI  - Obstacle Avoidance Path Planning for Delta Robots Based on Digital Twin and Deep Reinforcement Learning
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 83
IS  - 2
SN  - 1546-2226

AB  - Despite its immense potential, the application of digital twin technology in real industrial scenarios still faces numerous challenges. This study focuses on industrial assembly lines in sectors such as microelectronics, pharmaceuticals, and food packaging, where precision and speed are paramount, applying digital twin technology to the robotic assembly process. The innovation of this research lies in the development of a digital twin architecture and system for Delta robots that is suitable for real industrial environments. Based on this system, a deep reinforcement learning algorithm for obstacle avoidance path planning in Delta robots has been developed, significantly enhancing learning efficiency through an improved intermediate reward mechanism. Experiments on communication and interaction between the digital twin system and the physical robot validate the effectiveness of this method. The system not only enhances the integration of digital twin technology, deep reinforcement learning and robotics, offering an efficient solution for path planning and target grasping in Delta robots, but also underscores the transformative potential of digital twin technology in intelligent manufacturing, with extensive applicability across diverse industrial domains.
KW  - Digital twin; deep reinforcement learning; delta robot; obstacle path planning

DO  - 10.32604/cmc.2025.060384