This invention discloses a collaborative control method and
system for human-
robot collaborative
assembly robots based on digital twins. The method first constructs a digital twin
simulation environment in the Unity engine to map the physical collaborative
assembly scene to the virtual scene. Multimodal sensing technology is used to collect real-
time data on human actions and the state of the assembled workpiece, introducing virtual-real mapping to achieve
intent recognition. Then, based on
reinforcement learning algorithms, path planning is performed on the collaborative
robot in the digital twin environment to generate an optimal collaborative path sequence. Finally, the planned path is mapped to the real
robot control commands through coordinate transformation and
inverse kinematics algorithms, and an online adaptive strategy is used to dynamically optimize the motion trajectory. By combining digital twins and
reinforcement learning, this invention improves the intelligence and
flexible scheduling capabilities of the human-robot collaborative
assembly system, enhances the
system's rapid adaptability to unstructured changes and assembly accuracy, and overcomes the problems of cumbersome task
programming and poor adaptability in traditional methods.