High-Precision Monocular Vision Guided Robotic Assembly Based on Local Pose Invariance

Industrial robots provide a new production mode for automatic assembly. However, the motion accuracy of robots is hard to meet high-precision assembly requirements in many current scenarios. To address this issue, this article proposes a robotics assembly strategy based on local pose invariance, achieving a high-precision assembly guided by a monocular camera. This assembly strategy combines monocular measurements with local reference poses, providing a new assembly pose solution chain that effectively mitigates the accumulation of errors caused by the lengthy coordinate chain propagation. Next, a monocular visual measurement method based on 3-D–2-D matching is introduced, which improves the traditional matching model and enhances the efficiency and accuracy of the measurement process by directly mapping 2-D pixel coordinates to 3-D coordinates. This measurement method is universal and applicable to both calibration and actual assembly. Experimental results demonstrate that the proposed assembly method is suitable for both hole-to-hole and peg-in-hole tasks and can adapt to assembling different components, realizing a final error better than 0.26 mm within a range of approximately 4 m.