Autofeeding System for Assembling the CBCs on Automobile Engine Based on 3-D Vision Guidance

Li, Jianqi; Du, Rui; Zhang, Jun; Zhu, Jiang; Xu, Haixia; Cai, Muyao

doi:10.1109/tim.2021.3096278

Cited by 4 publications

(1 citation statement)

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“…In comparison to manual sorting and feeding, automated systems can swiftly and accurately convey soles to the production line, thus shortening production cycles and boosting output. Additionally, automated systems require only a small number of workers for equipment monitoring and maintenance, effectively reducing labor costs [2,3]. Therefore, the introduction of automated equipment to realize intelligent factories holds significant importance for enhancing enterprise production efficiency [4].…”

Section: Introductionmentioning

confidence: 99%

Automatic loading method for robot guided by 3D vision for scattered and stacked shoe soles

Zhang,

Wei

2024

Eng. Res. Express

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This paper primarily introduces a method for the automated feeding of scattered stacked shoe soles using a 3D visual-guided robot. Initially, addressing issues like slow speed in pose estimation and poor robustness during the robot sorting and feeding process, we introduce an enhanced pose estimation algorithm. This algorithm combines the improved Super 4-Point Congruent Sets (Super 4PCS) with the Truncated Least Squares Semidefinite Relaxation (TEASER) algorithm, significantly boosting the speed and robustness of pose estimation during sole sorting, and achieving precise target pose estimation. Building upon this foundation, we present a sorting strategy for disordered stacked shoe soles. This strategy integrates spatial positional information of each sole, employing multi-objective decision-making and recognition algorithms to determine optimal grasping targets. Finally, an experimental platform for automated sole feeding is designed to validate the proposed method. The experimental results indicate that the pose estimation method proposed in this paper achieves an average distance error of 2.04mm and an average angular error of 2.72°, with the robot's average success rate in grasping reaching 97.08%. Moreover, the average processing time of the vision algorithm is only 1.34s, demonstrating good efficiency, precision, and robustness. This method effectively meets the automated feeding needs of scattered and stacked shoe soles in the actual production processes of shoemaking enterprises.

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Section: Introductionmentioning

confidence: 99%