Visual edge feature detection and guidance under 3D interference: A case study on deep groove edge features for manufacturing robots with 3D vision sensors

IF 4.1 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Sensors and Actuators A-physical Pub Date : 2024-11-22 DOI:10.1016/j.sna.2024.116082

Zidong Wu , Hong Lu , Yongquan Zhang , He Huang , Zhi Liu , Jun Zhang , Xu Feng , Yongjie He , Yongjing Wang

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引用次数: 0

Abstract

For manufacturing robots equipped with 3D vision sensors, the presence of environmental interference significantly impedes the precision of edge extraction. Existing edge feature extraction methods often enhance adaptability to interference at the expense of final extraction precision. This paper introduces a novel 3D visual edge detection method that ensures greater precision while maintaining adaptability, capable of addressing various forms of interference in real manufacturing scenarios. To address the challenge, data-driven and traditional visual approaches are integrated. Deep groove edge feature extraction and guidance tasks are used as a case study. R-CNN and improved OTSU algorithm with adaptive threshold are combined to identify groove features. Subsequently, a scale adaptive average slope sliding window algorithm is devised to extract groove edge points, along with a corresponding continuity evaluation algorithm. Real data is used to validate the performance of the proposed method. The experiment results show that the average error in processing interfered data is 0.29 mm, with an average maximum error of 0.54 mm, exhibiting superior overall performance and precision compared to traditional and data-driven methods.

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三维干扰下的视觉边缘特征检测和引导：使用三维视觉传感器的制造机器人深槽边缘特征案例研究

对于配备三维视觉传感器的制造机器人来说，环境干扰的存在严重影响了边缘提取的精度。现有的边缘特征提取方法通常以牺牲最终提取精度为代价来提高对干扰的适应性。本文介绍了一种新颖的三维视觉边缘检测方法，该方法既能确保更高的精度，又能保持适应性，能够应对实际制造场景中各种形式的干扰。为了应对这一挑战，本文整合了数据驱动和传统视觉方法。以深槽边缘特征提取和引导任务为例进行研究。结合 R-CNN 和改进的 OTSU 算法（带自适应阈值）来识别沟槽特征。随后，设计了一种规模自适应平均斜率滑动窗口算法来提取凹槽边缘点，以及相应的连续性评估算法。使用真实数据验证了所提方法的性能。实验结果表明，处理干扰数据的平均误差为 0.29 毫米，平均最大误差为 0.54 毫米，与传统方法和数据驱动方法相比，表现出更优越的整体性能和精度。

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来源期刊

Sensors and Actuators A-physical 工程技术-工程：电子与电气

CiteScore

8.10

自引率

6.50%

发文量

630

审稿时长

49 days

期刊介绍： Sensors and Actuators A: Physical brings together multidisciplinary interests in one journal entirely devoted to disseminating information on all aspects of research and development of solid-state devices for transducing physical signals. Sensors and Actuators A: Physical regularly publishes original papers, letters to the Editors and from time to time invited review articles within the following device areas: • Fundamentals and Physics, such as: classification of effects, physical effects, measurement theory, modelling of sensors, measurement standards, measurement errors, units and constants, time and frequency measurement. Modeling papers should bring new modeling techniques to the field and be supported by experimental results. • Materials and their Processing, such as: piezoelectric materials, polymers, metal oxides, III-V and II-VI semiconductors, thick and thin films, optical glass fibres, amorphous, polycrystalline and monocrystalline silicon. • Optoelectronic sensors, such as: photovoltaic diodes, photoconductors, photodiodes, phototransistors, positron-sensitive photodetectors, optoisolators, photodiode arrays, charge-coupled devices, light-emitting diodes, injection lasers and liquid-crystal displays. • Mechanical sensors, such as: metallic, thin-film and semiconductor strain gauges, diffused silicon pressure sensors, silicon accelerometers, solid-state displacement transducers, piezo junction devices, piezoelectric field-effect transducers (PiFETs), tunnel-diode strain sensors, surface acoustic wave devices, silicon micromechanical switches, solid-state flow meters and electronic flow controllers. Etc...