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Research on workpiece location algorithm based on improved SSD
Industrial Robot ( IF 1.8 ) Pub Date : 2021-08-09 , DOI: 10.1108/ir-01-2021-0005
Lin Li 1 , Mingheng Fu 1 , Tie Zhang 1 , He Ying Wu 2
Affiliation  

Purpose

To improve production efficiency, industrial robots are expected to replace humans to complete the traditional manual operation on grasping, sorting and assembling workpieces. These implementations are closely related to the accuracy of workpiece location. However, workpiece location methods based on conventional machine vision are sensitive to the factors such as light intensity and surface roughness. To enhance the robustness of the workpiece location method and improve the location accuracy, a workpiece location algorithm based on improved Single Shot MultiBox Detector (SSD) is proposed.

Design/methodology/approach

The proposed algorithm integrates a weighted bi-directional feature pyramid network into SSD. A feature fusion architecture is structured by the combination of low-resolution, strong semantic features and high-resolution, weak semantic features. The architecture is built through a top-down pathway, bottom-up pathway, lateral connections and skip connections. To avoid treating all features equally, learnable weights are introduced into each feature layer to characterize its importance. More detailed information from the low-level layers is injected into the high-level layers, which could improve the accuracy of workpiece location.

Findings

It is found that the maximum location error at the center point calculated from the proposed algorithm is decreased by more than 22% compared with that of the SSD algorithm. Besides, the average location error evolves a decrease by at least 5%. In the trajectory prediction experiment of the workpiece center point, the results of the proposed algorithm demonstrate that the average location error is below 0.13 mm and the maximum error is no more than 0.23 mm.

Originality/value

In this work, a workpiece location algorithm based on improved SSD is developed to extract the center point of the workpiece. The results demonstrate that the proposed algorithm is beneficial for workpiece location. The proposed algorithm can be readily used in a variety of workpieces or adapted to other similar tasks.



中文翻译:

基于改进SSD的工件定位算法研究

目的

为提高生产效率,工业机器人有望取代人类完成传统的人工抓取、分拣和组装工件的操作。这些实现与工件定位的精度密切相关。然而,基于传统机器视觉的工件定位方法对光强、表面粗糙度等因素较为敏感。为增强工件定位方法的鲁棒性,提高定位精度,提出了一种基于改进型Single Shot MultiBox Detector(SSD)的工件定位算法。

设计/方法/方法

所提出的算法将加权双向特征金字塔网络集成到SSD中。特征融合架构由低分辨率强语义特征和高分辨率弱语义特征组合构成。该架构是通过自上而下的路径、自下而上的路径、横向连接和跳过连接构建的。为了避免平等对待所有特征,在每个特征层中引入了可学习的权重来表征其重要性。来自低层的更详细的信息被注入到高层,这可以提高工件定位的准确性。

发现

结果表明,与SSD算法相比,所提算法计算出的中心点最大定位误差降低了22%以上。此外,平均位置误差演化至少减少 5%。在工件中心点轨迹预测实验中,所提算法的结果表明,平均定位误差在0.13 mm以下,最大误差不超过0.23 mm。

原创性/价值

在这项工作中,开发了一种基于改进SSD的工件定位算法来提取工件的中心点。结果表明,所提出的算法有利于工件定位。所提出的算法可以很容易地用于各种工件或适用于其他类似的任务。

更新日期:2021-08-09
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