Purpose

The purpose of this paper is to propose a new video prediction-based methodology to solve the manufactural occlusion problem, which causes the loss of input images and uncertain controller parameters for the robot visual servo control.

Design/methodology/approach

This paper has put forward a method that can simultaneously generate images and controller parameter increments. Then, this paper also introduced target segmentation and designed a new comprehensive loss. Finally, this paper combines offline training to generate images and online training to generate controller parameter increments.

Findings

The data set experiments to prove that this method is better than the other four methods, and it can better restore the occluded situation of the human body in six manufactural scenarios. The simulation experiment proves that it can simultaneously generate image and controller parameter variations to improve the position accuracy of tracking under occlusions in manufacture.

Originality/value

The proposed method can effectively solve the occlusion problem in visual servo control.

中文翻译：

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人跟随机器人遮挡解决的预测视觉控制网络

目的

本文的目的是提出一种新的基于视频预测的方法来解决制造遮挡问题，该问题会导致机器人视觉伺服控制的输入图像丢失和控制器参数不确定。

设计/方法/方法

本文提出了一种可以同时生成图像和控制器参数增量的方法。然后，本文还介绍了目标分割并设计了一种新的综合损失。最后，本文结合离线训练生成图像和在线训练生成控制器参数增量。

发现

数据集实验证明，该方法优于其他四种方法，在六种制造场景下能够更好地还原人体被遮挡的情况。仿真实验证明，它可以同时生成图像和控制器参数变化，以提高制造过程中遮挡情况下跟踪的位置精度。

原创性/价值

该方法能有效解决视觉伺服控制中的遮挡问题。