Abstract
Aiming at the problem that the robot de-palletizing task is difficult to accomplish under unstable ambient light, a two-step method is proposed to realize the localization of workpieces, which in this work are woven bags. To begin with, Region Growing method is used to extract the whole target region in the original image, and the relationship model between image intensity and the optimal Region Growing threshold is established. Then, Progressive Probabilistic Hough Transform(PPHT) is used to locate each woven bag. To improve the system performance, the optimal parameters of the PPHT function in different illumination intervals are determined. Finally, experiments are conducted to verify the effectiveness of the proposed method. Experiment results demonstrate this method is robust and feasible.
Highlights
On automated production lines, palletizing and de-palletizing job is an important link connecting production and transportation [1]–[3]
We find many interference factors exist under practical complicated working environment, which complicates the identification and localization of single woven bag: 1) the ambient light varies with the weather, time, human-induced disturbance among others, which may cause image intensity varying intensely; 2) background interference, which includes but is not limited to shelves, conveyor belts and robot body, blends target with inseparable noises; 3) with diverse palletizing types and deformable objects, the flexible edges of adjacent woven bags overlap with each other
Among them, aiming at the problems of illumination instability, low image contrast and difficult segmentation in the actual de-palletizing job, an effective Region Growing image segmentation method based on the dynamic threshold is proposed
Summary
On automated production lines, palletizing and de-palletizing job is an important link connecting production and transportation [1]–[3]. In order to improve the production efficiency of this link, de-palletizing robots came into being, which can firmly grasp and deliver goods with a special customized multi-functional grasper. With the development of industrial robots and industrial control technology, de-palletizing robots are gaining prevalence in diverse industries [4]–[6]. Traditional industrial robots can hardly handle the grasping tasks at complex scenes when the objects are not fixed accurately, because offline programming or ‘‘teaching and playback mode’’ are heavily relied on [7]. To improve the generalization ability of industrial robots, aiding robots with machine vision is becoming increasingly prevalent [8]–[11]. Machine vision technology is widely used to detect the appearance and quality of products and packages [12]. Several studies on machine vision-based product positioning and packaging in robotic de-palletizing operations are reported. The system requires unique landmark features to identify the target object and estimate the pose of
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