Visual Inspection and Error Detection in a Reconﬁgurable Robot

Workcell: An Automotive Light Assembly Example

Tatyana Ivanovska

, Simon Reich

, Robert Bevec

, Ziga Gosar

3,4

, Minija Tamousinaite

, Ales Ude

and Florentin W

org

otter

Computational Neuroscience Department, University of G

ottingen, G

ottingen, Germany

Department of Automatics, Biocybernetics and Robotics, Joef Stefan Institute, Ljubljana, Slovenia

ELVEZ d.o.o., Ljubljana, Slovenia

Jozef Stefan International Postgraduate School, Ljubljana, Slovenia

Keywords:

Robotic Cell, Monitoring, Error Detection, Computer Vision.

Abstract:

Small and medium size enterprises (SMEs) often have small batch production. It leads to decreasing product

lifetimes and also to more frequent product launches. In order to assist such production, a highly reconﬁgurable

robot workcell is being developed. In this work, a visual inspection system designed for the robot workcell is

presented and discussed in the context of the automotive light assembly example. The proposed framework

is implemented using ROS and OpenCV libraries. We describe the hardware and software components of the

framework and explain the system’s beneﬁts when compared to other commercial packages.

1 INTRODUCTION

Nowadays robots have become essential in multiple

industrial tasks. The robotic solutions are usually

applied for complex repetitive tasks and high unit

volume (Gaspar et al., 2017). However, in small

or medium-sized enterprises (SMEs) a few-of-a-kind

production scenarios (Kr

uger et al., 2014) are more

typical. Since SMEs provide more than 40% of the

value added by the manufacturing industry in the Eu-

ropean Union (European commission, 2013), it is vi-

tal to allow them introduce efﬁcient and easily recon-

ﬁgurable robotic solutions to ease and speed up the

product manufacturing.

To facilitate the production in SMEs, a recon-

ﬁgurable workcell is being developed and introdu-

ced (Gaspar et al., 2017) as a modular system, where

hardware and software components allow for fast and

easy reconﬁguration.

In this paper, we present a visual monitoring sy-

stem, which is designed to detect errors during the

assembly process within the reconﬁgurable work cell.

We describe the hardware and software components

of the system and evaluate its applicability on the ex-

ample use case, namely, to the automotive light as-

sembly.

The paper is organized as follows. In Section 2,

the relevant information about the work cell develop-

ment as well as the available monitoring systems is

given. Section 3 presents the motivation for our de-

velopments. In Section 4, the use case details are

presented. In Section 5, the monitoring problem is

described and the measurement points are identiﬁed.

In Section 6, the proposed hardware and software so-

lutions are presented. The results are described and

analysed in Section 7. Section 8 concludes the paper.

2 RELATED WORK

Fulea et al. (Fulea et al., 2015) surveyed the litera-

ture on the topic of reconﬁgurability, focusing on in-

dustrial robotic workcells (as parts of reconﬁgurable

manufacturing systems), aiming to identify the main

approaches on reconﬁgurability and its relevant im-

plementations in the robotics (and related ﬁelds). Se-

tchi and Lagos (Setchi and Lagos, 2004) provided a

review of reconﬁgurability and reconﬁgurable manu-

facturing systems. Bi et al. (Bi et al., 2008) presen-

ted a survey of Reconﬁgurable Manufacturing Sys-

tems (RMS). It included general requirements of next

generation manufacturing systems and discussed the

strategies to meet these requirements.

Duro et al. focused on the applicability of the

Ivanovska T., Reich S., Bevec R., Gosar Z., Tamousinaite M., Ude A. and WÃ˝urgÃ˝utter F.

Visual Inspection and Error Detection in a Reconﬁgurable Robot Workcell: An Automotive Light Assembly Example.

DOI: 10.5220/0006666506070615

In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP 2018), pages 607-615

ISBN: 978-989-758-290-5

Multicomponent Robotic Systems and Hybrid Intel-

ligent Systems for industrial tasks (Duro et al., 2010).

Gaspar et al. (Gaspar et al., 2017) presented a no-

vel automatically reconﬁgurable robot workcell that

addressed the issues of ﬂexible manufacturing. The

proposed workcell is reconﬁgurable in terms of har-

dware and software. The hardware elements of the

workcell, both those selected off-the-shelf and those

developed speciﬁcally for the system, allow for fast

cell setup and reconﬁguration, while the software

aims to provide a modular, robot-independent, Ro-

bot Operating System (ROS) based programming en-

vironment (Quigley et al., 2009). One of the innova-

tive hardware elements for this cell type is a ﬂexible

ﬁxture based on a Gough-Stewart platform called the

hexapod presented by Bem et al. (Bem et al., 2017).

An industrial monitoring problem is highly rele-

vant and has been studied in several research pro-

jects. For instance, the SCOVIS project investigates

the automatic work ﬂow monitoring in a car assembly

environment in order to improve safety and process

quality (Kosmopoulos et al., 2012; M

orzinger et al.,

2010; Voulodimos et al., 2011).

The monitoring of assembly processes is usu-

ally addressed by building customized solutions using

commercial framework packages as Halcon (Eckstein

and Steger, 1999) or Cognex (Scola et al., 2001) fra-

meworks. These frameworks are powerful and con-

tain multiple efﬁcient algorithms for pattern recogni-

tion and image analysis. Another possible option is

to use other commercial frameworks (Matlab (Han-

selman and Littleﬁeld, 2005)) or free libraries such as

OpenCV (Bradski, 2000).

3 MOTIVATION

Since the general project architecture suggests to use

the ROS framework (Gaspar et al., 2017), we look for

a software package or library that can be integrated

with ROS interface in a straightforward way.

To our knowledge, there is no straightforward way

to connect the ROS (Quigley et al., 2009) interface

with such frameworks as Halcon or Cognex software.

Moreover, the use of a commercial software would

increase the overall cost of the robot work cell. This

leverages the use of the free and open source systems

as ROS (Quigley et al., 2009) in combination with the

OpenCV library (Bradski, 2000).

The processing using the well known software

as Halcon or Cognex can only be organized using

the procedure schematically shown in Figure 1. The

image must be obtained from the camera using the

ROS-based framework and saved on a hard drive.

Thereafter, the image can be read and processed by

a software package. The ROS node keeps listening

to the speciﬁed folder for the results. As soon as the

image processing is ﬁnished and saved, the ROS node

reads the results and sends a notiﬁcation to the robot.

This solution is rather time-consuming and inef-

ﬁcient. Hence, we have developed our own system,

which is completely integrated into the ROS frame-

work. Our system is based on OpenCV library, i. e.,

the main algorithm implementations are tested and

optimized. Several algorithms are combined into pro-

cessing pipelines, suitable for the example use case.

The main idea of our software package is to use fast

and efﬁcient algorithms that do not require any prior

training due to the fact that for each task in each use

case the collection of a big amount of data seems in-

feasible.

Therefore, we propose a novel system, based on

OpenCV and ROS libraries, which is designed for

monitoring tasks in a robot workcell.

4 AN EXAMPLE USE CASE:

AUTOMOTIVE LIGHT

ASSEMBLY

Automotive lights (headlamps) are made up of typi-

cal rigid structural elements such as housing, actua-

tors, bulb holders, adjustable screws, heat shields, wi-

res and other components (Gaspar et al., 2017).

In Figure 2, two housings of car lights assembled

by ELVEZ company are shown.

There are several details that have been used for

robotic assembly. The components are listed below

and shown in Figures 2 and 3:

• Housing X07 or X82;

• LWR drive;

• Heat shields (left and right);

• Buld holder;

• Adjusting screw.

Such components as harness connection (cf. Fi-

gure 4) have been excluded as not suitable for the ro-

botic assembly.

5 QUALITY CONTROL TASKS

In general, visual inspection is to identify whether a

particular attribute is present, properly located on a

predetermined area, and not deviates from a given set

of speciﬁcations (Liu et al., 2015).

ROS System

Camera Harddrive

Software Packages

Figure 1: The image processing pipeline, when a software package is not straightforwardly combined with a ROS-based

system.

Figure 2: Two models of left and right automotive light housings produced by Elvez. Left: Housing X07; Right: Housing

X82.

Figure 3: Components for the assembly process. 1 and 2 are

the heat shields. 3 is the LWR Drive. 4 is the adjustment

screw. 5 is the light bulb holder.

There are two types of quality control tasks: a bi-

nary decision and a measurement. In the binary task,

Figure 4: An example of a harness connection. It was ex-

cluded from the automated assembly process.

the algorithm needs to make a decision, for instance,

if a detail is damaged or incorrectly placed. The mea-

surement task requires to measure a distance between

two details, a height of a screw, an angle of rotation

of the detail.

Whereas the ﬁrst task can be implemented using

a camera, which has been only intrinsically calibra-

ted, the second task requires the extrinsic camera ca-

libration (Hartley and Zisserman, 2003) to obtain the

measurement results in physical units, for instance, in

mm. In this work, we describe and demonstrate the

solutions of the ﬁrst type.

Here, we consider the following quality control

tasks:

1. Is a certain part of the housing damaged?

2. Is the screw properly assembled and no damages

occurred?

3. Is the LWR drive properly assembled and no da-

mages occurred?

4. Is the light bulb holder properly assembled and no

damages occurred?

6 METHODS

For our framework, we have chosen hardware and

software components that are described below.

6.1 Hardware and User Pre-Setup

The monitoring of the execution and detection of er-

rors is performed in the workcell using a 2D color

camera (Basler acA4600-7gc), which is mounted to-

gether with a light ring to the robot using a pneumatic

tool changer system as seen in Figure 5. This system

allows the robot to release a gripper and pick up the

camera. This high resolution industrial camera pro-

duces 14MP images and has been selected to be able

to catch even minor detail damages and assembly in-

accuracies. The camera has a rolling shutter and a re-

latively low frame rate of 7 frames per second, which

makes the camera suitable only for situations where

the observed object is static relative to the camera.

As part of the reconﬁgurable workcell that our

proposed system is part of, the user has several ways

of planning the assembly sequence. It is possible to

use kinesthetic guidance in order to teach robotic se-

quences or use a simulation environment to design a

sequence. In both cases the user must deﬁne when

and where the visual inspections will be performed.

In this paper, we are proposing a solution for bi-

nary decisions, in which case camera plane alignment

is not so critical as in measurement inspections. That

means a user can use kinesthetic guidance or simu-

lation in order to select the camera pose for an ar-

bitrary inspection. There are several constraints that

must be obliged in order to come to a viable assem-

bly and inspection solution, e. g., the camera ﬁeld of

view (lens), the robot conﬁguration to reach the requi-

red camera pose (collision, workspace), and the cycle

time.

6.2 Software Solution

We have developed a framework, based on

OpenCV (Bradski, 2000) using ROS interface (Quig-

Figure 5: The industrial camera with a light ring mounted

on the UR-10 robot with the pneumatic tool changer system.

Left: a general view. Right: a close-up view.

ley et al., 2009), C++, and Qt (Summerﬁeld, 2010).

The framework consists of several ROS modules,

where the central is the Monitoring one.

The image data stream is published through the

ROS module for the Basler camera. As soon as the

request for inspection arrives to the Monitoring ROS

module, it acquires the data from the camera, executes

the corresponding processing, and returns the status

of the operation (a boolean value, indicating, if the

detail is damaged or not). Additionally, a graphical

user interface is developed to demonstrate the moni-

toring routine.

6.2.1 The Processing Pipeline

First, ideal or template images are acquired (cf. Fi-

gure 6) and the corresponding regions of interest

(ROIs) are predeﬁned by the user. After the templates

are deﬁned, the actual processing of new image data

can be executed.

Second, the template ROI is detected in the ne-

wly acquired image. The search of the template starts

within a neighborhood of the ROI location in the ideal

image. The search is based on the assumption that the

ideal template and the actual image data are always

acquired under very similar conditions, namely, the

same controlled lighting and the pose. The template

image is overlaid with the corresponding patches of

the original image and in each point in a certain neig-

hborhood of the template location a normalized cor-

relation coefﬁcient is computed. The location of the

global maximum is considered as the template loca-

tion (cf. Figure 7). Finally, the detected ROI of the

same size as the template image is extracted from the

acquired image and passed for further processing.

Third, the subimage preprocessing is executed.

Under preprocessing we understand illumination cor-

rection and denoising (Masters et al., 2009). It is

crucial to apply these techniques not on a full acqui-

red image, but on the extracted subimage to re-

duce the computational cost and the overall execution

Figure 6: The process of template selection.

Figure 7: The process of template detection is schematically

shown. The template image (left) is sought in the acquired

image (right), and the region that is the most similar to the

template image is marked red.

time. The subimage is converted to the L*a*b co-

lor space (Reinhard et al., 2001), which is designed

to approximate human vision. The luminance chan-

nel (L) is extracted and the adaptive histogram equa-

lization (Reza, 2004) is applied to it. Thereafter, the

image is smoothed with median ﬁlter to preserve the

edges.

Fourth, the use case-speciﬁc checks are executed.

Usually, they include a segmentation step to extract

the contours of the detail of interest, for example,

using color global or local, automatic or manual thres-

holding. For the automatic thresholding, the Otsu

thresholding (Zhang and Hu, 2008) is applied. The-

reafter, connected components (Masters et al., 2009)

are checked, and the component of interest is found

and analyzed.

6.2.2 Framework Modes

The framework has three following modes:

• Template selection (online or ofﬂine);

• Ofﬂine processing pipeline testing;

• Online monitoring.

After the template selection, the user can test the

developed detection pipelines and check different pa-

rameter settings on an image, acquired from the ca-

mera directly or saved on a hard drive.

In the current version of our framework, the pro-

cessing pipelines are pre-programmed. The next ver-

sion the user will be able to generate and save the pro-

cessing pipelines.

6.2.3 Use Case-speciﬁc Algorithms

Here, we describe the algorithms that have been de-

veloped for the automotive light assembly use-case.

Damage on the Housing Part. An example is

shown in Figure 7. In this task, the shape of the detail

as well as its intensity are analysed.

The shape of the detail top is assessed using the

Hough transform algorithm for circle detection (Mas-

ters et al., 2009).

The top circles are detected both in the template

and the ROI images. If the distance d between the

circle centers c

and c

and differences between the

radii are greater than a user deﬁned threshold T , the

detail is considered to be damaged:

(

d(c

, c

) >= T Damaged

d(c

, c

) < T OK

(1)

For the intensity analysis, the template and the ROI

images (I

and I

ROI

, respectively) are converted to the

HSV color space (Masters et al., 2009), and the abso-

lute differences between the intensity values are com-

puted: |HSV (I

) − HSV (I

ROI

)|. The total number of

pixels, where the difference value is greater than a

user deﬁned threshold, is evaluated.

In Figure 8, an example of the ROI extracted in

Figure 7 is shown. Since the detail is damaged, the

Hough transform fails to detect the circle in the ROI,

and the expected circle location is marked red. The

parts, where the differences with the template image

are signiﬁcant are marked white. As it can be obser-

ved, most of the differences are located in the dama-

ged region.

Detection of the Screw Height. Basically, it is re-

quired here to detect if the screw height lies in a user-

deﬁned range. In this case, a screw base is selected as

a suitable template (cf. Figure 4).

When the template is detected, the coordinates of

its top left corner [x

, y

] and the bottom right corner

+ w, y

+ h] are given, where w, h denote the width

and height, respectively. To extract a region, which

contains the screw, the following coordinates are ta-

ken: [x

, 0], [x

+ w, y

]. Thereafter, the preprocessing

Figure 8: Left: the acquired image of the damaged detail.

Right: The processing results are overlaid with the acquired

image.

Figure 9: The base of the screw is a template, since this part

does not change from image to image.

procedure described above is utilized. Finally, Otsu

thresholding (Zhang and Hu, 2008) is applied to the

image.

Figure 10: The screw is detected using connected compo-

nent analysis.

From the thresholding result the two biggest con-

nected components are extracted and their total height

is measured (cf. Figure 10). If the height H of the

screw does not lie in a predeﬁned range, the case is

rejected:

(

hmin

≤ H ≤ T

hmax

Otherwise Reject

(2)

Detection of the Detail Position. Here, both pro-

blems of the LWR drive and the bulb holder positi-

oning are solved with a similar processing pipeline.

Basically, the template is detected and then the ex-

tracted region of interest (ROI) is compared to the

template image. If the differences between the ima-

ges are higher than a certain threshold the case is re-

jected. In Figures 11 and 12, screenshots from our

framework, acquired in the Testing mode are shown.

The LWR drive is properly placed, whereas the image

with the bulb holder is rejected.

Figure 11: The detection result is positive.

7 EVALUATION AND

DISCUSSION

We analyze the presented hardware and software

components as well as discuss the advantages and li-

mitations of the system.

Figure 12: The assembly was not successful.

7.1 Hardware Components

Today the market of industrial imaging offers multi-

ple 2D and 3D cameras. For instance, such companies

as ”The Imaging Source“

or Basler

present indus-

trial cameras, which can be successfully applied for

the monitoring task. Apart from that, these manufac-

turers support a multi-platform application program-

ming interface (API).

Moreover, there is a specialized ROS module for

Basler cameras, which is naturally built in the gene-

ral software architecture of the reconﬁgurable work

cell (Gaspar et al., 2017).

We have selected a color high resolution (14 MP)

Basler camera, which allows for up to 7 frames per

second. The resolution is 4608px × 3288px. This ca-

mera can only be used for acquisition of still images,

i. e. the robot moves to a certain position, stops, and

the image is taken. For such tasks as video tracking,

this camera is not suitable.

For the selection of the appropriate lens, several

parameters have to be taken into account. The main

ones are the object size and the working distance. The

sensor size and the lens mount are deﬁned by the ca-

mera. Since the size of the imaged objects can be

rather small, e. g. 10 × 20 mm

, and considering the

sensor size, we have selected a f 25 mm lens with a

working distance 200 mm.

https://www.theimagingsource.de

https://www.baslerweb.com

7.2 Software Components and

Algorithms

We observed that measurement problems are very dif-

ferent even for the same use case. Hence, only limited

generalization with respect to processing algorithms

is possible. Namely, one is not able with the same al-

gorithm to measure the height of a metal screw and

detect, if some other plastic detail is intact. The gene-

ral evaluation pipeline consists of the following steps:

1. Template detection

2. ROI selection

3. ROI pre-processing (denoising and illumination

correction)

4. Segmentation and object extraction

5. Object evaluation (distance or angle measure-

ment, binary decision)

The ﬁrst three steps are rather general, and we use

them for all measurement problems. Of course, prior

to measurements, the user has to set up the templates

for each problem.

For the ﬁrst step, the template matching al-

gorithm provided by OpenCV is applied. We

observed that only normed metrics (for instance,

CV_TM_CCORR_NORMED) work reliably for our pro-

blems. This straightword template matching is ap-

plicable, when the assumption about the controlled

lighting conditions holds. In a more general set up,

this algorithm can be replaced with a more sophisti-

cate object detection using descriptors, for example,

SURF (Zhang and Hu, 2008).

The latter two steps are task-dependent. For

segmentation and object extraction we usually ap-

ply some prior knowledge about the object appea-

rance, e. g., color, shape, and location on the image.

Here, such algorithms as color clustering or threshol-

ding, and shape analysis (Hough transformation, Har-

ris corner detection, Connected component analysis)

are applicable.

For each measurement problem, we collected test

images, acquired in a test environment as well as the

real images, obtained during the assembly procedure.

The parameters were pre-selected and optimized for

the implemented use case.

A new use case, i. e. with new measurement tasks,

the algorithmic pipelines and their correspondent pa-

rameters would have to be again selected and optimi-

zed. However, we assume that a set of image pro-

cessing tools provided by the OpenCV library, such

as pattern matching, denoising, segmentation, and fe-

ature extraction algorithms would be fully sufﬁcient

for a monitoring task.

Although the presented framework for visual in-

spection is efﬁcient and completely integrated with

the whole robotic work cell, it has certain limitations.

First, the hardware choice is suitable for inspection of

static images, but it is not suitable for video tracking.

Second, the whole software system is tied to a speci-

ﬁc ROS version, which is not downwards compatible.

Third, the generation new processing pipelines would

require from the user some knowledge on image ana-

lysis and robotics. Fourth, new templates will have

to be generated and the parameters will have to be

re-optimized, if the robot poses or lighting conditions

change, which can be a rather time-consuming task.

8 CONCLUSIONS AND FUTURE

WORK

The computer vision framework, which is used as a

monitoring module in a highly reconﬁgurable robot

workcell has been presented here. The hardware as

well as software components were described and dis-

cussed. The automotive assembly use case example

was used as an application example.

As future work, we will extend the software com-

ponents to allow the user to generate processing pi-

pelines as well as test the framework on further use

cases.

ACKNOWLEDGEMENTS

The research leading to these results has received fun-

ding from the European Communitys Horizon 2020

Programme under grant agreement no. 680431, Re-

conCell(A Reconﬁgurable robot workCell for fast set-

up of automated assembly processes in SMEs).

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