Autonomous mobile robots can be found in a wide
field of applications. Their types range from household robots over
workshop robots to autonomous cars and many more. All of them
undergo a number of testing steps during development, production
and maintenance. This paper describes an approach to improve
testing of robot behavior. It was inspired by the RoboCup @work
competition that itself reflects a robotics benchmark for industrial
robotics. There, scaled down versions of mobile industrial robots
have to navigate through a workshop-like environment or operation
area and have to perform tasks of manipulating and transporting
work pieces. This paper will introduce an approach of automated
vision-based testing of the behavior of the so called youBot robot,
which is the most widely used robot platform in the RoboCup
@work competition. The proposed system allows automated testing
of multiple tries of the robot to perform a specific missions and
it allows for the flexibility of the robot, e.g. selecting different
paths between two tasks within a mission. The approach is based
on a multi-camera setup using, off the shelf cameras and optical
markers. It has been applied for test-driven development (TDD) and
maintenance-like verification of the robot behavior and performance.
[1] Saddek Bensalem, Laindra de Silva, Flix Ingrand, and Rongjie Yan.
A verifiable and correct-by-construction controller for robot functional
levels. Journal of Software Engineering for Robotics, 2(1):1-19,
September 2011.
[2] J.J. Aguilar, and F. Torres, M.A. Lope. Stereo vision for 3D measurement:
accuracy analysis, calibration and industrial applications. Measurement,
193200, Volume 18, Issue 4, August 1996.
[3] M. Fiala. ARTag Revision 1. A Fiducial Marker System Using Digital
Techniques. NRC 47419, pages 46, November 24, 2004.
[4] G. D. Hager. A modular system for robust positioning using feedback
from stereo vision. Robotics and Automation, IEEE Transactions,
582-595, Volume 13, Issue 4, August 1997.
[5] H. K.Nishihara. PRISM: A Practical Real-Time Imaging Stereo Matcher.
Optical Engineering, Volume 23, no. 5, 1984.
[6] M. Bertozzi, A. Broggi, A. Fascioli, and S. Nichele.Stereo Vision-based
Vehicle Detection. IEEE Intelligent Vehicles Symposium, 2000.
[7] Jung-Rye Son, Tae-Yong Kuc, Jong-Loo Park, and Hong-Seak Kim.
Simulation based functional and performance evaluation of robot
components and modules. Information Science and Applications (ICISA)
International Conference, 1-7, IEEE, April 2011.
[8] Enrique Medinaa, Eduardo Parrillaa, Alvaro Pagea, Jose Olasoa, Juan
Carlos Gonzleza, and Helios De Rosarioa. A new non-invasive and
low cost method for the characterisation of pronation patterns by using
AR-markers and functional classification. Footwear Science, Volume 5,
Supplement 1, 2013.
[9] Gontje C. Claasen, Philippe Martin, and Frederic Picard. High-Bandwidth
Low-Latency Tracking Using Optical and Inertial Sensors. 5th
International Conference on Automation, Robotics and Applications,
Wellington, New Zealand, 2011.
[10] Kuanhao Zheng, Dylan F. Glas, Takayuki Kanda, Hiroshi Ishiguro, and
Norihiro Hagita. Supervisory Control of Multiple Social Robots for
Navigation. International Conference on Human-Robot Interaction (HRI),
2013 8th ACM/IEEE, 17-24, 2013.
[11] Yun Koo Chung and Sun-Myung Hwang. Software testing for intelligent
robots. International Conference on Control, Automation and Systems
2007, 2344-2349, IEEE, 2007.
[12] Aitor Arrieta, Irune Agirre, and Ane Alberdi. Testing Architecture with
Variability Management in Embedded Distributed Systems. Actas de las
IV Jornadas de Computacin Empotrada (JCE), September 2013.
[13] G. Biggs .Applying regression testing to software for robot hardware
interaction. Robotics and Automation(ICRA) 2010, IEEE International
Conference, 4621-4626,2010.
[14] Jae-Hee Lim, Suk-Hoon Song, Jung-Rye Son, Tae-Yong Kuc,
Hong-Seong Park, and HongSeak Kim. An automated test method for
robot platform and its components. International Journal of Software
Engineering and its Applications, 4(3):9-18, July 2010.
[15] S. Peters, D. Thomas, M. Friedmann, and O. Von Stryk. Multilevel
testing of control software for teams of autonomous mobile robots.
Simulation, Modelling, and Programming for Autonomous Robots,
183-194, 2008.
[16] S. J. Ahn, and M. Schultes. A new circular coded target for the
automation of photogrammetric 3D-surface measurements. Optical 3-D
Measurement Techniques IV, 225-234, 1997.
Fig. 4 Software structure of the approach
components (see Fig. 4). The camera stream component
Fig. 5 Screenshot of tracking visualisation using rviz
[17] S. J. Ahn, Wolfgang Rauh, and Matthias Recknagel. Circular Coded
Landmark for Optical 3D-Measurement and Robot Vision. International
Conference on Intelligent Robots and Systems, 1128-1133, 1999.
[18] G. Kraetzschmar, W. Nowak, N. Hochgeschwender, R.
Bischoff, D. Kaczor, F. Hegger. RoboCup@Work Rulebook.
http://www.robocupatwork.org/download/rulebook-2013-06-08.pdf,
2013.
[19] Nguyen Hai, Ciocarlie Matei, Hsiao Kaijen, and Kemp Charles. ROS
Commander: Flexible Behavior Creation for Home Robots. ICRA ,
05/2013, 2013.
[20] Prof. Dr. Erwin Prassler. Cooperations of the Locomotec GmbH.
http://www.locomotec.com/en/cooperations, 11.03.2014.
[1] Saddek Bensalem, Laindra de Silva, Flix Ingrand, and Rongjie Yan.
A verifiable and correct-by-construction controller for robot functional
levels. Journal of Software Engineering for Robotics, 2(1):1-19,
September 2011.
[2] J.J. Aguilar, and F. Torres, M.A. Lope. Stereo vision for 3D measurement:
accuracy analysis, calibration and industrial applications. Measurement,
193200, Volume 18, Issue 4, August 1996.
[3] M. Fiala. ARTag Revision 1. A Fiducial Marker System Using Digital
Techniques. NRC 47419, pages 46, November 24, 2004.
[4] G. D. Hager. A modular system for robust positioning using feedback
from stereo vision. Robotics and Automation, IEEE Transactions,
582-595, Volume 13, Issue 4, August 1997.
[5] H. K.Nishihara. PRISM: A Practical Real-Time Imaging Stereo Matcher.
Optical Engineering, Volume 23, no. 5, 1984.
[6] M. Bertozzi, A. Broggi, A. Fascioli, and S. Nichele.Stereo Vision-based
Vehicle Detection. IEEE Intelligent Vehicles Symposium, 2000.
[7] Jung-Rye Son, Tae-Yong Kuc, Jong-Loo Park, and Hong-Seak Kim.
Simulation based functional and performance evaluation of robot
components and modules. Information Science and Applications (ICISA)
International Conference, 1-7, IEEE, April 2011.
[8] Enrique Medinaa, Eduardo Parrillaa, Alvaro Pagea, Jose Olasoa, Juan
Carlos Gonzleza, and Helios De Rosarioa. A new non-invasive and
low cost method for the characterisation of pronation patterns by using
AR-markers and functional classification. Footwear Science, Volume 5,
Supplement 1, 2013.
[9] Gontje C. Claasen, Philippe Martin, and Frederic Picard. High-Bandwidth
Low-Latency Tracking Using Optical and Inertial Sensors. 5th
International Conference on Automation, Robotics and Applications,
Wellington, New Zealand, 2011.
[10] Kuanhao Zheng, Dylan F. Glas, Takayuki Kanda, Hiroshi Ishiguro, and
Norihiro Hagita. Supervisory Control of Multiple Social Robots for
Navigation. International Conference on Human-Robot Interaction (HRI),
2013 8th ACM/IEEE, 17-24, 2013.
[11] Yun Koo Chung and Sun-Myung Hwang. Software testing for intelligent
robots. International Conference on Control, Automation and Systems
2007, 2344-2349, IEEE, 2007.
[12] Aitor Arrieta, Irune Agirre, and Ane Alberdi. Testing Architecture with
Variability Management in Embedded Distributed Systems. Actas de las
IV Jornadas de Computacin Empotrada (JCE), September 2013.
[13] G. Biggs .Applying regression testing to software for robot hardware
interaction. Robotics and Automation(ICRA) 2010, IEEE International
Conference, 4621-4626,2010.
[14] Jae-Hee Lim, Suk-Hoon Song, Jung-Rye Son, Tae-Yong Kuc,
Hong-Seong Park, and HongSeak Kim. An automated test method for
robot platform and its components. International Journal of Software
Engineering and its Applications, 4(3):9-18, July 2010.
[15] S. Peters, D. Thomas, M. Friedmann, and O. Von Stryk. Multilevel
testing of control software for teams of autonomous mobile robots.
Simulation, Modelling, and Programming for Autonomous Robots,
183-194, 2008.
[16] S. J. Ahn, and M. Schultes. A new circular coded target for the
automation of photogrammetric 3D-surface measurements. Optical 3-D
Measurement Techniques IV, 225-234, 1997.
Fig. 4 Software structure of the approach
components (see Fig. 4). The camera stream component
Fig. 5 Screenshot of tracking visualisation using rviz
[17] S. J. Ahn, Wolfgang Rauh, and Matthias Recknagel. Circular Coded
Landmark for Optical 3D-Measurement and Robot Vision. International
Conference on Intelligent Robots and Systems, 1128-1133, 1999.
[18] G. Kraetzschmar, W. Nowak, N. Hochgeschwender, R.
Bischoff, D. Kaczor, F. Hegger. RoboCup@Work Rulebook.
http://www.robocupatwork.org/download/rulebook-2013-06-08.pdf,
2013.
[19] Nguyen Hai, Ciocarlie Matei, Hsiao Kaijen, and Kemp Charles. ROS
Commander: Flexible Behavior Creation for Home Robots. ICRA ,
05/2013, 2013.
[20] Prof. Dr. Erwin Prassler. Cooperations of the Locomotec GmbH.
http://www.locomotec.com/en/cooperations, 11.03.2014.
@article{"International Journal of Information, Control and Computer Sciences:67086", author = "Arne Hitzmann and Philipp Wentscher and Alexander Gabel and Reinhard Gerndt", title = "Automated Testing of Workshop Robot Behavior", abstract = "Autonomous mobile robots can be found in a wide
field of applications. Their types range from household robots over
workshop robots to autonomous cars and many more. All of them
undergo a number of testing steps during development, production
and maintenance. This paper describes an approach to improve
testing of robot behavior. It was inspired by the RoboCup @work
competition that itself reflects a robotics benchmark for industrial
robotics. There, scaled down versions of mobile industrial robots
have to navigate through a workshop-like environment or operation
area and have to perform tasks of manipulating and transporting
work pieces. This paper will introduce an approach of automated
vision-based testing of the behavior of the so called youBot robot,
which is the most widely used robot platform in the RoboCup
@work competition. The proposed system allows automated testing
of multiple tries of the robot to perform a specific missions and
it allows for the flexibility of the robot, e.g. selecting different
paths between two tasks within a mission. The approach is based
on a multi-camera setup using, off the shelf cameras and optical
markers. It has been applied for test-driven development (TDD) and
maintenance-like verification of the robot behavior and performance.
", keywords = "Supervisory control, Testing, Markers, Mono Vision, Automation.", volume = "8", number = "5", pages = "737-4", }
