Automated generation of component system for the calibration of the service robot kinematic parameters

In the article we propose a fast and cheap strategy of service robot kinematic parameters calibration. Our method is based on sensors (cameras in particular) that are already mounted on the robot and inexpensive markers, that are easy to fix on the robot arms. We developed the method to compute the impact of manipulator velocity on markers localization error in camera image. Thanks to our method, the calibration data acquisition can be significantly shortened, because a certain, acceptable marker detection error threshold can be introduced that allows to acquire the data with non-zero velocity of the manipulator. Additionally, we propose a method of automatic component based data acquisition system generation, based on the embodied agent theory and tree like kinematic model representation of the robot. Our model is suitable for the most of service robots. The same model forms the base for an automatic generation of measure functions for cost functions used in the optimization process to find the optimal set of model parameters. The whole approach has been verified experimentally using Velma service robot.

[1]  Alessandro Roncone,et al.  Automatic kinematic chain calibration using artificial skin: Self-touch in the iCub humanoid robot , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[2]  Oliver Birbach,et al.  Rapid development of manifold-based graph optimization systems for multi-sensor calibration and SLAM , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Berthold Bäuml,et al.  Rapid calibration of a multi-sensorial humanoid’s upper body: An automatic and self-contained approach , 2015, Int. J. Robotics Res..

[4]  Herman Bruyninckx,et al.  Open robot control software: the OROCOS project , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[5]  Piotr Trojanek.  Model-driven engineering approach to design and implementation of robot control system , 2013, ArXiv.

[6]  Maciej Stefanczyk,et al.  Localization of Essential Door Features for Mobile Manipulation , 2014, Recent Advances in Automation, Robotics and Measuring Techniques.

[7]  Steven Dubowsky,et al.  An analytical method to eliminate the redundant parameters in robot calibration , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[8]  Konrad Banachowicz,et al.  Multi-sensory Feedback Control in Door Approaching and Opening , 2014, IEEE Conf. on Intelligent Systems.

[9]  Christian Schlegel,et al.  Model-driven engineering and run-time model-usage in service robotics , 2011, GPCE '11.

[10]  Konrad Banachowicz,et al.  Opening a door with a redundant impedance controlled robot , 2013, 9th International Workshop on Robot Motion and Control.

[11]  Cezary Zielinski,et al.  Motion Generation in the MRROC++ Robot Programming Framework , 2010, Int. J. Robotics Res..

[12]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[13]  Konrad Banachowicz,et al.  Two Mode Impedance Control of Velma Service Robot Redundant Arm , 2015, Progress in Automation, Robotics and Measuring Techniques.

[14]  C.J.H. Mann,et al.  A Practical Guide to SysML: The Systems Modeling Language , 2009 .

[15]  Kurt Konolige,et al.  Calibrating a Multi-arm Multi-sensor Robot: A Bundle Adjustment Approach , 2010, ISER.

[16]  Cezary Zielinski,et al.  A systematic method of designing control systems for service and field robots , 2014, 2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR).