Cobot User Frame Calibration: Evaluation and Comparison between Positioning Repeatability Performances Achieved by Traditional and Vision-Based Methods

Since cobots are designed to be flexible, they are frequently repositioned to change the production line according to the needs; hence, their working area (user frame) needs to be often calibrated. Therefore, it is important to adopt a fast and intuitive user frame calibration method that allows even non-expert users to perform the procedure effectively, reducing the possible mistakes that may arise in such contexts. The aim of this work was to quantitatively assess the performance of different user frame calibration procedures in terms of accuracy, complexity, and calibration time, to allow a reliable choice of which calibration method to adopt and the number of calibration points to use, given the requirements of the specific application. This has been done by first analyzing the performances of a Rethink Robotics Sawyer robot built-in user frame calibration method (Robot Positioning System, RPS) based on the analysis of a fiducial marker distortion obtained from the image acquired by the wrist camera. This resulted in a quantitative analysis of the limitations of this approach that only computes local calibration planes, highlighting the reduction of performances observed. Hence, the analysis focused on the comparison between two traditional calibration methods involving rigid markers to determine the best number of calibration points to adopt to achieve good repeatability performances. The analysis shows that, among the three methods, the RPS one resulted in very poor repeatability performances (1.42 mm), while the three and five points calibration methods achieve lower values (0.33 mm and 0.12 mm, respectively) which are closer to the reference repeatability (0.08 mm). Moreover, comparing the overall calibration times achieved by the three methods, it is shown that, incrementing the number of calibration points to more than five, it is not suggested since it could lead to a plateau in the performances, while increasing the overall calibration time.

[1]  Yunhui Liu,et al.  Vision-Based Calibration of Dual RCM-Based Robot Arms in Human-Robot Collaborative Minimally Invasive Surgery , 2018, IEEE Robotics and Automation Letters.

[2]  Bahram Ravani,et al.  An overview of robot calibration , 1987, IEEE Journal on Robotics and Automation.

[3]  Paolo Righettini,et al.  A homogeneous matrix approach to 3D kinematics and dynamics—II. Applications to chains of rigid bodies and serial manipulators , 1996 .

[4]  A. Hall.  The Method of Least Squares , 1872, Nature.

[5]  ハエゲルマルック アンデシュ.  Robot positioning system , 2013 .

[6]  Qiang Chen,et al.  3 Points Calibration Method of Part Coordinates for Arc Welding Robot , 2008, ICIRA.

[7]  Jeremy L. Rickli,et al.  A Framework for Collaborative Robot (CoBot) Integration in Advanced Manufacturing Systems , 2016 .

[8]  Hanqi Zhuang,et al.  Autonomous robot calibration using vision technology , 2007 .

[9]  Frank Shaopeng Cheng.  Calibration of Robot Reference Frames for Enhanced Robot Positioning Accuracy , 2008 .

[10]  Cristina Nuzzi,et al.  MEGURU: a gesture-based robot program builder for Meta-Collaborative workstations , 2021, Robotics Comput. Integr. Manuf..

[11]  Alan B. Craig.  Augmented Reality Concepts , 2013 .

[12]  Ping Zhang,et al.  Online robot calibration based on vision measurement , 2013 .

[13]  Gregory D. Hager,et al.  A framework for end-user instruction of a robot assistant for manufacturing , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[14]  Roberto Pagani,et al.  Evaluation and Modeling of the Friction in Robotic Joints Considering Thermal Effects , 2020 .

[15]  Nicola Pedrocchi,et al.  A Robust Linear Control Strategy to Enhance Damping of a Series Elastic Actuator on a Collaborative Robot , 2020, J. Intell. Robotic Syst..

[16]  Ilian A. Bonev,et al.  Assessment of the positioning performance of an industrial robot , 2012, Ind. Robot.

[17]  J. Edward Colgate,et al.  Nonholonomic haptic display , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[18]  Riby Abraham Boby,et al.  Single image based camera calibration and pose estimation of the end-effector of a robot , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[19]  Giacomo Palmieri,et al.  Real-Time Strategy for Obstacle Avoidance in Redundant Manipulators , 2021 .

[20]  Francesco Leali,et al.  Offline workpiece calibration method for robotic reconfigurable machining platform , 2014, Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA).

[21]  Ronald Lumia,et al.  Calibration of industrial robots by magnifying errors on a distant plane , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[22]  Cristina Nuzzi,et al.  Hands-Free: a robot augmented reality teleoperation system , 2020, 2020 17th International Conference on Ubiquitous Robots (UR).

[23]  Giulio Rosati,et al.  One-Step Fast Calibration of an Industrial Workcell , 2021 .

[24]  Roberto Pagani,et al.  The Influence of Heat Exchanges on Friction in Robotic Joints: Theoretical Modelling, Identification and Experiments , 2020 .

[25]  Sun Lei,et al.  Geometry-based robot calibration method , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[26]  G. Duelen,et al.  Robot calibration—Method and results , 1991 .

[27]  J. E. Colgate,et al.  Cobots: Robots for Collaboration With Human Operators , 1996, Dynamic Systems and Control.

[28]  Paulo Ricardo Marques de Araujo,et al.  Computer vision system for workpiece referencing in three-axis machining centers , 2020 .

[29]  Giulio Rosati,et al.  Human-Robot Collaboration in Manufacturing Applications: A Review , 2019, Robotics.

[30]  Francisco José Madrid-Cuevas,et al.  Automatic generation and detection of highly reliable fiducial markers under occlusion , 2014, Pattern Recognit..

[31]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  Dong-Shu Wang,et al.  Calibration of the arc-welding robot by neural network , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[33]  Jay B. West,et al.  Predicting error in rigid-body point-based registration , 1998, IEEE Transactions on Medical Imaging.

[34]  C. Brisan,et al.  Aspects of Calibration and Control of PARTNER Robots , 2006, 2006 IEEE International Conference on Automation, Quality and Testing, Robotics.

[35]  G. Legnani,et al.  A homogeneous matrix approach to 3D kinematics and dynamics — I. Theory , 1996 .

[36]  Muhammad Mujtaba Asad,et al.  Collaborative Robots and Industrial Revolution 4.0 (IR 4.0) , 2020, 2020 International Conference on Emerging Trends in Smart Technologies (ICETST).

[37]  Biao Zhang,et al.  Toward general industrial robot cell calibration , 2011, 2011 IEEE 5th International Conference on Robotics, Automation and Mechatronics (RAM).