Hyperparameter Optimization of Artificial Neural Networks to Improve the Positional Accuracy of Industrial Robots
More details
Hide details
Institute for Machine Tools and Factory Management IWF, TU Berlin, Germany
Institute for Production Systems and Design Technology IPK, Fraunhofer, Germany
Submission date: 2020-11-30
Final revision date: 2021-03-02
Acceptance date: 2021-03-14
Online publication date: 2021-06-10
Publication date: 2021-06-25
Corresponding author
Julian Blumberg   

Institute for Machine Tools and Factory Management IWF, TU Berlin, Pascalstraße 8 - 9, 10587, Berlin, Germany
Journal of Machine Engineering 2021;21(2):47-59
Due to the rising demand for individualized product specifications and short innovation cycles, industrial robots gain increasing attention for machining operations as milling and forming. Limitations in their absolute positional accuracy are addressed by enhanced modelling and calibration techniques. However, the resulting absolute positional accuracy stays in a range still not feasible for general purpose milling and forming tolerances. Improvements of the model accuracy demand complex, often not accessible system knowledge on the expense of realtime capability. This article presents a new approach using artificial neural networks to enhance positional accuracy of industrial robots. A hyperparameter optimization is applied, to overcome the downside of choosing an appropriate artificial neural network structure and training strategy in a trial and error procedure. The effectiveness of the method is validated with a heavy-duty industrial robot. It is demonstrated that artificial neural networks with suitable hyperparameters outperform a kinematic model with calibrated geometric parameters.
ELATTA A.Y., GEN L.P., ZHI F.L., DAOYUAN Y., FEI L., 2004, An Overview of Robot Calibration, Information Technology Journal, 3/1, 74–78.
KHALIL W., DOMBRE E., 2004, Modeling, Identification and Control of Robots, Butherworth-Heinemann, Oxford.
VEITSCHEGGER W., WU C.H., 1987, A Method for Calibrating and Compensating Robot Kinematic Errors, Proceeding IEEE International Conference on Robotics and Automation, 39–44.
JINGFU P., YE D., GANG Z., HAN D., 2019, An Enhanced Kinematic Model for Calibration of Robotic Machining Systems with Parallelogram Mechanisms, Robotics and Computer Integrated Manufacturing, 59, 92–103.
KLIMCHIK A., 2012, Enhanced Stiffness Modeling of Serial and Parallel Manipulators for Robotic-Based Processing of High Performance Materials, PhD Thesis, Ecole des Mines de Nantes, France.
NGUYEN H.N., LE P.N., KANG H.J., 2019, A New Calibration Method for Enhancing Robot Position Accuracy by Combining a Robot Model–Based Identification Approach and an Artificial Neural Network–Based Error Compensation Technique, Advances in Mechanical Engineering, 11/1, 1–11.
FAN J., MA K., ZHONG Y., 2019, A Selective Overview of Deep Learning, arXiv preprint.
ANGELIDIS A., VOSNIAKOS G.C., 2014, Prediction and Compensation of Relative Position Error Along Industrial Robot End-Effector Paths, Int. J. Precis. Eng. Manuf., 15/1, 63–73.
NGUYEN H.N., ZHOUG J., KANG, H.J., 2015, A Calibration Method for Enhancing Robot Accuracy Through Integration of an Extended Kalman Filter Algorithm and an Artificial Neural Network, Neurocomputing, 151, 996–1,005.
AOYAGI S., KOHAMA A., NAKATA Y., HAYANO Y., SUZUKI M., 2010. Improvement of Robot Accuracy by Calibrating Kinematic Model Using a Laser Tracking System – Compensation of Non-Geometric Errors Using Neural Networks and Selection of Optimal Measuring Points Using Genetic Algorithm, IEEE RSJ International Conference on Intelligent Robots and Systems, 5,660–5,665.
ZHU Q., LI J., YUAN P., SHI Z., LIN M., CHEN D., WANG T., 2016, Accuracy Compensation of a Spraying Robot Based on RBF Neural Network, International Conference on Advanced Robotics and Mechatronics (ICARM), 414–419.
CAI Y., YUAN P., CHEN D., GAO D., WU X., XUE L., WANG T., 2017, A Calibration Method of Industrial Robots Based on ELM, 2nd International Conference on Advanced Robotics and Mechatronics (ICARM), 70–75.
YUAN P., CHEN D., WANG T., CAO S., CAI Y., XUE L., 2018, A compensation Method Based on Extreme Learning Machine to Enhance Absolute Position Accuracy for Aviation Drilling Robot, Advances in Mechanical Engineering, 10/3, 1–11.
LE P.N., KANG H.J., 2020, A Robotic Calibration Method Using a Model-Based Identification Technique and an Ivasive Weed Optimization Neural Network Compensator, Applied Sciences, 10/20, 1–14.
ELSKEN T., METZE J.H., HUTTER F., 2019, Neural Architecture Search: A Survey, Journal of Machine Learning Research, 20, 1–21.
AKIBA T., SANO S., YANASE T., OHTA T., KOYAMA M., 2019, Optuna: A Next-generation Hyperparameter Optimization, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage AK USA, 2,623–2,631.
BERGSTRA J., BENGIO Y., 2012. Random Search for Hyper-Parameter Optimization, Journal of Machine Learning Research, 13, 281–305.
BERGSTRA J., BARDENET R., BENGIO Y., KÉGL B., 2011. Algorithms for Hyper-Parameter Optimization, Proceedings of the 24th International Conference on Nerual Information Processing Systems, 2,545–2,554.
JAMIESON K., TALWALKAR A., 2016. Non-Stochastic Best Arm Identification and Hyperparameter Optimization, Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 51, 240–248.
LI L., JAMIESON K., DESALVO G., ROSTAMIZADEH A., TALWALKAR A., 2018, Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization, Journal of Machine Learning Research, 18, 1–52.
WU L., REN H., 2013, Finding the Kinematic Base Frame of a Robot by Hand-Eye Calibration Using 3D Position Data, IEEE Transactions on Automation Science and Engineering, 14/1, 314–324.
Journals System - logo
Scroll to top