TR2019-055
Anomaly Detection for Insertion Tasks in Robotic Assembly Using Gaussian Process Models
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- "Anomaly Detection for Insertion Tasks in Robotic Assembly Using Gaussian Process Models", European Control Conference (ECC), DOI: 10.23919/ECC.2019.8795698, June 2019, pp. 1017-1022.BibTeX TR2019-055 PDF
- @inproceedings{Romeres2019jun,
- author = {Romeres, Diego and Jha, Devesh K. and Dau, Hoang and Yerazunis, William S. and Nikovski, Daniel N.},
- title = {Anomaly Detection for Insertion Tasks in Robotic Assembly Using Gaussian Process Models},
- booktitle = {European Control Conference (ECC)},
- year = 2019,
- pages = {1017--1022},
- month = jun,
- publisher = {IEEE},
- doi = {10.23919/ECC.2019.8795698},
- isbn = {978-3-907144-00-8},
- url = {https://www.merl.com/publications/TR2019-055}
- }
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- "Anomaly Detection for Insertion Tasks in Robotic Assembly Using Gaussian Process Models", European Control Conference (ECC), DOI: 10.23919/ECC.2019.8795698, June 2019, pp. 1017-1022.
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MERL Contacts:
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Research Areas:
Artificial Intelligence, Control, Data Analytics, Machine Learning, Robotics
Abstract:
Component insertion is a common task in robotic assembly, and is widely used for manufacturing a variety of electronic devices. This task is generally characterized by low tolerances, thus requiring high precision during assembly. An early detection of a fault in the mating during the insertion process enables quality control of the end products, as well as safeguards the robotic equipment. We propose to use Gaussian Process Regression-based methods to learn the force profile during successful insertions, as well as quantify permissible deviations from this profile. The GPR model is then used to detect anomalies in case the observed force profile deviates significantly from the expected range. Apart from the standard GPR formulation, we consider two other variants – the Heteroscedastic GPR and the local GPR for better modeling accuracy and computational time efficiency, respectively. We report an accuracy of 100% in differentiating between normal and faulty insertions. The modeling and detection results indicate that our approach is accurate and robust to severe uncertainties due to process (e.g., force drift) and measurement noise.
Related News & Events
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NEWS MERL researchers presented more than 8 papers in European Control Conference, ECC 2019 Date: June 25, 2019 - June 28, 2019
Where: Naples, Italy
MERL Contacts: Scott A. Bortoff; Ankush Chakrabarty; Stefano Di Cairano; Devesh K. Jha; Christopher R. Laughman; Daniel N. Nikovski; Diego Romeres; William S. Yerazunis
Research Areas: Control, Machine Learning, OptimizationBrief- The European Control Conference is the premier control conference in Europe. This year MERL was well represented with papers on control for HVAC, machine learning for estimation and control, robot assembly, and optimization methods for control.