research-article

A Polishing Robot Force Control System Based on Time Series Data in Industrial Internet of Things

Authors:

Jianzhong Yang,

Li YangAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 21, Issue 2

Article No.: 34, Pages 1 - 22

https://doi.org/10.1145/3419469

Published: 08 March 2021 Publication History

Abstract

Installing a six-dimensional force/torque sensor on an industrial arm for force feedback is a common robotic force control strategy. However, because of the high price of force/torque sensors and the closedness of an industrial robot control system, this method is not convenient for industrial mass production applications. Various types of data generated by industrial robots during the polishing process can be saved, transmitted, and applied, benefiting from the growth of the industrial internet of things (IIoT). Therefore, we propose a constant force control system that combines an industrial robot control system and industrial robot offline programming software for a polishing robot based on IIoT time series data. The system mainly consists of four parts, which can achieve constant force polishing of industrial robots in mass production. (1) Data collection module. Install a six-dimensional force/torque sensor at a manipulator and collect the robot data (current series data, etc.) and sensor data (force/torque series data). (2) Data analysis module. Establish a relationship model based on variant long short-term memory which we propose between current time series data of the polishing manipulator and data of the force sensor. (3) Data prediction module. A large number of sensorless polishing robots of the same type can utilize that model to predict force time series. (4) Trajectory optimization module. The polishing trajectories can be adjusted according to the prediction sequences. The experiments verified that the relational model we proposed has an accurate prediction, small error, and a manipulator taking advantage of this method has a better polishing effect.

References

[1]

Ejaz Ahmed, Ibrar Yaqoob, Ibrahim Abaker Targio Hashem, Imran Khan, Abdelmuttlib Ibrahim Abdalla Ahmed, Muhammad Imran, and Athanasios V. Vasilakos. 2017. The role of big data analytics in Internet of Things. Computer Networks 129 (2017), 459–471.

Digital Library

[2]

Fadi Al-Turjman and Sinem Alturjman. 2018. Context-sensitive access in industrial internet of things (IIoT) healthcare applications. IEEE Transactions on Industrial Informatics 14, 6 (2018), 2736–2744.

[3]

Yoshua Bengio, Patrice Simard, Paolo Frasconi, et al. 1994. Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks 5, 2 (1994), 157–166.

Digital Library

[4]

George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel, and Greta M. Ljung. 2015. Time Series Analysis: Forecasting and Control. John Wiley & Sons.

[5]

Himanshu Chaudhary, Vikas Panwar, Rajendra Prasad, and Nagarajan Sukavanam. 2016. Adaptive neuro fuzzy based hybrid force/position control for an industrial robot manipulator. Journal of Intelligent Manufacturing 27, 6 (2016), 1299–1308.

Digital Library

[6]

Min Chen. 2013. Towards smart city: M2M communications with software agent intelligence. Multimedia Tools and Applications 67, 1 (2013), 167–178.

[7]

Sheng Chen, X. X. Wang, and Chris J. Harris. 2007. NARX-based nonlinear system identification using orthogonal least squares basis hunting. IEEE Transactions on Control Systems Technology 16, 1 (2007), 78–84.

[8]

Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.

[9]

Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555.

Digital Library

[10]

Richard Colbaugh, Homayoun Seraji, and Kristin Glass. 1993. Direct adaptive impedance control of robot manipulators. Journal of Robotic Systems 10, 2 (1993), 217–248.

[11]

Jerome Connor, Les E. Atlas, and Douglas R. Martin. 1992. Recurrent networks and NARMA modeling. In Advances in Neural Information Processing Systems. 301–308.

Digital Library

[12]

Sakyasingha Dasgupta and Takayuki Osogami. 2017. Nonlinear dynamic Boltzmann machines for time-series prediction. In 31st AAAI Conference on Artificial Intelligence.

Digital Library

[13]

Gianni Ferretti, G. Magnani, Paolo Rocco, Flavio Cecconello, and Gianmarco Rossetti. 2000. Impedance control for industrial robots. In Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065), Vol. 4. IEEE, 4027–4032.

[14]

Roger Frigola and Carl Edward Rasmussen. 2013. Integrated pre-processing for Bayesian nonlinear system identification with Gaussian processes. In Proceedings of the 52nd IEEE Conference on Decision and Control. IEEE, 5371–5376.

[15]

Hongbo Gao, Bo Cheng, Jianqiang Wang, Keqiang Li, Jianhui Zhao, and Deyi Li. 2018. Object classification using CNN-based fusion of vision and LIDAR in autonomous vehicle environment. IEEE Transactions on Industrial Informatics 14, 9 (2018), 4224–4231.

[16]

Piotr Gierlak and Marcin Szuster. 2017. Adaptive position/force control for robot manipulator in contact with a flexible environment. Robotics and Autonomous Systems 95 (2017), 80–101.

Digital Library

[17]

Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. 2013. Speech recognition with deep recurrent neural networks. In Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 6645–6649.

[18]

Wei He, Yiting Dong, and Changyin Sun. 2015. Adaptive neural impedance control of a robotic manipulator with input saturation. IEEE Transactions on Systems, Man, and Cybernetics: Systems 46, 3 (2015), 334–344.

[19]

José Miguel Hernández-Lobato, James R. Lloyd, and Daniel Hernández-Lobato. 2013. Gaussian process conditional copulas with applications to financial time series. In Advances in Neural Information Processing Systems. 1736–1744.

Digital Library

[20]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computation 9, 8 (1997), 1735–1780.

Digital Library

[21]

M. Shamim Hossain and Ghulam Muhammad. 2016. Cloud-assisted industrial internet of things (IIoT)–enabled framework for health monitoring. Computer Networks 101 (2016), 192–202.

Digital Library

[22]

Zhang Ji and Qi Anwen. 2010. The application of internet of things (IOT) in emergency management system in China. In Proceedings of the 2010 IEEE International Conference on Technologies for Homeland Security (HST’10). IEEE, 139–142.

[23]

Ming-Shaung Ju, C.-C. K. Lin, Dong-Huang Lin, I.-S. Hwang, and Shu-Min Chen. 2005. A rehabilitation robot with force-position hybrid fuzzy controller: Hybrid fuzzy control of rehabilitation robot. IEEE Transactions on Neural Systems and Rehabilitation Engineering 13, 3 (2005), 349–358.

[24]

Sang Hoon Kang, Maolin Jin, and Pyung Hun Chang. 2008. An IMC based enhancement of accuracy and robustness of impedance control. In Proceedings of the 2008 IEEE International Conference on Robotics and Automation. IEEE, 2623–2628.

[25]

Bill Karakostas. 2013. A DNS architecture for the internet of things: A case study in transport logistics. Procedia Computer Science 19 (2013), 594–601.

[26]

Prasanth Lade, Rumi Ghosh, and Soundar Srinivasan. 2017. Manufacturing analytics and industrial internet of things. IEEE Intelligent Systems 32, 3 (2017), 74–79.

[27]

Guokun Lai, Wei-Cheng Chang, Yiming Yang, and Hanxiao Liu. 2018. Modeling long-and short-term temporal patterns with deep neural networks. In Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, 95–104.

Digital Library

[28]

Nikolay Laptev, Jason Yosinski, Li Erran Li, and Slawek Smyl. 2017. Time-series extreme event forecasting with neural networks at uber. In International Conference on Machine Learning, Vol. 34. 1–5.

[29]

Dale A. Lawrence. 1988. Impedance control stability properties in common implementations. In Proceedings of the 1988 IEEE International Conference on Robotics and Automation. IEEE, 1185–1190.

[30]

Xiaomin Li, Di Li, Jiafu Wan, Athanasios V. Vasilakos, Chin-Feng Lai, and Shiyong Wang. 2017. A review of industrial wireless networks in the context of Industry 4.0. Wireless Networks 23, 1 (2017), 23–41.

Digital Library

[31]

Shih-Tin Lin and Ang-Kiong Huang. 1998. Hierarchical fuzzy force control for industrial robots. IEEE Transactions on Industrial Electronics 45, 4 (1998), 646–653.

[32]

Zachary C. Lipton, David C. Kale, Charles Elkan, and Randall Wetzel. 2015. Learning to diagnose with LSTM recurrent neural networks. arXiv preprint arXiv:1511.03677.

[33]

Jianqi Liu, Qinruo Wang, Jiafu Wan, and Jianbin Xiong. 2012. Towards real-time indoor localization in wireless sensor networks. In Proceedings of the 2012 IEEE 12th International Conference on Computer and Information Technology. IEEE, 877–884.

Digital Library

[34]

Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. 2016. Recurrent neural network for text classification with multi-task learning. arXiv preprint arXiv:1605.05101.

Digital Library

[35]

Ruonan Liu, Guotao Meng, Boyuan Yang, Chuang Sun, and Xuefeng Chen. 2016. Dislocated time series convolutional neural architecture: An intelligent fault diagnosis approach for electric machine. IEEE Transactions on Industrial Informatics 13, 3 (2016), 1310–1320.

[36]

Francisco Martínez-Álvarez, Alicia Troncoso, Gualberto Asencio-Cortés, and José C. Riquelme. 2015. A survey on data mining techniques applied to electricity-related time series forecasting. Energies 8, 11 (2015), 13162–13193.

[37]

Matthew T. Mason. 1981. Compliance and force control for computer controlled manipulators. IEEE Transactions on Systems, Man, and Cybernetics 11, 6 (1981), 418–432.

[38]

Lichao Mou, Pedram Ghamisi, and Xiao Xiang Zhu. 2017. Deep recurrent neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 55, 7 (2017), 3639–3655.

[39]

Fusaomi Nagata, Yukihiro Kusumoto, Keigo Watanabe, Kunihiro Tsuda, Kiminori Yasuda, Kazuhiko Yokoyama, Masaaki Omoto, and Hiroyuki Miyako. 2004. Polishing robot for PET bottle molds using a learning-based hybrid position/force controller. In Proceedings of the 2004 5th Asian Control Conference (IEEE Cat. No. 04EX904), Vol. 2. IEEE, 914–921.

[40]

Mehrzad Namvar and Farhad Aghili. 2006. Adaptive force control of robots in presence of uncertainty in environment. In Proceedings of the 2006 American Control Conference. IEEE, 6–pp.

[41]

Henry Friday Nweke, Ying Wah Teh, Mohammed Ali Al-Garadi, and Uzoma Rita Alo. 2018. Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges. Expert Systems with Applications 105 (2018), 233–261.

[42]

Zhibo Pang, Qiang Chen, Weili Han, and Lirong Zheng. 2015. Value-centric design of the internet-of-things solution for food supply chain: Value creation, sensor portfolio and information fusion. Information Systems Frontiers 17, 2 (2015), 289–319.

Digital Library

[43]

Wei Qiuping, Zhu Shunbing, and Du Chunquan. 2011. Study on key technologies of Internet of Things perceiving mine. Procedia Engineering 26 (2011), 2326–2333.

[44]

Alexander M. Rush, Sumit Chopra, and Jason Weston. 2015. A neural attention model for abstractive sentence summarization. arXiv preprint arXiv:1509.00685.

[45]

Sunil K. Singh and Dan O. Popa. 1995. An analysis of some fundamental problems in adaptive control of force and impedance behavior: Theory and experiments. IEEE Transactions on Robotics and Automation 11, 6 (1995), 912–921.

[46]

Emiliano Sisinni, Abusayeed Saifullah, Song Han, Ulf Jennehag, and Mikael Gidlund. 2018. Industrial internet of things: Challenges, opportunities, and directions. IEEE Transactions on Industrial Informatics 14, 11 (2018), 4724–4734.

[47]

Huan Song, Deepta Rajan, Jayaraman J. Thiagarajan, and Andreas Spanias. 2018. Attend and diagnose: Clinical time series analysis using attention models. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence.

[48]

Fengjie Tian, Zhenguo Li, Chong Lv, and Guangbao Liu. 2016. Polishing pressure investigations of robot automatic polishing on curved surfaces. The International Journal of Advanced Manufacturing Technology 87, 1-4 (2016), 639–646.

[49]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems. 5998–6008.

Digital Library

[50]

Cyril Voyant, Gilles Notton, Soteris Kalogirou, Marie-Laure Nivet, Christophe Paoli, Fabrice Motte, and Alexis Fouilloy. 2017. Machine learning methods for solar radiation forecasting: A review. Renewable Energy 105 (2017), 569–582.

[51]

Jiafu Wan, Shenglong Tang, Di Li, Shiyong Wang, Chengliang Liu, Haider Abbas, and Athanasios V. Vasilakos. 2017. A manufacturing big data solution for active preventive maintenance. IEEE Transactions on Industrial Informatics 13, 4 (2017), 2039–2047.

[52]

Danwei Wang and N. Harris McClamroch. 1993. Position and force control for constrained manipulator motion: Lyapunov’s direct method. IEEE Transactions on Robotics and Automation 9, 3 (1993), 308–313.

[53]

Hao Wang, Ottar L. Osen, Guoyuan Li, Wei Li, Hong-Ning Dai, and Wei Zeng. 2015. Big data and industrial internet of things for the maritime industry in northwestern norway. In Proceedings of the TENCON 2015-2015 IEEE Region 10 Conference. IEEE, 1–5.

[54]

Kun Wang, Yihui Wang, Yanfei Sun, Song Guo, and Jinsong Wu. 2016. Green industrial Internet of Things architecture: An energy-efficient perspective. IEEE Communications Magazine 54, 12 (2016), 48–54.

[55]

Shiyong Wang, Jiafu Wan, Daqiang Zhang, Di Li, and Chunhua Zhang. 2016. Towards smart factory for industry 4.0: A self-organized multi-agent system with big data based feedback and coordination. Computer Networks 101 (2016), 158–168.

Digital Library

[56]

Martin Wollschlaeger, Thilo Sauter, and Juergen Jasperneite. 2017. The future of industrial communication: Automation networks in the era of the internet of things and industry 4.0. IEEE Industrial Electronics Magazine 11, 1 (2017), 17–27.

[57]

Li Yang, Ying Li, Jin Wang, and R. Simon Sherratt. 2020. Sentiment analysis for E-commerce product reviews in Chinese based on sentiment lexicon and deep learning. IEEE Access 8 (2020), 23522–23530.

[58]

Bin Yao, Sai Piu Chan, and Danwei Wang. 1994. VSC motion and force control of robot manipulators in the presence of environmental constraint uncertainties. Journal of Robotic Systems 11, 6 (1994), 503–515.

[59]

Hong Zhang and R. Paul. 1985. Hybrid control of robot manipulators. In Proceedings of the 1985 IEEE International Conference on Robotics and Automation, Vol. 2. IEEE, 602–607.

[60]

Qingchen Zhang, Chunsheng Zhu, Laurence T. Yang, Zhikui Chen, Liang Zhao, and Peng Li. 2017. An incremental CFS algorithm for clustering large data in industrial Internet of Things. IEEE Transactions on Industrial Informatics 13, 3 (2017), 1193–1201.

[61]

Lingxue Zhu and Nikolay Laptev. 2017. Deep and confident prediction for time series at Uber. In Proceedings of the 2017 IEEE International Conference on Data Mining Workshops (ICDMW ’17). IEEE, 103–110.

Cited By

Jia WZhan YSun JZhang JDai Y(2023)Grinding Constant Force Control based on PID Controller with Nonlinear Differential Link Optimized by Differential Evolution Algorithm2023 42nd Chinese Control Conference (CCC)10.23919/CCC58697.2023.10240482(4298-4303)Online publication date: 24-Jul-2023
https://doi.org/10.23919/CCC58697.2023.10240482
Zhang YAn HXing YLiu YZhang T(2023)Learning Temporal and Spatial Features Jointly: A Unified Framework for Space-Time Data Prediction in Industrial IoT NetworksIEEE Sensors Journal10.1109/JSEN.2023.327162923:16(18752-18764)Online publication date: 15-Aug-2023
https://doi.org/10.1109/JSEN.2023.3271629
Zhu YJiao J(2022)Automatic Control of Mobile Industrial Robot Based on Multiobjective Optimization StrategyJournal of Electrical and Computer Engineering10.1155/2022/78259062022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/7825906
Show More Cited By

Index Terms

A Polishing Robot Force Control System Based on Time Series Data in Industrial Internet of Things
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Robotics

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 21, Issue 2

June 2021

599 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3453144

Editor:
Ling Liu
Georgia Institute of Technology, USA

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Copyright © 2021 Association for Computing Machinery.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 08 March 2021

Accepted: 01 August 2020

Revised: 01 July 2020

Received: 01 May 2020

Published in TOIT Volume 21, Issue 2

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National Key Research and Development Program of China
National Natural Science Foundation of China
China Knowledge Centre for Engineering Sciences and Technology Project

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Cited By

Jia WZhan YSun JZhang JDai Y(2023)Grinding Constant Force Control based on PID Controller with Nonlinear Differential Link Optimized by Differential Evolution Algorithm2023 42nd Chinese Control Conference (CCC)10.23919/CCC58697.2023.10240482(4298-4303)Online publication date: 24-Jul-2023
https://doi.org/10.23919/CCC58697.2023.10240482
Zhang YAn HXing YLiu YZhang T(2023)Learning Temporal and Spatial Features Jointly: A Unified Framework for Space-Time Data Prediction in Industrial IoT NetworksIEEE Sensors Journal10.1109/JSEN.2023.327162923:16(18752-18764)Online publication date: 15-Aug-2023
https://doi.org/10.1109/JSEN.2023.3271629
Zhu YJiao J(2022)Automatic Control of Mobile Industrial Robot Based on Multiobjective Optimization StrategyJournal of Electrical and Computer Engineering10.1155/2022/78259062022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/7825906
Liu ZLi ZMa SLi LLi RZhou H(2022)A Steel Billet Crack Marking System: Design And Implementation2022 International Conference on Service Robotics (ICoSR)10.1109/ICoSR57188.2022.00029(107-112)Online publication date: Jun-2022
https://doi.org/10.1109/ICoSR57188.2022.00029

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