Abstract
One of the most important problems in teleoperation systems is time delay and packet loss in the communication channel, which can affect transparency and stability. One way to overcome the time delay effects in a teleoperation system is to predict the master-side motion. In this way, when data is received in the slave side, it will be considered as the current position of the master robot and, thus, complete transparency could be achieved. The majority of the previous works regarding operator position prediction have considered known and constant time delay in the system; however, in the real applications, time delay is unknown and variable. In this paper, an adaptive online prediction approach based on artificial neural network (NN) is proposed. The time delay of the communication channel is estimated using an observer based on the dynamics of the master and slave sides. Then an artificial NN predicts the master-side motion based on the current available data of the master robot and the variable time delay estimated by the observer. This adaptive prediction approach is utilized in simulations and experiments on Phantom Omni haptic devices. The simulation results indicate the feasibility of this approach. It is revealed that this approach can predict an alternative human’s hand motion in a teleoperation system with unknown and variable time delay. Finally, the simulation results would be supported by experimental results.
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References
Uddin R, Ryu J (2016) Predictive control approaches for bilateral teleoperation. Ann Rev Control 42:82–99
Passenberg C, Peer A, Buss M (2010) A survey of environment-, operator-, and task-adapted controllers for teleoperation systems. Mechatronics 20(7):787–801
Choi H, Jung S (2020) Teleoperation control of a position-based impedance force controlled mobile robot by neural network learning: experimental studies. Asian J Control 22(1):92–103
Xu X, Cizmeci B, Schuwerk C, Steinbach E (2016) Model-mediated teleoperation: toward stable and transparent teleoperation systems. IEEE Access 4:425–449
Mitra P, Niemeyer G (2008) Model-mediated telemanipulation. Int J Robot Res 27(2):253–262
Yazdankhoo B, Beigzadeh B (2019) Increasing stability in model-mediated teleoperation approach by reducing model jump effect. Sci Iran 26:3–14. https://doi.org/10.24200/sci.2017.20007(Special Issue on: Socio-Cognitive Engineering)
Li S, Bowman M, Nobarani H, Zhang X (2020) Inference of manipulation intent in teleoperation for robotic assistance. J Intell Rob Syst. https://doi.org/10.1007/s10846-019-01125-8
Jarrassé N, Paik J, Pasqui V, Morel G (2008) How can human motion prediction increase transparency? In: 2008 IEEE international conference on robotics and automation (ICRA), Pasadena, CA, USA. IEEE, pp 2134–2139
Uddin R, Park S, Ryu J (2016) A predictive energy-bounding approach for Haptic teleoperation. Mechatronics 35:148–161
Engelbrecht SE (2001) Minimum principles in motor control. J Math Psychol 45(3):497–542
Smith C, Jensfelt P (2010) A predictor for operator input for time-delayed teleoperation. Mechatronics 20(7):778–786
Feth D, Peer A, Buss M (2014) Enhancement of multi-user teleoperation systems by prediction of dyadic haptic interaction. In: Experimental robotics. Springer, pp 855–869
Prekopiou P, Tzafestas SG, Harwin WS (1999) Towards variable-time-delays-robust telemanipulation through master state prediction. In: IEEE/ASME international conference on advanced intelligent mechatronics, Atlanta, USA. IEEE, pp 305–310
Clarke S, Schillhuber G, Zaeh MF, Ulbrich H (2008) Prediction-based methods for teleoperation across delayed networks. Multimedia Syst 13(4):253–261
Stakem F, AlRegib G (2009) An adaptive approach to exponential smoothing for CVE state prediction. In: 2nd International conference on immersive telecommunications, Berkley, USA. Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (ICST), pp 141–146
Sakr N, Georganas ND, Zhao J, Shen X (2007) Motion and force prediction in haptic media. In: 2007 IEEE International Conference on Multimedia and Expo, Beijing, China. IEEE, pp 2242–2245
Pentland A, Liu A (1999) Modeling and prediction of human behavior. Neural Comput 11(1):229–242
Hua C-C, Yang Y, Guan X (2013) Neural network-based adaptive position tracking control for bilateral teleoperation under constant time delay. Neurocomputing 113:204–212
Nava J, Kreinovich V (2014) Why a model produced by training a neural network is often more computationally efficient than a nonlinear regression model: a theoretical explanation. J Uncertain Syst 8(3):193–204
Nicolau V, Palade V, Aiordachioaie D, Miholca C (2007) Neural network prediction of the roll motion of a ship for intelligent course control. In: International conference on knowledge-based and intelligent information and engineering systems, Vietri sul Mare, Italy. Springer, pp 284–291
Alvarez-Aguirre A, Van De Wouw N, Oguchi T, Nijmeijer H (2014) Predictor-based remote tracking control of a mobile robot. IEEE Trans Control Syst Technol 22(6):2087–2102
Alt GH, Lages WF (2003) Networked robot control with delay compensation. In: 5th Real-Time Linux Workshop
Lian F-L, Moyne J, Tilbury D (2001) Time delay modeling and sample time selection for networked control systems. In: Proceedings of ASME-DSC. DCS, New York, pp 5–6
Jiang W, Schulzrinne H (2000) Modeling of packet loss and delay and their effect on real-time multimedia service quality. In: Proceedings of NOSSDAV’2000. Citeseer
Liu T, Boumaiza S, Ghannouchi F (2004) Dynamic behavioral modeling of 3G power amplifiers using real-valued time-delay neural networks. IEEE Trans Microw Theory Tech 52(3):1025–1033
Han K-H, Kim S, Kim Y-J, Kim J-H (2001) Internet control architecture for internet-based personal robot. Auton Robot 10(2):135–147
Juanping Z, Xianwen G (2009) Time-delay analysis and estimation of Internet-based robot teleoperation system. In: Control and decision conference, CCDC’09. Chinese. IEEE, pp 4643–4646
Çetin K, Bayrak A, Tatlicioglu E (2016) Online time delay estimation in networked control systems with application to bilateral teleoperation. In: Control conference (ECC), European. IEEE, pp 1007–1012
Teklemariam HG, Das A (2017) A case study of Phantom Omni force feedback device for virtual product design. Int J Interact Des Manuf (IJIDeM) 11(4):881–892
Chen H, Huang P, Liu Z, Ma Z (2020) Time delay prediction for space telerobot system with a modified sparse multivariate linear regression method. Acta Astronaut 166:330–341
Yazdankhoo B, Nikpour M, Beigzadeh B, Meghdari A (2019) Improvement of operator position prediction in teleoperation systems with time delay: simulation and experimental studies on Phantom Omni devices. Jordan J Mech Ind Eng 13(3):197–205
Mehrotra K, Mohan CK, Ranka S (2000) Elements of artificial neural networks. MIT Press, Cambridge
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Nikpour, M., Yazdankhoo, B., Beigzadeh, B. et al. Adaptive online prediction of operator position in teleoperation with unknown time-varying delay: simulation and experiments. Neural Comput & Applic 33, 7575–7592 (2021). https://doi.org/10.1007/s00521-020-05502-5
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DOI: https://doi.org/10.1007/s00521-020-05502-5