Human-Robot Co-Adaptation in Construction: Bio-Signal Based Control of Bricklaying Robots
Publication: Computing in Civil Engineering 2021
ABSTRACT
Robots will soon become as commonplace as heavy-duty vehicles at construction sites. Their emergence will create safety challenges, as most of them will work very near humans in the dynamic and unstructured workplace of construction. Among them, being stuck by robots could be a major cause of accidents. Robotic control techniques can tackle these challenges by providing efficient and reliable channels for worker-robot interaction. However, traditional techniques, such as haptic- and gesture-based control, require workers to use hands and arms as middleware. Therefore, in scenarios where workers’ range of motion is restricted—such as moving materials or roofing—physically interactive control may not be adequate. To bridge the gap, this study examines the feasibility of a novel approach. The technique employs an adaptive neural network to translate workers’ mental images of their bodily movements into robotic commands via brainwave signals. These commands regulate robots’ actions through human kinematics. To examine its performance, four subjects were instructed to control a masonry robot lifting and placing concrete bricks to build a wall. The results demonstrated that the average success rate of picking up and laying bricks was 84.02%, with a 3.35% standard deviation. This demonstrates the feasibility of controlling robots under the physically complex conditions of construction sites. It opens the door to safer interaction between workers and construction robots.
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REFERENCES
Andersen, R. S. (2018). Kinematics of a UR5. Aalborg University, May 2018, 1–12.
Ang, K. K., Chin, Z. Y., Wang, C., Guan, C., and Zhang, H. (2012). “Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b.” Frontiers in Neuroscience.
Bock, T., and Linner, T. (2016). “Single-Task Construction Robots by Category.” Construction Robots, Cambridge University Press, Cambridge, 14–290.
Chen, J. Y. C., Haas, E. C., and Barnes, M. J. (2007). “Human Performance Issues and User Interface Design for Teleoperated Robots.” IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 37(6), 1231–1245.
Correa, A. G., Laciar, E., Patiño, H. D., and Valentinuzzi, M. E. (2007). “Artifact removal from EEG signals using adaptive filters in cascade.” Journal of Physics: Conference Series.
Czarnowski, J., Dąbrowski, A., Maciaś, M., Główka, J., and Wrona, J. (2018). “Technology gaps in Human-Machine Interfaces for autonomous construction robots.” Automation in Construction, 94(June), 179–190.
van Erp, J., Lotte, F., and Tangermann, M. (2012). “Brain-Computer Interfaces: Beyond Medical Applications.” Computer, 45(4), 26–34.
Hirabayashi, T., Akizono, J., Yamamoto, T., Sakai, H., and Yano, H. (2006). “Teleoperation of construction machines with haptic information for underwater applications.” Automation in Construction, 15(5), 563–570.
Jebelli, H., Hwang, S., and Lee, S. (2018). “EEG Signal-Processing Framework to Obtain High-Quality Brain Waves from an Off-the-Shelf Wearable EEG Device.” Journal of Computing in Civil Engineering, 32(1), 1–12.
Kim, D., Kim, J., Lee, K., Park, C., Song, J., and Kang, D. (2009). “Excavator tele-operation system using a human arm.” Automation in Construction, Elsevier B.V., 18(2), 173–182.
Kovincic, N., Müller, A., Gattringer, H., Matthias, W., Andreas, S., and Mathias, B. (2005). “Dynamic parameter identification of the Universal Robots UR5.”
Lemhadri, I., Ruan, F., and Tibshirani, R. (2019). “A neural network with feature sparsity.”.
Liu, Y., Habibnezhad, M., and Jebelli, H. (2021a). “Brain-computer interface for hands-free teleoperation of construction robots.” Automation in Construction, Elsevier B.V., 123(November 2020), 103523.
Liu, Y., Habibnezhad, M., and Jebelli, H. (2021b). “Brainwave-driven human-robot collaboration in construction.” Automation in Construction, Elsevier B.V., 124(January), 103556.
Liu, Y., Habibnezhad, M., Jebelli, H., Asadi, S., and Lee, S. (2020). “Ocular Artifacts Reduction in EEG Signals Acquired at Construction Sites by Applying a Dependent Component Analysis (DCA).” Construction Research Congress 2020, American Society of Civil Engineers, Reston, VA, 1281–1289.
Liu, Y., Habibnezhad, M., Shayesteh, S., Jebelli, H., and Lee, S. (2021c). “Paving the Way for Future EEG Studies in Construction: Dependent Component Analysis for Automatic Ocular Artifact Removal from Brainwave Signals.” Journal of Construction Engineering and Management, 147(8), 04021087.
Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., and Yger, F. (2018). “A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update.” Journal of Neural Engineering, IOP Publishing, 15(3).
Bu, N., Okamoto, M., and Tsuji, T. (2009). “A Hybrid Motion Classification Approach for EMG-Based Human–Robot Interfaces Using Bayesian and Neural Networks.” IEEE Transactions on Robotics, 25(3), 502–511.
Petters, S., and Belden, R. (2014). SAM, the robotic bricklayer. SMART/Dynamics of Masonry.
Rezeika, A., Benda, M., Stawicki, P., Gembler, F., Saboor, A., and Volosyak, I. (2018). “Brain–Computer Interface Spellers: A Review.” Brain Sciences, 8(57), 1–38.
Robla-Gomez, S., Becerra, V. M., Llata, J. R., Gonzalez-Sarabia, E., Torre-Ferrero, C., and Perez-Oria, J. (2017). “Working Together: A Review on Safe Human-Robot Collaboration in Industrial Environments.” IEEE Access, 5, 26754–26773.
Shayesteh, S., and Jebelli, H. (2021). “Investigating the Impact of Construction Robots Autonomy Level on Workers’ Cognitive Load.” 2021 Canadian Society for Civil Engineering Annual Conference, 2021 CSCE.
Vasic, M., and Billard, A. (2013). “Safety issues in human-robot interactions.” 2013 IEEE International Conference on Robotics and Automation, IEEE, 197–204.
Villani, V., Pini, F., Leali, F., and Secchi, C. (2018). “Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications.” Mechatronics, Elsevier, 55(March), 248–266.
Woetzel, J., Mischke, J., Barbosa, F., Sridhar, M., Ribeirinho, M. J., Parsons, M., Bertram, N., and Brown, S. (2017). Reinventing construction: a route to higher productivity. McKinsey Global Institute.
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Computing in Civil Engineering 2021
Pages: 304 - 312
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Published online: May 24, 2022
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