Abstract
In physical Human-Robot Cooperation (pHRC), humans and robots interact frequently or continuously to manipulate the same object or workpiece. One of the tasks within pHRC that has the highest potential for increased value in the industry is the cooperative lifting (co-lift) task where humans and robots lift long, flexible or heavy objects together. For such tasks, it is important for both safety and control that the human and robot can access motion information of the other to safely and accurately execute tasks together. In this paper, we propose to use Inertial Measurement Units (IMUs) to estimate human motions for pHRC, and also to use the IMU motion data to identify two-arm gestures that can aid in controlling the human-robot cooperation. We show how to use pHRC leader-follower roles to exploit the human cognitive skills to easily locate the object to lift, and robot skills to accurately place the object on a predefined target location. The experimental results presented show how to divide the co-lifting operation into stages: approaching the object while clutching in and out of controlling the robot motions, cooperatively lift and move the object towards a new location, and place the object accurately on a predefined target location. We believe that the results presented in this paper have the potential to further increase the uptake of pHRC in the industry since the proposed approach do not require any pre-installation of a positioning system or features of the object to enable pHRC.
This work was funded by the Research Council of Norway through grant number 280771.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Evrard, P., Gribovskaya, E., Calinon, S., Billard, A., Kheddar, A.: Teaching physical collaborative tasks: object-lifting case study with a humanoid. In: 2009 9th IEEE-RAS International Conference on Humanoid Robots. IEEE (2009). https://doi.org/10.1109/ichr.2009.5379513
Gu, Y., Thobbi, A., Sheng, W.: Human-robot collaborative manipulation through imitation and reinforcement learning. In: 2011 IEEE International Conference on Information and Automation. IEEE (2011). https://doi.org/10.1109/icinfa.2011.5948979
Kok, M., Hol, J.D., Schön, T.B.: An optimization-based approach to human body motion capture using inertial sensors. IFAC Proc. Vol. (IFAC-PapersOnline) 19, 79–85 (2014). https://doi.org/10.3182/20140824-6-ZA-1003.02252
Kok, M., Hol, J.D., Schön, T.B.: Using inertial sensors for position and orientation estimation (2017). https://doi.org/10.1561/2000000094
Kyrkjebo, E., et al.: The potential of physical human-robot cooperation using cobots on AGVs in flexible manufacturing, pp. 1–13 (under review)
Liu, S., Wang, L., Wang, X.V.: Sensorless haptic control for human-robot collaborative assembly. CIRP J. Manuf. Sci. Technol. 32, 132–144 (2021). https://doi.org/10.1016/j.cirpj.2020.11.015
Luzheng, B., Guan, C.: A review on EMG-based motor intention prediction of continuous human upper limb motion for human-robot collaboration. Biomed. Signal Process. Control 51, 113–127 (2019). https://doi.org/10.1016/j.bspc.2019.02.011
Malleson, C., Gilbert, A., Trumble, M., Collomosse, J., Hilton, A., Volino, M.: Real-time full-body motion capture from video and IMUs. In: 2017 International Conference on 3D Vision (3DV). IEEE (2017). https://doi.org/10.1109/3dv.2017.00058
von Marcard, T., Henschel, R., Black, M.J., Rosenhahn, B., Pons-Moll, G.: Recovering accurate 3D human pose in the wild using IMUs and a moving camera. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 614–631. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01249-6_37
Morato, C., Kaipa, K.N., Zhao, B., Gupta, S.K.: Toward safe human robot collaboration by using multiple kinects based real-time human tracking. J. Comput. Inf. Sci. Eng. 14(1) (2014)
Mörtl, A., Lawitzky, M., Kucukyilmaz, A., Sezgin, M., Basdogan, C., Hirche, S.: The role of roles: physical cooperation between humans and robots. Int. J. Robot. Res. 31(13), 1656–1674 (2012). https://doi.org/10.1177/0278364912455366
Nemec, B., Likar, N., Gams, A., Ude, A.: Human robot cooperation with compliance adaptation along the motion trajectory. Auton. Robot. 42(5), 1023–1035 (2017). https://doi.org/10.1007/s10514-017-9676-3
Ramasubramanian, A.K., Papakostas, N.: Operator - mobile robot collaboration for synchronized part movement. Procedia CIRP 97, 217–223 (2021). https://doi.org/10.1016/j.procir.2020.05.228
Roetenberg, D., Luinge, H., Slycke, P.: Xsens MVN: full 6DOF human motion tracking using miniature inertial sensors. Xsens Motion Tech. BV, Tech. Rep. 1, 1–7 (2009). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.569.9604&rep=rep1&type=pdf
Sheng, W., Thobbi, A., Gu, Y.: An integrated framework for human–robot collaborative manipulation. IEEE Trans. Cybern. 45(10), 2030–2041 (2015). https://doi.org/10.1109/tcyb.2014.2363664
Haddadin, S., Croft, E.: Physical human–robot interaction. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics, pp. 1835–1874. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32552-1_69
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Ateş, G., Kyrkjebø, E. (2021). Human-Robot Cooperative Lifting Using IMUs and Human Gestures. In: Fox, C., Gao, J., Ghalamzan Esfahani, A., Saaj, M., Hanheide, M., Parsons, S. (eds) Towards Autonomous Robotic Systems. TAROS 2021. Lecture Notes in Computer Science(), vol 13054. Springer, Cham. https://doi.org/10.1007/978-3-030-89177-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-89177-0_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-89176-3
Online ISBN: 978-3-030-89177-0
eBook Packages: Computer ScienceComputer Science (R0)