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Unified Virtual Guides Framework for Path Tracking Tasks

Published online by Cambridge University Press:  26 July 2019

Leon Žlajpah  and
Tadej Petrič
Leon Žlajpah*
Affiliation:
Department of Automation, Biocybernetics and Robotics, Jožef Stefan Institute, Ljubljana, Slovenia E-mail: tadej.petric@ijs.si
Tadej Petrič
Affiliation:
Department of Automation, Biocybernetics and Robotics, Jožef Stefan Institute, Ljubljana, Slovenia E-mail: tadej.petric@ijs.si
*
*Corresponding author. E-mail: leon.zlajpah@ijs.si
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Summary

In this paper, we propose a novel unified framework for virtual guides. The human–robot interaction is based on a virtual robot, which is controlled by the admittance control. The unified framework combines virtual guides, control of the dynamic behavior, and path tracking. Different virtual guides and active constraints can be realized by using dead-zones in the position part of the admittance controller. The proposed algorithm can act in a changing task space and allows selection of the tasks-space and redundant degrees-of-freedom during the task execution. The admittance control algorithm can be implemented either on a velocity or on acceleration level. The proposed framework has been validated by an experiment on a KUKA LWR robot performing the Buzz-Wire task.

Keywords

Human–robot cooperationAdmittance controlVirtual guidesRedundant robotsPath following
Type
Articles
Information
Robotica , Volume 38 , Special Issue 10: Human–Robot Interaction (HRI) , October 2020 , pp. 1807 - 1823
Copyright
© Cambridge University Press 2019

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References

De Santis, A., Siciliano, B., De Luca, A. and Bicchi, A., “An atlas of physical human–robot interaction,” Mech. Mach. Theory 43(3), 253–270 (2008).CrossRefGoogle Scholar
Haddadin, S. and Croft, E., “Physical Human–Robot Interaction,” In:Springer Handbook of Robotics (Siciliano, B. B and Khatib, O., eds.) (Springer International Publishing, Cham, 2016) pp. 1835–1874.CrossRefGoogle Scholar
Ikemoto, S., Amor, H. B., Minato, T., Jung, B. and Ishiguro, H., “Physical human–robot interaction: mutual learning and adaptation,” IEEE Robot. Auto. Mag. 19(4), 24–35 (2012).CrossRefGoogle Scholar
Nemec, B., Likar, N., Gams, A. and Ude, A., “Human robot cooperation with compliance adaptation along the motion trajectory,” Auto. Robot. 42(5), 1023–1035 (2017).CrossRefGoogle Scholar
Mörtl, A., Lawitzky, M., Kucukyilmaz, A., Sezgin, M., Basdogan, C. and Hirche, S., “The role of roles: Physical cooperation between humans and robots,” Int. J. Robot. Res. 31(13), 1656–1674 (2012).CrossRefGoogle Scholar
Petrič, T., Goljat, R. and Babič, J., “Cooperative Human–Robot Control Based on Fitts’ Law,” 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), Cancun, Mexico (IEEE, 2016) pp. 345–350.CrossRefGoogle Scholar
Peternel, L., Petrič, T., Oztop, E. and Babič, J., “Teaching robots to cooperate with humans in dynamic manipulation tasks based on multi-modal human-in-the-loop approach,” Auto. Robot. 36(1–2), 123–136 (2014).CrossRefGoogle Scholar
Bicchi, A., Peshkin, M. A. and Colgate, J. E., “Safety for Physical Human–Robot Interaction,” In:Springer Handbook of Robotics (Siciliano, B. B and Khatib, O., eds.) (Springer, Berlin, Heidelberg, 2008) pp. 1335–1348.CrossRefGoogle Scholar
Petrič, T., Cevzar, M. and Babič, J., “Utilizing Speed-Accuracy Trade-Off Models for Human–Robot Coadaptation During Cooperative Groove Fitting Task,” 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), Birmingham, UK (2017) pp. 107–112.Google Scholar
Rosenberg, L., “Virtual Fixtures: Perceptual Tools for Telerobotic Manipulation,” Proceedings of IEEE Virtual Reality Annual International Symposium, Seattle, WA, USA (1993) pp. 76–82.Google Scholar
Bowyer, S. A., Davies, B. L. and Rodriguez y Baena, F., “Active constraints/virtual fixtures: A survey,” IEEE Trans. Robot. 30, 138–157 (2014).CrossRefGoogle Scholar
Bettini, A., Marayong, P., Lang, S., Okamura, A. and Hager, G. D., “Visual assisted control for manipulation using virtual fixtures,” IEEE Trans. Robot . 20(6), 953–966 (2004).CrossRefGoogle Scholar
Abbott, J. J., Marayong, P. and Okamura, A. M., “Haptic Virtual Fixtures for Robot-Assisted Manipulation,” In:Robotics Research. Springer Tracts in Advanced Robotics (Thrun, S., Brooks, R. and Durrant-Whyte, H., eds.), vol 28 (Springer, Berlin, Heidelberg, 2007) pp. 49–64.Google Scholar
Hager, G. D., “Human–Machine Cooperative Manipulation with Vision-Based Motion Constraints,” In:Lecture Notes in Control and Information Sciences Visual Servoing via Advanced Numerical Methods (Chesi, G. and Hashimoto, K., eds.), vol. 401 (Springer, London, 2010) pp. 55–70.CrossRefGoogle Scholar
Restrepo, S. S., Intuitive, Iterative and Assisted Virtual Guides Programming for Human–Robot Comanipulation, PhD thesis (2018).Google Scholar
Raiola, G., Lamy, X. and Stulp, F., “Co-manipulation with Multiple Probabilistic Virtual Guides,” 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol. 2015, Hamburg, Germany (2015) pp. 7–13.Google Scholar
Lee, D. and Ott, C., “Incremental kinesthetic teaching of motion primitives using the motion refinement tube,” Auto. Robot. 31(2–3), 115–131 (2011).CrossRefGoogle Scholar
Pezzementi, Z., Okamura, A. M. and Hager, G. D., “Dynamic Guidance with Pseudoadmittance Virtual Fixtures,” Proceedings 2007 IEEE International Conference on Robotics and Automation, Roma, Italy (IEEE, 2007) pp. 1761–1767.Google Scholar
Kragic, D., Marayong, P., Li, M., Okamura, A. M. and Hager, G. D., “Human–Machine collaborative systems for microsurgical applications,” Int. J. Robot. Res. 24(9), 731–741 (2005).Google Scholar
Restrepo, S. S., Raiola, G., Chevalier, P., Lamy, X. and Sidobre, D., “Iterative Virtual Guides Programming for Human–Robot Comanipulation,” 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Munich, Germany (IEEE, 2017) pp. 219–226CrossRefGoogle Scholar
Raiola, G., Restrepo, S. S., Chevalier, P., Rodriguez-Ayerbe, P., Lamy, X., Tliba, S. and Stulp, F., “Co-manipulation with a library of virtual guiding fixtures,” Autonomous Robots 42(5), 1037–1051 (2017).CrossRefGoogle Scholar
Siciliano, B., Sciavicco, L., Villani, L. and Oriolo, G., Robotics - Modelling, Planning and Control (Springer-Verlag, London, 2009).Google Scholar
Ott, C., Mukherjee, R. and Nakamura, Y., “Unified Impedance and Admittance Control,” 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, USA (2010) pp. 554–561.Google Scholar
Duchaine, V., Mayer St-Onge, B., Gao, D. and Gosselin, C., “Stable and intuitive control of an intelligent assist device,” IEEE Trans. Haptics 5(2), 148–159 (2012).CrossRefGoogle Scholar
Ficuciello, F., Villani, L. and Siciliano, B., “Variable impedance control of redundant manipulators for intuitive Human–Robot Physical Interaction,” IEEE Trans. Robot . 31, 850–863 (2015).CrossRefGoogle Scholar
Ranatunga, I., Lewis, F., Popa, D. O. and Tousif, S. M., “Adaptive admittance control for Human–Robot interaction using model reference design and adaptive inverse filtering,” IEEE Trans. Control Systems Technology 25(1), 1–10 (2017).CrossRefGoogle Scholar
Žlajpah, L. and Petrič, T., “Virtual Guides for Redundant Robots Using Admittance Control for Path Tracking Tasks,” In:Advances in Service and Industrial Robotics: Proceedings of the 27th International Conference on Robotics in Alpe–Adria Danube Region (RAAD 2018) (Aspragathos, N. A., Koustoumpardis, P. N. and Moulianitis, V. C., eds.) (Springer International Publishing, Cham, 2019) pp. 13–23.CrossRefGoogle Scholar
Albu-Schaffer, A., Ott, C. and Hirzinger, G., “A unified passivity-based control framework for position, torque and impedance control of flexible joint robots,” Int. J. Robot. Res. 26, 23–39 (2007).Google Scholar
Nakanishi, J., Cory, R., Mistry, M., Schaal, S. and Peters, J., “Operational space control: A theoretical and empirical comparison,” Int. J. Robot. Res. 27, 737–757 (2008).CrossRefGoogle Scholar
Duchaine, V. and Gosselin, C. M., “General Model of Human–Robot Cooperation Using a Novel Velocity Based Variable Impedance Control,” Second Joint EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (WHC’07), Tsukaba, Japan (2007) pp. 445–451.Google Scholar
Lecours, A., Mayer-St-Onge, B. and Gosselin, C., “variable Admittance Control of a Four-Degree-of-Freedom Intelligent Assist Device,” 2012 IEEE International Conference on Robotics and Automation, vol. 2, Saint Paul, MN, USA (2012) pp. 3903–3908.Google Scholar
Žlajpah, L., “Kinematic Control of Redundant Robots in Changing Task Space,” In:Advances in Robot Design and Intelligent Control. RAAD 2016. Advances in Intelligent Systems and Computing (Rodić, A. and Borangiu, T., eds.), vol. 540 (Springer, Cham) (2016) pp. 3–11.Google Scholar
Ude, A., Nemec, B., Petri, T. and Morimoto, J., “Orientation in Cartesian Space Dynamic Movement Primitives,” 2014 IEEE International Conference on Robotics and Automation (ICRA) Hong Kong, China (2014) pp. 2997–3004.Google Scholar
Žlajpah, L., “On Time Optimal Path Control of Manipulators with Bounded Joint Velocities and Torques,” IEEE International Conference on Robotics and Automation, Minneapolis, Minnesota (1996) pp. 1572–1577.Google Scholar
Mihelj, M. and Podobnik, J., “Virtual Fixtures,” In:Haptics for Virtual Reality and Teleoperation (Mihelj, M. and Podobnik, J., eds.) (Springer, Netherlands, 2012) pp. 179–200.CrossRefGoogle Scholar

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