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Special Issue on Human–Robot Interaction (HRI)

Published online by Cambridge University Press:  12 October 2020

Nikos Aspragathos ,
Vassilis Moulianitis  and
Panagiotis Koustoumpardis Open the ORCID record for Panagiotis Koustoumpardis [Opens in a new window]
Nikos Aspragathos
Affiliation:
Robotics Group, Mechanical and Aeronautics Engineering Department, University of Patras, Greece
Vassilis Moulianitis
Affiliation:
Department of Product and Systems Design Engineering, University of the Aegean, Greece
Panagiotis Koustoumpardis*
Affiliation:
Robotics Group, Mechanical and Aeronautics Engineering Department, University of Patras, Greece
*
**Corresponding author. E-mail: koust@upatras.gr
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Abstract

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Human–robot interaction (HRI) is one of the most rapidly growing research fields in robotics and promising for the future of robotics technology. Despite the fact that numerous significant research results in HRI have been presented during the last years, there are still challenges in several critical topics of HRI, which could be summarized as: (i) collision and safety, (ii) virtual guides, (iii) cooperative manipulation, (iv) teleoperation and haptic interfaces, and (v) learning by observation or demonstration. In physical HRI research, the complementarity of the human and the robot capabilities is carefully considered for the advancement of their cooperation in a safe manner. New advanced control systems should be developed so the robot will acquire the ability to adapt easily to the human intentions and to the given task. The possible applications requiring co-manipulation are cooperative transportation of bulky and heavy objects, manufacturing processes such as assembly and surgery.

Information

Type
Introduction to Special Issue
Information
Robotica , Volume 38 , Special Issue 10: Human–Robot Interaction (HRI) , October 2020 , pp. 1715 - 1716
Copyright
© The Author(s), 2020. Published by Cambridge University Press

References

Gordić, Z. and Jovanović, K., “Collision detection on industrial robots in repetitive tasks using modified dynamic time warping,” Robotica, 38(10), 1717–1736 (2020).Google Scholar
Sharkawy, A., Koustoumpardis, P. and Aspragathos, N., “Neural network design for manipulator collision detection based only on the joint position sensors,” Robotica, 38(10), 1737–1755 (2020).Google Scholar
Huber, G. and Wollherr, D., “An online trajectory generator on SE (3) for human–robot collaboration,” Robotica, 38(10), 1756–1777 (2020).Google Scholar
Restrepo, S. S., Raiola, G., Guerry, J., D'Elia, E., Lamy, X. and Sidobre, D., “Toward an intuitive and iterative 6D virtual guide programming framework for assisted human–robot comanipulation,” Robotica, 38(10), 1778–1806 (2020).Google Scholar
Žlajpah, L. and Petrič, T., “Unified virtual guides framework for path tracking tasks,” Robotica, 38(10), 1807–1823 (2020).Google Scholar
Papageorgiou, D., Dimeas, F., Kastritsi, T. and Doulgeri, Z., “Kinesthetic guidance utilizing DMP synchronization and assistive virtual fixtures for progressive automation,” Robotica, 38(10), 1824–1841 (2020).Google Scholar
Alevizos, K., Bechlioulis, C. and Kyriakopoulos, K., “Physical human-robot cooperation based on robust motion intention estimation,” Robotica, 38(10), 1842–1866 (2020).Google Scholar
Koskinopoulou, M., Maniadakis, M. and Trahanias, P., “Speed adaptation in learning from demonstration through latent space formulation,” Robotica, 38(10), 1867–1879 (2020).Google Scholar
Torabi, A., Khadem, M., Zareinia, K., Sutherland, G. R. and Tavakoli, M., “Using a redundant user interface in teleoperated surgical systems for task performance enhancement,” Robotica, 1880–1894.Google Scholar