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Kinesthetic Guidance Utilizing DMP Synchronization and Assistive Virtual Fixtures for Progressive Automation

Published online by Cambridge University Press:  14 October 2019

Dimitrios Papageorgiou ,
Fotios Dimeas Open the ORCID record for Fotios Dimeas [Opens in a new window] ,
Theodora Kastritsi  and
Zoe Doulgeri
Dimitrios Papageorgiou
Affiliation:
Automation & Robotics Lab, Department of Electrical & Computer Engineering, Aristotle University of Thessaloniki, Greece. E-mails: [email protected], [email protected], [email protected]
Fotios Dimeas*
Affiliation:
Automation & Robotics Lab, Department of Electrical & Computer Engineering, Aristotle University of Thessaloniki, Greece. E-mails: [email protected], [email protected], [email protected]
Theodora Kastritsi
Affiliation:
Automation & Robotics Lab, Department of Electrical & Computer Engineering, Aristotle University of Thessaloniki, Greece. E-mails: [email protected], [email protected], [email protected]
Zoe Doulgeri
Affiliation:
Automation & Robotics Lab, Department of Electrical & Computer Engineering, Aristotle University of Thessaloniki, Greece. E-mails: [email protected], [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

The progressive automation framework allows the seamless transition of a robot from kinesthetic guidance to autonomous operation mode during programming by demonstration of discrete motion tasks. This is achieved by the synergetic action of dynamic movement primitives (DMPs), virtual fixtures, and variable impedance control. The proposed DMPs encode the demonstrated trajectory and synchronize with the current demonstration from the user so that the reference generated motion follows the human’s demonstration. The proposed virtual fixtures assist the user in repeating the learned kinematic behavior but allow penetration so that the user can make modifications to the learned trajectory if needed. The tracking error in combination with the interaction forces and torques is used by a variable stiffness strategy to adjust the progressive automation level and transition the leading role between the human and the robot. An energy tank approach is utilized to apply the designed controller and to prove the passivity of the overall control method. An experimental evaluation of the proposed framework is presented for a pick and place task and results show that the transition to autonomous mode is achieved in few demonstrations.

Keywords

Physical human–robot interactionProgramming by demonstrationDynamic movement primitivesVirtual fixtures
Type
Articles
Information
Robotica , Volume 38 , Special Issue 10: Human–Robot Interaction (HRI) , October 2020 , pp. 1824 - 1841
Copyright
Copyright © Cambridge University Press 2019

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