TY - GEN
T1 - Detection of Collaboration and Collision Events during Contact Task Execution
AU - Franzel, Felix
AU - Eiband, Thomas
AU - Lee, Dongheui
N1 - Publisher Copyright:
©2021 IEEE.
PY - 2021
Y1 - 2021
N2 - This work introduces a contact event pipeline to distinguish task-contact from Human-Robot interaction and collision during task execution. The increasing need for close proximity physical human-robot interaction (pHRI) in the private, health and industrial sector demands for new safety solutions. One of the most important issues regarding safe collaboration is the robust recognition and classification of contacts between human and robot. A solution is designed, that enables simple task teaching and accurate contact monitoring during task execution. Besides an external force and torque sensor, only proprioceptive data is used for the contact evaluation. An approach based on demonstrated task knowledge and the offset resulting from human interaction is designed to distinguish contact events from normal execution by a contact event detector. A contact type classifier implemented as Support Vector Machine is trained with the identified events. The system is set up to quickly identify contact incidents and enable appropriate robot reactions. An offline evaluation is conducted with data recorded from intended and unintended contacts as well as examples of task-contacts like object manipulation and environmental interactions. The system's performance and its high responsiveness are evaluated in different experiments including a real world task.
AB - This work introduces a contact event pipeline to distinguish task-contact from Human-Robot interaction and collision during task execution. The increasing need for close proximity physical human-robot interaction (pHRI) in the private, health and industrial sector demands for new safety solutions. One of the most important issues regarding safe collaboration is the robust recognition and classification of contacts between human and robot. A solution is designed, that enables simple task teaching and accurate contact monitoring during task execution. Besides an external force and torque sensor, only proprioceptive data is used for the contact evaluation. An approach based on demonstrated task knowledge and the offset resulting from human interaction is designed to distinguish contact events from normal execution by a contact event detector. A contact type classifier implemented as Support Vector Machine is trained with the identified events. The system is set up to quickly identify contact incidents and enable appropriate robot reactions. An offline evaluation is conducted with data recorded from intended and unintended contacts as well as examples of task-contacts like object manipulation and environmental interactions. The system's performance and its high responsiveness are evaluated in different experiments including a real world task.
UR - http://www.scopus.com/inward/record.url?scp=85126009156&partnerID=8YFLogxK
U2 - 10.1109/HUMANOIDS47582.2021.9555677
DO - 10.1109/HUMANOIDS47582.2021.9555677
M3 - Conference contribution
AN - SCOPUS:85126009156
T3 - IEEE-RAS International Conference on Humanoid Robots
SP - 376
EP - 383
BT - 2020 10th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2020
A2 - Asfour, Tamim
A2 - Lee, Dongheui
A2 - Katja, Mombaur
A2 - Yamane, Katsu
A2 - Harada, Kensuke
A2 - Righetti, Ludovic
A2 - Tsagarakis, Nikos
A2 - Sugihara, Tomomichi
PB - IEEE Computer Society
T2 - 20th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2020
Y2 - 19 July 2021 through 21 July 2021
ER -