Fast Contact Detection and Classification for Kinesthetic Teaching in Robots using only Embedded Sensors
(2022) 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) p.1138-1145- Abstract
- Collaborative robots have been designed to perform tasks where human cooperation may occur. Additionally, undesired collisions can happen in the robot’s environment. A contact classifier may be needed if robot trajectory recalculation is to be activated depending on the source of robot–environment contact. For this reason, we have evaluated a fast contact detection and classification method and we propose necessary modifications and extensions so that it is able to detect a contact in any direction and distinguish if it has been caused by voluntary human cooperation or by accidental collision with a static obstacle for kinesthetic teaching applications. Robot compliance control is used for trajectory following as an active strategy to... (More)
- Collaborative robots have been designed to perform tasks where human cooperation may occur. Additionally, undesired collisions can happen in the robot’s environment. A contact classifier may be needed if robot trajectory recalculation is to be activated depending on the source of robot–environment contact. For this reason, we have evaluated a fast contact detection and classification method and we propose necessary modifications and extensions so that it is able to detect a contact in any direction and distinguish if it has been caused by voluntary human cooperation or by accidental collision with a static obstacle for kinesthetic teaching applications. Robot compliance control is used for trajectory following as an active strategy to ensure safety of the robot and its environment. Only sensor data that are conventionally available in commercial collaborative robots, such as joint-torque sensors and joint-position encoders/resolvers, are used in our method. Moreover, fast contact detection is ensured by using the frequency content of the estimated external forces, whereas external force direction and sense relative to the robot’s motion is used to classify its source. Our method has been experimentally proven to be successful in a collaborative assembly task for a number of different experimentally recorded trajectories and with the intervention of different operators. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/8ef803eb-edf6-4e6d-b899-400ba9fbafe4
- author
- Salt Ducaju, Julian LU ; Olofsson, Björn LU ; Robertsson, Anders LU and Johansson, Rolf LU
- organization
- publishing date
- 2022-08
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Robotics, Contact Detection, Kinesthetic Teaching
- host publication
- Proc. 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) Aug 29 - Sep 2, 2022
- pages
- 8 pages
- conference name
- 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)
- conference location
- Naples, Italy
- conference dates
- 2022-08-29 - 2022-09-02
- external identifiers
-
- scopus:85140731049
- ISBN
- 978-172818859-1
- DOI
- 10.1109/RO-MAN53752.2022.9900800
- project
- Human-Robot Collaboration for Kinesthetic Teaching
- RobotLab LTH
- ELLIIT LU P06: Collaborative Robotic Systems
- WASP: Wallenberg AI, Autonomous Systems and Software Program at Lund University
- language
- English
- LU publication?
- yes
- id
- 8ef803eb-edf6-4e6d-b899-400ba9fbafe4
- date added to LUP
- 2022-09-02 20:00:12
- date last changed
- 2024-03-08 14:32:40
@inproceedings{8ef803eb-edf6-4e6d-b899-400ba9fbafe4, abstract = {{Collaborative robots have been designed to perform tasks where human cooperation may occur. Additionally, undesired collisions can happen in the robot’s environment. A contact classifier may be needed if robot trajectory recalculation is to be activated depending on the source of robot–environment contact. For this reason, we have evaluated a fast contact detection and classification method and we propose necessary modifications and extensions so that it is able to detect a contact in any direction and distinguish if it has been caused by voluntary human cooperation or by accidental collision with a static obstacle for kinesthetic teaching applications. Robot compliance control is used for trajectory following as an active strategy to ensure safety of the robot and its environment. Only sensor data that are conventionally available in commercial collaborative robots, such as joint-torque sensors and joint-position encoders/resolvers, are used in our method. Moreover, fast contact detection is ensured by using the frequency content of the estimated external forces, whereas external force direction and sense relative to the robot’s motion is used to classify its source. Our method has been experimentally proven to be successful in a collaborative assembly task for a number of different experimentally recorded trajectories and with the intervention of different operators.}}, author = {{Salt Ducaju, Julian and Olofsson, Björn and Robertsson, Anders and Johansson, Rolf}}, booktitle = {{Proc. 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) Aug 29 - Sep 2, 2022}}, isbn = {{978-172818859-1}}, keywords = {{Robotics; Contact Detection; Kinesthetic Teaching}}, language = {{eng}}, pages = {{1138--1145}}, title = {{Fast Contact Detection and Classification for Kinesthetic Teaching in Robots using only Embedded Sensors}}, url = {{https://lup.lub.lu.se/search/files/160718393/ROMAN2022_reviewed_2_.pdf}}, doi = {{10.1109/RO-MAN53752.2022.9900800}}, year = {{2022}}, }