TY - GEN
T1 - TACTO-Selector
T2 - 2020 IEEE International Conference on Robotics and Automation, ICRA 2020
AU - Martin, Ana Elvira H.
AU - Dean-Leon, Emmanuel
AU - Cheng, Gordon
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - In a physical Human-Robot Interaction for industrial scenarios is paramount to guarantee the safety of the user while keeping the robot's performance. Hierarchical task approaches are not sufficient since they tend to sacrifice the low priority tasks in order to guarantee the consistency of the main task. To handle this problem, we enhance the standard hierarchical fusion by introducing a novel interactive task-reconfiguring approach (TACTO-Selector) that uses the information of the tactile interaction to adapt the dimension of the tasks, therefore guaranteeing the execution of the safety task while performing the other task as good as possible. In this work, we hierarchically combine a 6 DOF Position-Based Visual Servoing (PBVS) task with a reactive skin control. This approach was evaluated on a 6 DOF industrial robot showing an improvement of 36.37% on average in tracking error reduction compared with a standard approach.
AB - In a physical Human-Robot Interaction for industrial scenarios is paramount to guarantee the safety of the user while keeping the robot's performance. Hierarchical task approaches are not sufficient since they tend to sacrifice the low priority tasks in order to guarantee the consistency of the main task. To handle this problem, we enhance the standard hierarchical fusion by introducing a novel interactive task-reconfiguring approach (TACTO-Selector) that uses the information of the tactile interaction to adapt the dimension of the tasks, therefore guaranteeing the execution of the safety task while performing the other task as good as possible. In this work, we hierarchically combine a 6 DOF Position-Based Visual Servoing (PBVS) task with a reactive skin control. This approach was evaluated on a 6 DOF industrial robot showing an improvement of 36.37% on average in tracking error reduction compared with a standard approach.
UR - http://www.scopus.com/inward/record.url?scp=85092691569&partnerID=8YFLogxK
U2 - 10.1109/ICRA40945.2020.9196979
DO - 10.1109/ICRA40945.2020.9196979
M3 - Conference contribution
AN - SCOPUS:85092691569
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 9121
EP - 9127
BT - 2020 IEEE International Conference on Robotics and Automation, ICRA 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 31 May 2020 through 31 August 2020
ER -