Abstract
This letter proposes a model predictive control approach for semi-autonomous teleoperation of robot manipulators: the focus is on avoiding obstacles with the whole robot frame, while exploiting predictions of the operator's motion. The hand pose of the human operator provides the reference for the end effector, and the robot motion is continuously replanned in real time, satisfying several constraints. An experimental case study is described regarding the design and testing of the described framework on a UR5 manipulator: the experimental results confirm the suitability of the proposed method for semi-autonomous teleoperation, both in terms of performance (tracking capability and constraint satisfaction) and computational complexity (the control law is calculated well within the sampling interval).
| Original language | English |
|---|---|
| Article number | 8718327 |
| Pages (from-to) | 2746-2753 |
| Number of pages | 8 |
| Journal | IEEE Robotics and Automation Letters |
| Volume | 4 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - Jul 1 2019 |
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Keywords
- industrial robots
- motion and path planning
- Optimization and optimal control
ASJC Scopus subject areas
- Control and Systems Engineering
- Biomedical Engineering
- Human-Computer Interaction
- Mechanical Engineering
- Computer Vision and Pattern Recognition
- Computer Science Applications
- Control and Optimization
- Artificial Intelligence
Cite this
Semi-Autonomous Robot Teleoperation with Obstacle Avoidance via Model Predictive Control. / Rubagotti, Matteo; Taunyazov, Tasbolat; Omarali, Bukeikhan; Shintemirov, Almas.
In: IEEE Robotics and Automation Letters, Vol. 4, No. 3, 8718327, 01.07.2019, p. 2746-2753.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Semi-Autonomous Robot Teleoperation with Obstacle Avoidance via Model Predictive Control
AU - Rubagotti, Matteo
AU - Taunyazov, Tasbolat
AU - Omarali, Bukeikhan
AU - Shintemirov, Almas
PY - 2019/7/1
Y1 - 2019/7/1
N2 - This letter proposes a model predictive control approach for semi-autonomous teleoperation of robot manipulators: the focus is on avoiding obstacles with the whole robot frame, while exploiting predictions of the operator's motion. The hand pose of the human operator provides the reference for the end effector, and the robot motion is continuously replanned in real time, satisfying several constraints. An experimental case study is described regarding the design and testing of the described framework on a UR5 manipulator: the experimental results confirm the suitability of the proposed method for semi-autonomous teleoperation, both in terms of performance (tracking capability and constraint satisfaction) and computational complexity (the control law is calculated well within the sampling interval).
AB - This letter proposes a model predictive control approach for semi-autonomous teleoperation of robot manipulators: the focus is on avoiding obstacles with the whole robot frame, while exploiting predictions of the operator's motion. The hand pose of the human operator provides the reference for the end effector, and the robot motion is continuously replanned in real time, satisfying several constraints. An experimental case study is described regarding the design and testing of the described framework on a UR5 manipulator: the experimental results confirm the suitability of the proposed method for semi-autonomous teleoperation, both in terms of performance (tracking capability and constraint satisfaction) and computational complexity (the control law is calculated well within the sampling interval).
KW - industrial robots
KW - motion and path planning
KW - Optimization and optimal control
UR - http://www.scopus.com/inward/record.url?scp=85066948615&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066948615&partnerID=8YFLogxK
U2 - 10.1109/LRA.2019.2917707
DO - 10.1109/LRA.2019.2917707
M3 - Article
VL - 4
SP - 2746
EP - 2753
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
SN - 2377-3766
IS - 3
M1 - 8718327
ER -