Abstract
A fundamental problem is making the robot run blindly without additional sensors while still being robust to uncertainties and variations in the assembly processes.
In this paper, we address the use of parameterized motions suitable for blind execution and robust to uncertainties in the assembly process.
Collisions and incorrect assemblies are detected based on robot motor currents while motion parameters are updated based on Bayesian Optimization utilizing Gaussian Process learning.
This allows for motion parameters to be optimized using real world trials which incorporate all uncertainties inherent in the assembly process without requiring advanced robot and sensor setups.
The result is a simple and straightforward system which helps the user automatically find robust and uncertainty-tolerant motions.
We present experiments for an assembly case showing both detection and learning in the real world and how these combine to a robust robot system.
| Original language | English |
|---|---|
| Title of host publication | Proceeding of the 2018 IEEE International Conference on Robotics and Automation |
| Publisher | IEEE |
| Publication date | 13. Sep 2018 |
| Pages | 7587-7594 |
| ISBN (Print) | 978-1-5386-3082-2 |
| ISBN (Electronic) | 978-1-5386-3081-5 |
| DOIs | |
| Publication status | Published - 13. Sep 2018 |
| Event | 2018 IEEE International Conference on Robotics and Automation - The Brisbane Convention & Exhibition Centre, Brisbane, Australia Duration: 21. May 2018 → 25. May 2018 https://icra2018.org/ |
Conference
| Conference | 2018 IEEE International Conference on Robotics and Automation |
|---|---|
| Location | The Brisbane Convention & Exhibition Centre |
| Country | Australia |
| City | Brisbane |
| Period | 21/05/2018 → 25/05/2018 |
| Internet address |
Cite this
}
Adapting Parameterized Motions using Iterative Learning and Online Collision Detection. / Laursen, Johan Sund; Sørensen, Lars Carøe; Schultz, Ulrik Pagh; Ellekilde, Lars-Peter; Kraft, Dirk.
Proceeding of the 2018 IEEE International Conference on Robotics and Automation. IEEE, 2018. p. 7587-7594.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
TY - GEN
T1 - Adapting Parameterized Motions using Iterative Learning and Online Collision Detection
AU - Laursen, Johan Sund
AU - Sørensen, Lars Carøe
AU - Schultz, Ulrik Pagh
AU - Ellekilde, Lars-Peter
AU - Kraft, Dirk
PY - 2018/9/13
Y1 - 2018/9/13
N2 - Achieving both the flexibility and robustness required to advance the use of robotics in small and medium-sized productions is an essential but difficult task. A fundamental problem is making the robot run blindly without additional sensors while still being robust to uncertainties and variations in the assembly processes.In this paper, we address the use of parameterized motions suitable for blind execution and robust to uncertainties in the assembly process. Collisions and incorrect assemblies are detected based on robot motor currents while motion parameters are updated based on Bayesian Optimization utilizing Gaussian Process learning. This allows for motion parameters to be optimized using real world trials which incorporate all uncertainties inherent in the assembly process without requiring advanced robot and sensor setups. The result is a simple and straightforward system which helps the user automatically find robust and uncertainty-tolerant motions. We present experiments for an assembly case showing both detection and learning in the real world and how these combine to a robust robot system.
AB - Achieving both the flexibility and robustness required to advance the use of robotics in small and medium-sized productions is an essential but difficult task. A fundamental problem is making the robot run blindly without additional sensors while still being robust to uncertainties and variations in the assembly processes.In this paper, we address the use of parameterized motions suitable for blind execution and robust to uncertainties in the assembly process. Collisions and incorrect assemblies are detected based on robot motor currents while motion parameters are updated based on Bayesian Optimization utilizing Gaussian Process learning. This allows for motion parameters to be optimized using real world trials which incorporate all uncertainties inherent in the assembly process without requiring advanced robot and sensor setups. The result is a simple and straightforward system which helps the user automatically find robust and uncertainty-tolerant motions. We present experiments for an assembly case showing both detection and learning in the real world and how these combine to a robust robot system.
U2 - 10.1109/ICRA.2018.8463208
DO - 10.1109/ICRA.2018.8463208
M3 - Article in proceedings
SN - 978-1-5386-3082-2
SP - 7587
EP - 7594
BT - Proceeding of the 2018 IEEE International Conference on Robotics and Automation
PB - IEEE
ER -