Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2019 International Conference on Robotics and Automation, ICRA 2019 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 966-972 |
| Seitenumfang | 7 |
| ISBN (elektronisch) | 978-1-5386-6027-0 |
| ISBN (Print) | 978-1-5386-8176-3 |
| Publikationsstatus | Veröffentlicht - Mai 2019 |
| Veranstaltung | 2019 International Conference on Robotics and Automation, ICRA 2019 - Montreal, Kanada Dauer: 20 Mai 2019 → 24 Mai 2019 |
Publikationsreihe
| Name | Proceedings - IEEE International Conference on Robotics and Automation |
|---|---|
| ISSN (Print) | 1050-4729 |
Abstract
This paper presents a holistic approach for door opening with a cartesian impedance controlled mobile robot, a KUICA KMR iiwa. Based on a given map of the environment, the robot autonomously detects the door handle, opens doors and traverses doorways without knowledge of a door model or the door's geometry. The door handle detection uses a convolutional neural network (CNN)-based architecture to obtain the handle's bounding box in a RGB image that works robustly for various handle shapes and colors. We achieve a detection rate of 100% for an evaluation set of 38 different door handles, by always selecting for highest confidence score. Registered depth data segmentation defines the door plane to construct a handle coordinate frame. We introduce a control structure based on the task frame formalism that uses the handle frame for reference in an outer loop for the manipulator's impedance controller. It runs in soft real-time on an external computer with approximately 20 Hz since access to inner controller loops is not available for the KMR iiwa. With the approach proposed in this paper, the robot successfully opened and traversed for 22 out of 25 trials at five different doors.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Informatik (insg.)
- Artificial intelligence
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
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- BibTex
- RIS
2019 International Conference on Robotics and Automation, ICRA 2019. Institute of Electrical and Electronics Engineers Inc., 2019. S. 966-972 8793866 (Proceedings - IEEE International Conference on Robotics and Automation).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Door opening and traversal with an industrial cartesian impedance controlled mobile robot
AU - Stüde, Marvin
AU - Nülle, Kathrin
AU - Tappe, Svenja
AU - Ortmaier, Tobias
PY - 2019/5
Y1 - 2019/5
N2 - This paper presents a holistic approach for door opening with a cartesian impedance controlled mobile robot, a KUICA KMR iiwa. Based on a given map of the environment, the robot autonomously detects the door handle, opens doors and traverses doorways without knowledge of a door model or the door's geometry. The door handle detection uses a convolutional neural network (CNN)-based architecture to obtain the handle's bounding box in a RGB image that works robustly for various handle shapes and colors. We achieve a detection rate of 100% for an evaluation set of 38 different door handles, by always selecting for highest confidence score. Registered depth data segmentation defines the door plane to construct a handle coordinate frame. We introduce a control structure based on the task frame formalism that uses the handle frame for reference in an outer loop for the manipulator's impedance controller. It runs in soft real-time on an external computer with approximately 20 Hz since access to inner controller loops is not available for the KMR iiwa. With the approach proposed in this paper, the robot successfully opened and traversed for 22 out of 25 trials at five different doors.
AB - This paper presents a holistic approach for door opening with a cartesian impedance controlled mobile robot, a KUICA KMR iiwa. Based on a given map of the environment, the robot autonomously detects the door handle, opens doors and traverses doorways without knowledge of a door model or the door's geometry. The door handle detection uses a convolutional neural network (CNN)-based architecture to obtain the handle's bounding box in a RGB image that works robustly for various handle shapes and colors. We achieve a detection rate of 100% for an evaluation set of 38 different door handles, by always selecting for highest confidence score. Registered depth data segmentation defines the door plane to construct a handle coordinate frame. We introduce a control structure based on the task frame formalism that uses the handle frame for reference in an outer loop for the manipulator's impedance controller. It runs in soft real-time on an external computer with approximately 20 Hz since access to inner controller loops is not available for the KMR iiwa. With the approach proposed in this paper, the robot successfully opened and traversed for 22 out of 25 trials at five different doors.
UR - http://www.scopus.com/inward/record.url?scp=85071443533&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2019.8793866
DO - 10.1109/ICRA.2019.8793866
M3 - Conference contribution
AN - SCOPUS:85071443533
SN - 978-1-5386-8176-3
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 966
EP - 972
BT - 2019 International Conference on Robotics and Automation, ICRA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Robotics and Automation, ICRA 2019
Y2 - 20 May 2019 through 24 May 2019
ER -