Abstract
Accurate knowledge of geometric and inertia parameters are a necessity for precise and robust control of aerial vehicles. We propose a novel filter that is able to fuse motor speed, inertia, and pose measurements to estimate the vehicle's key dynamic properties online. The presented framework is able to estimate the multirotor's moment of inertia, mass, center of mass and each sensor module's relative position. Obtaining these estimates in-flight allow the multirotor to be precisely controlled even during tasks such as load transportation or after configuration changes on scene. We provide a nonlinear observability analysis, proving that the presented model is locally weakly observable. Experimental results validate the proposed approach, showing the ability to estimate the dynamic properties accurately and demonstrate its capability to do so even while additional loads are added. The framework is flexible and can easily be adapted to a wide range of applications, including self-calibration, object grasping, and single robot or multi-robot payload transportation.
| Original language | English (US) |
|---|---|
| Title of host publication | 2019 International Conference on Robotics and Automation, ICRA 2019 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1884-1890 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781538660263 |
| DOIs | |
| State | Published - May 1 2019 |
| Event | 2019 International Conference on Robotics and Automation, ICRA 2019 - Montreal, Canada Duration: May 20 2019 → May 24 2019 |
Publication series
| Name | Proceedings - IEEE International Conference on Robotics and Automation |
|---|---|
| Volume | 2019-May |
| ISSN (Print) | 1050-4729 |
Conference
| Conference | 2019 International Conference on Robotics and Automation, ICRA 2019 |
|---|---|
| Country | Canada |
| City | Montreal |
| Period | 5/20/19 → 5/24/19 |
Fingerprint
ASJC Scopus subject areas
- Software
- Control and Systems Engineering
- Artificial Intelligence
- Electrical and Electronic Engineering
Cite this
Online estimation of geometric and inertia parameters for multirotor aerial vehicles. / Wuest, Valentin; Kumar, Vijay; Loianno, Giuseppe.
2019 International Conference on Robotics and Automation, ICRA 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1884-1890 8794274 (Proceedings - IEEE International Conference on Robotics and Automation; Vol. 2019-May).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Online estimation of geometric and inertia parameters for multirotor aerial vehicles
AU - Wuest, Valentin
AU - Kumar, Vijay
AU - Loianno, Giuseppe
PY - 2019/5/1
Y1 - 2019/5/1
N2 - Accurate knowledge of geometric and inertia parameters are a necessity for precise and robust control of aerial vehicles. We propose a novel filter that is able to fuse motor speed, inertia, and pose measurements to estimate the vehicle's key dynamic properties online. The presented framework is able to estimate the multirotor's moment of inertia, mass, center of mass and each sensor module's relative position. Obtaining these estimates in-flight allow the multirotor to be precisely controlled even during tasks such as load transportation or after configuration changes on scene. We provide a nonlinear observability analysis, proving that the presented model is locally weakly observable. Experimental results validate the proposed approach, showing the ability to estimate the dynamic properties accurately and demonstrate its capability to do so even while additional loads are added. The framework is flexible and can easily be adapted to a wide range of applications, including self-calibration, object grasping, and single robot or multi-robot payload transportation.
AB - Accurate knowledge of geometric and inertia parameters are a necessity for precise and robust control of aerial vehicles. We propose a novel filter that is able to fuse motor speed, inertia, and pose measurements to estimate the vehicle's key dynamic properties online. The presented framework is able to estimate the multirotor's moment of inertia, mass, center of mass and each sensor module's relative position. Obtaining these estimates in-flight allow the multirotor to be precisely controlled even during tasks such as load transportation or after configuration changes on scene. We provide a nonlinear observability analysis, proving that the presented model is locally weakly observable. Experimental results validate the proposed approach, showing the ability to estimate the dynamic properties accurately and demonstrate its capability to do so even while additional loads are added. The framework is flexible and can easily be adapted to a wide range of applications, including self-calibration, object grasping, and single robot or multi-robot payload transportation.
UR - http://www.scopus.com/inward/record.url?scp=85071452455&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071452455&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2019.8794274
DO - 10.1109/ICRA.2019.8794274
M3 - Conference contribution
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 1884
EP - 1890
BT - 2019 International Conference on Robotics and Automation, ICRA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
ER -