TY - GEN
T1 - Online estimation of geometric and inertia parameters for multirotor aerial vehicles
AU - Wuest, Valentin
AU - Kumar, Vijay
AU - Loianno, Giuseppe
N1 - Funding Information:
This work was supported by Qualcomm Research and the ARL grant DCIST CRA W911NF-17-2-0181. 1 The authors are with the GRASP Lab, University of Pennsylvania, 3330 Walnut Street, Philadelphia, PA 19104, USA. email: {vwueest, kumar}@seas.upenn.edu. 2 The author is with the New York University, Tandon School of Engineering, Brooklyn, NY 11201, USA. email: {loiannog}@nyu.edu
Funding Information:
This work was supported by Qualcomm Research and the ARL grant DCIST CRA W911NF-17-2-0181.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
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.
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U2 - 10.1109/ICRA.2019.8794274
DO - 10.1109/ICRA.2019.8794274
M3 - Conference contribution
AN - SCOPUS:85071452455
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.
T2 - 2019 International Conference on Robotics and Automation, ICRA 2019
Y2 - 20 May 2019 through 24 May 2019
ER -