Abstract
This paper introduces a novel extended Kalman filter (EKF) approach for the pose estimation of a cable driven parallel robot (CDPR). The filter fuses accelerometer, rate gyroscope, and winch encoder data through a dynamically-updated covariance on the forward kinematics pose estimation error. The filter is tested on experimental data collected by a six degree-of-freedom CDPR test bed. The results show that the EKF is capable of providing similar pose estimation accuracy compared to forward kinematics alone, but with much lower covariance on the estimation error (i.e., much greater confidence in the pose estimate).
Original language | English (US) |
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Title of host publication | Proceedings of the 2022 USCToMM Symposium on Mechanical Systems and Robotics |
Editors | Pierre Larochelle, J. Michael McCarthy |
Publisher | Springer Science and Business Media B.V. |
Pages | 65-75 |
Number of pages | 11 |
ISBN (Print) | 9783030998257 |
DOIs | |
State | Published - 2022 |
Event | 2nd USCToMM Symposium on Mechanical Systems and Robotics, USCToMM MSR 2022 - Rapid City, United States Duration: May 19 2022 → May 21 2022 |
Publication series
Name | Mechanisms and Machine Science |
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Volume | 118 MMS |
ISSN (Print) | 2211-0984 |
ISSN (Electronic) | 2211-0992 |
Conference
Conference | 2nd USCToMM Symposium on Mechanical Systems and Robotics, USCToMM MSR 2022 |
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Country/Territory | United States |
City | Rapid City |
Period | 5/19/22 → 5/21/22 |
Bibliographical note
Publisher Copyright:© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Keywords
- Cable driven parallel robots
- Extended Kalman filter
- Forward kinematics
- Pose estimation