Kalman filtering of IMU sensor for robot balance control
Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2008.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2009
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| Online Access: | http://hdl.handle.net/1721.1/45791 |
| _version_ | 1811073558555656192 |
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| author | Angelosanto, Gina (Gina C.) |
| author2 | Wai K. Cheng and Andreas Hofmann. |
| author_facet | Wai K. Cheng and Andreas Hofmann. Angelosanto, Gina (Gina C.) |
| author_sort | Angelosanto, Gina (Gina C.) |
| collection | MIT |
| description | Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2008. |
| first_indexed | 2024-09-23T09:34:52Z |
| format | Thesis |
| id | mit-1721.1/45791 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T09:34:52Z |
| publishDate | 2009 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/457912019-04-10T18:14:30Z Kalman filtering of IMU sensor for robot balance control Kalman filtering of inertial measurement unit sensor for robot balance control Angelosanto, Gina (Gina C.) Wai K. Cheng and Andreas Hofmann. Massachusetts Institute of Technology. Dept. of Mechanical Engineering. Massachusetts Institute of Technology. Dept. of Mechanical Engineering. Mechanical Engineering. Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2008. Includes bibliographical references (leaves 41-42). This study explores the use of Kalman filtering of measurements from an inertial measurement unit (IMU) to provide information on the orientation of a robot for balance control. A test bed was created to characterize the random noise and errors inherent to orientation sensing in the MicroStrain 3DM-GX1 IMU for static cases as well as after experiencing an impact force. Balance simulations were performed to control the center of mass location of a robot modeled as an inverted pendulum. The controlled center of mass trajectories with state estimates generated from Kalman filtering were compared, where possible, to the CM trajectory based on unfiltered sensor measurements of the states. For the simple case of inverted pendulum control, it was determined that noise and error in the IMU are sufficiently small that Kalman filtering is not necessary when all states can be measured, but results in significant improvements in the RMS error of the actual and desired center of mass positions. by Gina Angelosanto. S.B. 2009-06-30T16:18:26Z 2009-06-30T16:18:26Z 2008 2008 Thesis http://hdl.handle.net/1721.1/45791 318906502 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 46 leaves application/pdf Massachusetts Institute of Technology |
| spellingShingle | Mechanical Engineering. Angelosanto, Gina (Gina C.) Kalman filtering of IMU sensor for robot balance control |
| title | Kalman filtering of IMU sensor for robot balance control |
| title_full | Kalman filtering of IMU sensor for robot balance control |
| title_fullStr | Kalman filtering of IMU sensor for robot balance control |
| title_full_unstemmed | Kalman filtering of IMU sensor for robot balance control |
| title_short | Kalman filtering of IMU sensor for robot balance control |
| title_sort | kalman filtering of imu sensor for robot balance control |
| topic | Mechanical Engineering. |
| url | http://hdl.handle.net/1721.1/45791 |
| work_keys_str_mv | AT angelosantoginaginac kalmanfilteringofimusensorforrobotbalancecontrol AT angelosantoginaginac kalmanfilteringofinertialmeasurementunitsensorforrobotbalancecontrol |