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http://dx.doi.org/10.5392/JKCA.2020.20.06.099

Real-Time Acquisition Method of Posture Information of Arm with MEMS Sensor and Extended Kalman Filter  

Choi, Wonseok (LIG넥스원)
Kim, HeeSu (LIG넥스원)
Kim, Jaehyun (LIG넥스원)
Cho, Youngki (LIG넥스원)
Publication Information
The Journal of the Korea Contents Association / v.20, no.6, 2020 , pp. 99-113 More about this Journal
Abstract
In the future, robots and drones for the convenience of our lives in everyday life will increase. As a method for controlling this, a remote control or a human voice method is most commonly used. However, the remote control needs to be operated by a person and can not ignore ambient noise in the case of voice. In this paper, we propose an economical attitude information acquisition method to accurately acquire the posture information of the arm in real time under the assumption that the surround drones or robots can be controlled wirelessly with the posture information of the arm. For this purpose, the extended Kalman filter was used to eliminate the noise of the arm position information. in order to detect the arm movement, a low cost MEMS type sensor was applied to secure the economical efficiency of the apparatus. To increase the wear ability of the arm, We developed a compact and lightweight attitude information acquisition system by integrating all functions into one chip as much as possible. As a result, the real-time performance of 1 ms was secured and the extended Kalman filter was applied to acquire the accurate attitude information of the arm with noise removed and display the attitude information of the arm in real time. This provides a basis for generating commands using real-time attitude information of the arm.
Keywords
MEMS Sensor; Extended Kalman Filter; Embedded System; FPGA; Sensor Fusion; Arm Position Information;
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