Estimation of load side position in indirect drive robots by sensor fusion and kalman filtering

Author

Wenjie Chen ; Tomizuka, M.

Author_Institution

Dept. of Mech. Eng., Univ. of California at Berkeley, Berkeley, CA, USA

fYear

2010

fDate

June 30 2010-July 2 2010

Firstpage

6852

Lastpage

6857

Abstract

In indirect drive robot joint, discrepancies exist between the motor side and the load side due to joint flexibilities. Thus, sensor signals may not precisely represent the actual information of interest. In this paper, estimation algorithms for load side information of the indirect robot joint are investigated. Low-cost MEMS sensors, such as gyroscopes and accelerometers, are installed on the load side. Measurement dynamics are incorporated into the model to deal with the sensor noise and bias. Kalman filtering methods are designed based on the extended dynamic/kinematic model using the fusion of multiple sensor signals. Specific issue related to the noise covariance adaptation is studied. The effectiveness of the proposed schemes is experimentally demonstrated and also confirmed in the friction compensation.

Keywords

Kalman filters; accelerometers; estimation theory; gyroscopes; micromechanical devices; robot dynamics; robot kinematics; sensor fusion; Kalman filtering; MEMS sensor; accelerometer; dynamic model; friction compensation; gyroscopes; indirect drive robot joint; kinematic model; load side position estimation; motor side; noise covariance; sensor fusion; Accelerometers; Design methodology; Drives; Filtering; Gyroscopes; Kalman filters; Micromechanical devices; Noise measurement; Robot sensing systems; Sensor fusion;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference (ACC), 2010

Conference_Location

Baltimore, MD

ISSN

0743-1619

Print_ISBN

978-1-4244-7426-4

Type

conf

DOI

10.1109/ACC.2010.5531572

Filename

5531572