DocumentCode :
1797705
Title :
A sliding parameter estimation method based on UKF for agricultural tracked robot
Author :
Jun Jiao ; Li Sun ; Wen Kong ; Youhua Zhang ; Yan Qiao ; Chenchen Yuan
Author_Institution :
Coll. of Inf. & Comput., Anhui Agric. Univ., Hefei, China
fYear :
2014
fDate :
15-17 Nov. 2014
Firstpage :
277
Lastpage :
282
Abstract :
As the sliding parameter of track is hard-to-measure when Agricultural Tracked Robot (ATR) is moving in complicated farmland environment, an estimation method for sliding parameters of ATR based on UKF is proposed. A kinematics equation and a measurement equation of ATR are deduced by kinematics principle, and then the precision position parameters of ATR is calculated. Sliding parameters which cannot be measured directly may be reconstructed through this estimation method. The simulation and experimental results suggest that the estimation system is able to provide reliable and high update rate sliding information, which can provide some theoretical guidance for studying the control accuracy of ATR at a high speed in complicated farmland.
Keywords :
Kalman filters; agriculture; mobile robots; nonlinear filters; parameter estimation; UKF; agricultural tracked robot; farmland; kinematics equation; kinematics principle; measurement equation; sliding parameter estimation method; unscented Kalman filters; Educational institutions; Equations; Estimation; Mathematical model; Robots; Tracking; Trajectory; Agricultural Tracked Robot(ATR); Sliding parameter; Unscented Kalman Filter(UKF);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems and Informatics (ICSAI), 2014 2nd International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4799-5457-5
Type :
conf
DOI :
10.1109/ICSAI.2014.7009299
Filename :
7009299
Link To Document :
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