scholarly journals Enhanced SLAM for Autonomous Mobile Robots using Unscented Kalman Filter and Neural Network

2015 ◽  
Vol 8 (20) ◽  
Author(s):  
Omid Panah ◽  
Amir Panah ◽  
Amin Panah ◽  
Samere Fallahpour
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Mobile Robots ◽  
Unscented Kalman Filter ◽  
Autonomous Mobile Robots

A Localization Algorithm for Autonomous Mobile Robots via a Fuzzy Tuned Extended Kalman Filter

2010 ◽  
Vol 24 (1-2) ◽  
pp. 179-206 ◽  
Author(s):  
Y. L. Ip ◽  
A. B. Rad ◽  
Y. K. Wong ◽  
Y. Liu ◽  
X. M. Ren
Keyword(s):  
Kalman Filter ◽  
Mobile Robots ◽  
Extended Kalman Filter ◽  
Autonomous Mobile Robots ◽  
Localization Algorithm

scholarly journals Slip-Aware Motion Estimation for Off-Road Mobile Robots via Multi-Innovation Unscented Kalman Filter

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 43482-43496
Author(s):  
Fangxu Liu ◽  
Xueyuan Li ◽  
Shihua Yuan ◽  
Wei Lan
Keyword(s):  
Kalman Filter ◽  
Motion Estimation ◽  
Mobile Robots ◽  
Unscented Kalman Filter

Prediction Model of Concrete Dam Deformation Based on Adaptive Unscented Kalman Filter and BP Neural Network

2014 ◽  
Vol 513-517 ◽  
pp. 4076-4079 ◽  
Author(s):  
Liang Hui Li ◽  
Sheng Jun Peng ◽  
Zhen Xiang Jiang ◽  
Bo Wen Wei
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Unscented Kalman Filter ◽  
Safety Monitoring ◽  
Hydraulic Engineering ◽  
Concrete Dam ◽  
Adaptive Unscented Kalman Filter ◽  
Dam Deformation

By using unscented kalman filter (UKF) theory and introducing adaptive factor into BP neural network, a new prediction model of concrete dam deformation was proposed. Example shows that this model can improve the convergence speed of BP neural network, and the calculation precision of this model meets engineering requirements. Meanwhile, this model can be applied in the safety monitoring of other hydraulic engineering structure.

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