State Estimation of ALV Integrated Navigation System Based on BP Neural Network

2008 ◽  
Author(s):  
Meiling Wang ◽  
Yongwei Fu
Keyword(s):  
Neural Network ◽  
State Estimation ◽  
Bp Neural Network ◽  
Navigation System ◽  
Integrated Navigation ◽  
Integrated Navigation System

Research on outdoor AGV precise navigation based on BDS/INS data fusion

2021 ◽  
pp. 1-12
Author(s):  
Yongwei Tang ◽  
Huijuan Hao ◽  
Jun Zhou ◽  
Yuexiang Lin ◽  
Zhenzhen Dong
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Navigation System ◽  
Integrated Navigation ◽  
Complex Environments ◽  
Assisted Navigation ◽  
Precise Control ◽  
Bee Colony ◽  
Navigation Algorithm ◽  
Navigation Error

AGV (Automated Guided Vehicle) technology has attracted increasing attention. Precise control of AGV position and attitude information in complex operating environment is a key part of smart factories. With outdoor AGV as a platform, this study uses BDS/INS combined navigation system combining Beidou positioning system and inertial navigation system and takes the velocity and position difference between BDS and INS as a model. An integrated navigation method is proposed to improve bee colony algorithm and optimize the BP neural network-assisted Kalman filtering to achieve accurate positioning. Moreover, the optimization of BP neural network navigation using INS navigation, network-assisted navigation and bee colony algorithm is simulated. Results demonstrate that the integrated navigation algorithm has effectiveness and feasibility, and can solve the problems of BDS misalignment and large INS navigation error in complex environments.

Using Artificial Neural Networks to Compensate for the Error in an Integrated Navigation System

2020 ◽  
pp. 4-26
Author(s):  
N. Al Bitar ◽  
A.I. Gavrilov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Navigation System ◽  
Satellite Navigation ◽  
Satellite System ◽  
Integrated Navigation ◽  
Satellite Navigation System ◽  
Integrated Navigation System ◽  
Artificial Neural

The paper presents a new method for improving the accuracy of an integrated navigation system in terms of coordinate and velocity when there is no signal received from the global navigation satellite system. We used artificial neural networks to simulate the error occurring in an integrated navigation system in the absence of the satellite navigation system signal. We propose a method for selecting the inputs for the artificial neural networks based on the mutual information (MI) criterion and lag-space estimation. The artificial neural network employed is a non-linear autoregressive neural network with external inputs. We estimated the efficiency of using our method to solve the problem of compensating for the error in an integrated navigation system in the absence of the satellite navigation system signal

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