Open AccessOptimal Estimation Using Deep Neural Networks Applied to Navigation and Motion Control
Author(s) -
O.S. Amosov,
S.G. Amosova
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1864/1/012012
Subject(s) - computer science , artificial neural network , trajectory , artificial intelligence , recurrent neural network , relation (database) , motion (physics) , estimation , convolutional neural network , deep learning , machine learning , data mining , engineering , physics , systems engineering , astronomy
The critical analysis is given concerning the current state of using deep neural networks with convolutional and recurrent layers, a recurrent network of Long Short-Term Memory, Gated Recurrent Units for estimation tasks in relation to navigation and motion control. A comparison of neural network and traditional methods is given for understanding and explaining their functioning. The differences, advantages and disadvantages of deep neural networks in relation to solving estimation problems are revealed. The possibility of machine training with reinforcement is analyzed for estimation tasks in navigation and motion control in real time. The prospects of using neural networks in the processing of navigation data, as well as for the tasks of adaptive estimation and trajectory tracking, are formulated.