Neural network techniques for navigation of AUVs

A neural net approach is considered as a nonlinear controller for precise navigation and positioning of an autonomous underwater vehicle (AUV) around and about fixed and/or moving objects. The network can be trained to operate within various noise conditions consisting of current fields or other constraints. A neural net uses sensor position and velocity information as the inputs and relative position and motion vectors for the propulsion/steering unit as the output. The effectiveness of backpropagation and evolutionary programming methods for training networks with single and multiple hidden layers are investigated. Results based on simulated data sources and capabilities are presented. The experiments discussed indicate the practicality of implementing neural networks using backpropagation or evolutionary programming for online optimal navigation