Neural-network based AUV path planning in estuary environments
Shuai Li, Yi Peng Guo · 2012
For the path planning problem of autonomous underwater vehicles (AUVs) in 3-dimensional (3-D) estuary environments, traditional methods may encounter problems due to their high computational complexity. In this paper, we proposed a dynamic neural network to solve the AUV path planning problem. In the neural network, neurons get input from the environment, locally interact with the neighbors and update neural activities in real time. The AUV path is then generated according to the neural activity landscapes. Stability, computational complexity of the neural network, and optimality of the generated path are analyzed. AUV path planning in 3-D complex environments without currents, with constant currents, and with variable currents are studied through simulations, which demonstrate the effectiveness of this approach.