Intelligent Collaborative Navigation and Control for AUV Tracking

In order to maintain the submarine equipment, autonomous underwater vehicle (AUV) is usually assigned to track the submarine cables or pipes. The capabilities of navigation and control are critical to track the target accurately. Ultra-short baseline (USBL) is essential equipment for AUV, which uses sound waves for positioning. Unfortunately, due to the low frequency of USBL, it inevitably limits the frequency of control and ultimately affects the tracking effect. In order to improve the aforementioned issue and achieve better tracking tasks, intelligent collaborative navigation and control (CNaC) was herein proposed in this article. First, we proposed nonlinear state reconstruction neural network navigation, which used the neural networks to reconstruct the state between two adjacent USBL valid values online. Combined with the valid USBL and reconstructed states, the online process model generated by neural networks are applied to give the estimate position for AUV. At last, intelligent CNaC use the estimated position and valid USBL as inputs to control AUV to achieve tracking tasks. This strategy makes the control frequency free from the limitation of the USBL frequency. The proposed intelligent CNaC is demonstrated by simulation and real experiments. Compared to mechanically combining the traditional navigation and control algorithm, the tracking accuracy of intelligent CNaC improves by 81.96%.