Bathymetric simultaneous localisation and mapping (SLAM) methods yield accurate navigation results for autonomous underwater vehicles (AUVs) and can construct consistent seabed terrain maps. Multiple independently working vehicles can complete tasks like surveying and mapping efficiently, which means cooperative bathymetric SLAM using multiple AUVs is suitable for large-scale seabed mapping. However, the transmission of bathymetric measurements collected using a multi-beam echo sounder over a low bandwidth, noisy, and unreliable acoustic channel is difficult, making cooperative bathymetric SLAM very challenging. This paper develops a graph-based cooperative bathymetric SLAM system that can compress many bathymetric measurements into small-scale acoustic packets and yield accurate navigation results with a 10% loss of acoustic packets caused by unreliable acoustic communication. According to the simulation conducted using the field data, the new algorithm is shown to be robust and capable of providing accurate location and mapping results over a low bandwidth, noisy, and unreliable acoustic channel.

A biologically inspired neurodynamics-based tracking controller of underactuated Autonomous Underwater Vehicles (AUV) is proposed in this paper. The proposed control strategy includes a velocity controller with biological neurons and an adaptive sliding mode controller. The biological neurons are embedded into the backstepping velocity controller to eliminate the sharp speed jumps commonly existing in vehicles due to tracking errors changing suddenly. The outputs of the velocity controller are used as the command inputs of the sliding mode controller, and the thruster control constraints problems that are commonly seen in the backstepping control of AUV are solved by the proposed controller. Simulation results show that the control strategy achieved success in smoothly tracking AUV position and velocity.