Autonomous underwater vehicles (AUVs) are underwater robots which are able to perform certain tasks without the help of a human operator. The key skill of each AUV is the capability to avoid collisions. To this end, appropriate devices and software are necessary with the potential to detect obstacles and to take proper decisions from the point of view of both the task and safety of the vehicle. The paper presents a neural collision avoidance system (NCAS) designed for the biomimetic autonomous underwater vehicle (BAUV). The NCAS is a component of the path following and collision avoidance system (PFCAS), which as the name implies is responsible for safely leading the vehicle along a desired path with collision avoidance. The task of NCAS is to make decisions regarding vehicle maneuvers in the horizontal plane, but only in the close proximity of the obstacles. It is implemented as an evolutionary artificial neural network designed by means of a neuro-evolutionary technique called assembler encoding with evolvable operations (AEEO). The paper outlines operation and construction of the BAUV as well as the PFCAS, the role of the NCAS in the entire system, and briefly presents AEEO as well as reporting on the experiments performed in simulation.

The information about ship spatial orientation is required by different ship systems. For example, dynamic positioning, artillery, or video tracking systems, to stabilize a ship, a gun or a camera, have to work in advance, and therefore, they need the information about future ship orientation. To obtain this information, a current ship orientation and sometimes also historical orientations are necessary. Another example is ship echo-sounders applied to hydro-graphic surveys. Since each list of the ship affects echo-sounder depth measurement, to determine true depth of the sea based on the echo-sounders, the spatial orientation of the ship at the moment of each measurement has to be known. One method for determining the ship spatial orientation is an optical system including video cameras, a computer and specialized software. The system works through extracting the horizon line from each video shot and calculating an angle between the line and the horizontal border of the shot. The main disadvantage of this approach is its slowness. There are a number of methods which can be used to extract the horizon line from an image; however, they are so computationally complex that their application to high-resolution images is limited rather to only post-processing. In the paper, a quick method for horizon line detection is proposed. The experiments on real marine images showed that it is at least as accurate as other methods, what is more, it is also a number of times faster and it seems to be more reliable than its rivals.