Applying of artificial intelligence techniques to designing of an autopilot for path following control of an underwater robotic vehicle is considered in the paper. The waypoint line of sight scheme is incorporated for the tracking of the reference path and four independent fuzzy controllers are used to generate command signals. Parameters of membership functions of input and output are tuned using genetic algorithms. Quality of control is concerned without and in presence of external disturbances. Some computer simulations are provided to demonstrate the effectiveness and robustness of the approach.

Real-time seabed tracking applications play an important role in underwater systems. A lot of them use computer vision for servoing, positioning, navigation, odometry and simultaneous localisation and mapping. They are mostly based on local image features, therefore feature detection, description and matching are crucial for their efficient operations. The aim of this study was to investigate the most popular feature detection and description algorithms such as SIFT, SURF, FAST, STAR, HAR-RIS, ORB, BRISK and FREAK. Additionally, the image correction technique was presented and image enhancement methods were analysed in order to increase efficiency of image features matching. The matching algorithm was based on the homography matrix and random sample consensus technique. Our results indicate that the combination of the histogram equalisation technique and ORB detector and descriptor enables real-time seabed tracking with sufficient efficiency. ARTICLE HISTORY

In this paper the attempt to make an analysis of distance measurement using a stereo vision system was presented. Main emphasis was placed on the geometric camera calibration. The classical method based on the specially prepared calibration pattern with known dimensions and position in a certain coordinates system was performed. Finally, the metric information obtained from images was presented.

The inspection-class Remotely Operated Vehicles (ROVs) are crucial in underwater inspections. Their prime function is to allow the replacing of humans during risky subaquatic operations. These vehicles gather videos from underwater scenes that are sent online to a human operator who provides control. Furthermore, these videos are used for analysis. This demands an RGB camera operating at a close distance to the observed objects. Thus, to obtain a detailed depiction, the vehicle should move with a constant speed and a measured distance from the bottom. As very few inspection-class ROVs possess navigation systems that facilitate these requirements, this study had the objective of designing a vision-based control method to compensate for this limitation. To this end, a stereo vision system and image-feature matching and tracking techniques were employed. As these tasks are challenging in the underwater environment, we carried out analyses aimed at finding fast and reliable image-processing techniques. The analyses, through a sequence of experiments designed to test effectiveness, were carried out in a swimming pool using a VideoRay Pro 4 vehicle. The results indicate that the method under consideration enables automatic control of the vehicle, given that the image features are present in stereo-pair images as well as in consecutive frames captured by the left camera.

This paper attempts to develop a segmentation algorithm applicable to the issue of recognizing objects in video images. The paper presents the steps of the algorithm with a discussion of techniques used in mathematical morphology, filtration and gradient methods. Also there were presented examples of the results of a verification researches.