Oceanpribor (Russia)

This article explores the application of Reinforcement Learning (RL) for developing tactics for using Inertial Navigation Systems (INS) in the event of information failures. The study focuses on an unmanned vehicle traveling from point A to point B, equipped with a navigation complex comprising three INS units. The proposed solution involves dynamically adjusting the trust coefficients for INS output data using the Proximal Policy Optimization (PPO) algorithm. Simulation results demonstrate a significant reduction in positioning errors, with radial errors reduced by 2–25 times across various test scenarios. The results highlight the potential of RL for enhancing the reliability of autonomous navigation systems, particularly in marine robotics, where such applications remain underexplored.

Natural compounds possess significant potential for pharmacological applications. This study investigates melanin derived from birch heartwood polymerized with the mycelium of fungus Fomes fomentarius and its fruiting body, for its potential geroprotective properties. The cytotoxicity and proliferative effects of the studied compound, Amelan, were evaluated using the immortalized human foreskin fibroblasts (BJ-5ta) and human breast adenocarcinoma (MDA-MB-231) cell lines through the MTT assay and cell counting with a Goryaev chamber. To assess the geroprotective properties of the compound, an accelerated aging model was induced with D-galactose in primary human dermal fibroblast cultures. Senescent cell populations were identified through cytochemical staining for senescence-associated β-galactosidase (SA-β-gal). Amelan exhibited a dose-dependent geroprotective effect, significantly reducing senescent cell populations at optimal concentrations. Importantly, the compound did not stimulate proliferation in either immortalized BJ-5ta fibroblasts or MDA-MB-231 cancer cells, suggesting no oncogenic activation potential. In light of the demonstrated geroprotective effects of Amelan, its concentration-dependent efficacy underscores the importance of dosage optimization to maximize therapeutic benefits while minimizing adverse effects. Further research is needed to evaluate its potential as a candidate for anti-aging therapy.

The organometallic polymer UiO-66 was synthesized and characterized. Samples of the resulting material after drying at two different temperatures (100 and 300°C) were studied using various analytical methods. IR absorption spectra of the synthesized samples were recorded in the range from 500 to 3500 cm–1, and the main absorption bands of the synthesized polymer UiO-66 were confirmed. The crystal lattice parameters of the samples for two temperatures were determined to be 20.10(5) Å at 100°C and 20.52(4) Å at 300°C, with the characteristic sizes of coherent scattering regions (CSR) of X-ray radiation being ~44 Å at 100°C and ~37 Å at 300°C. The specific surface area was calculated using the BET method, and the specific surface area of the sample after drying at 100°C was estimated to be S ~ 363 m2/g, while the characteristic size of microparticles was ~18 μm. As a result of the synthesis, a single-phase material with a type of X-ray diffraction pattern characteristic of UiO-66 was obtained. It has been shown that drying at 300°C structures the resulting material in the [111] direction, while other directions become amorphized.

The paper presents an overview of neural network (NN) technologies applied in navigation support of marine surface vehicles. It considers the problems related to motion path generation, detection, classification, and avoiding collisions with approaching ships, as well as navigation data integration and cybersecurity issues.

The article describes the methodology and provides the results of a model quantitative assessment of the effectiveness of solving the problem of detecting and tracking a low-noise underwater object using three algorithms for spatial signal processing at the output of a multi-element antenna — the non-adaptive Bartlett algorithm, the Capon algorithm, and the Capon algorithm combined with a projection procedure limiting the signal power of strong local sources.

High-precision docking with a landing port is one of the main tasks of an autonomous underwater vehicle. Repeated testing of this task due to virtualization of the docking process with the casting port allows you to debug the control algorithms. The control algorithms receive a lot of data from various sensors as input. One of these sensors can be a deep-sea television camera interfaced with a computing module that, based on the results of video stream analysis, produces coordinate data on the position of an autonomous underwater vehicle relative to the landing port. The article discusses an approach to debugging control algorithms for an autonomous underwater vehicle during high-precision guidance to the port using data from deep-sea television cameras. The simulation results are presented in the case where the landing port is equipped with markers (QR codes), and the autonomous underwater vehicle is equipped with two deep-sea television cameras and lighting devices.

Concern CSRI Elektropribor, JSC, has accumulated extensive experience in the development and application of electrostatic suspensions (ESS) of sensitive masses of various inertial sensors—gyroscopes and accelerometers. The article presents the schemes that form the basis of these devices. An attempt is made to generalize the principles of construction and search for unified approaches to the creation of inertial sensors using ESS.

The paper presents the analytical review of an inertial measuring unit (IMU) rotation as a method to improve the accuracy of a strapdown inertial navigation system (SINS). There are two ways to improve the accuracy. One of them is based on the transformation of the error change pattern in the inertial sensors (IS) when using the IMU self-compensation rotation (SCR). The criteria for selecting an efficient SCR law to minimize the accumulated error in the parameters generated by SINS are presented. Along with the advantages of this technology, its weak points that may limit significantly the potentially achievable IMU accuracy are described. The other technique consists in increasing the observability of the IS error model components due to the IMU rotation while filtering the SINS errors. The IS error model is described, and the problem of recursive filtering of the SINS errors is stated to refine these errors, with the reference data on coordinates and motion velocity being available. The methods for quantifying the observability of the IS error model components are presented.

Detection of bottom objects is one of the key tasks of an autonomous underwater vehicle. Neural networks are being increasingly used for solving these problems. Application of neural networks to object recognition is always associated with the problem of training sample generation. The lack of training material is particularly relevant in processing the hydroacoustic and optical sea bottom survey data. The paper describes an approach consisting in generating the simulated data. The generation of a training sample is based on three-dimensional modeling of the bottom surface and objects located on it, and the use of hydroacoustic calculations for each of the bottom monitoring tools. The paper describes the transfer of training for a neural network and presents the results of checking its operation on a test sample of real data.

Monitoring of water pollutions of different offshore zones is one of the applications of autonomous underwater vehicles. Conducted researches and ecological design and survey works showed the efficiency of using hydrobionts as sensing elements for quick finding of water pollution. The article is devoted to the analysis of the possibility of application of hydrobionts for equipping of autonomous underwater vehicles with specified sensors.