Loosening machines occupy a special place for mechanization of earth-moving. Despite rapid development of new methods for soils excavation and specialized machines, the mechanical method of their destruction by rippers in the near future will remain the most effective in most operating conditions. This is explained by relative simplicity of rippers design, wide scope and versatility of their applications, high productivity and low cost per unit of work performed. Rippers play a special role while excavating rocky and frozen soils, and in the latter case, the issue of loosening is particularly relevant, since the area occupied by permafrost is 58% of our country, and together with seasonally frozen soils it covers almost 90% of its territory. One of the most rational ways to intensify the processes of loosening are high-frequency oscillations of the ripper working body in the sonic range using resonant magnetostrictive vibrators. A new approach to the design of a loosening process control system is needed, which covers a range of issues related to development of new principles and methods of automation. Only in this way it will be possible to significantly improve the technical and economic indicators of loosening machines, to avoid influence on them of significant fluctuations in quantitative and qualitative characteristics of soil. Analysis of loosening processes showed that earthworks in heavy soils using traditional methods and mechanisms of loosening are not sufficiently effective first of all due to insufficient use of energy potential of loosening machine. Therefore, one of the most important tasks that needs to be solved when introducing the methods of vibro-loosening of frozen soils is to increase energy efficiency of these processes. Since traditional methods for excavation of frozen soils require substantial energy costs of 5–6 kW per m3, the introduction of the proposed system makes it possible to reduce these costs by 15–20%.

The article describes the method of simple kinematic connections, which allows to organize at the matrix level the solution of problems of spherical trigonometry and angular orientation, which is reduced to the decomposition of the original matrix description of the problem into a system of simple matrices, the sequential solution of which individually or in combinations, allows to produce unambiguous results in the absence of restrictions in the original data. The mathematical apparatus of homogeneous transformations makes it possible to calculate the values of angular reversals in all intermediate joints, from the first to the n-1st, uniquely determining their required spatial orientation, as well as the spatial orientation of the end section of the switchgear.

This article is devoted to the research and development of methods for the automated detection of road surface defects in offline mode. The article discusses the problems encountered in the operation of an automated road scanner (ARS), as well as the modernization of the system to solve these problems using computer (machine) vision and a Field-Programmable Gate Array (FPGA). The work uses deep learning methods and analysis of various architectures of neural networks. About 100 terabytes were collected and tagged to train the neural network for recognizing road defects. It is worth noting that the task of recognizing defects in the roadway is one of the most difficult even for the human eye, since the contours merge with the defect. During the study, a board was developed to collect telemetric data from road scanner devices. To store the collected telemetry characteristics, a large data storage was developed with replication and synchronization functions.

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