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Production of the Future: AI Research Centre Presents Its Developments in Manual Operations Control Systems

Production of the Future: AI Research Centre Presents Its Developments in Manual Operations Control Systems

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Researchers from the HSE AI Research Centre have built a system for the automated control of manual operations, which finds application in industrial production. The system facilitates the process of monitoring objects and actions, as well as controlling the quality of their execution.

Artificial intelligence technologies help automate human actions, simplifying their work, or completely replacing personnel. The automation of manual operations control in production goes even further: from selective quality control of products by the Quality Control Department to continuous monitoring of the entire assembly process.

Based on AI technologies, HSE scientists have developed a demonstration stand—one of the key components for testing developments before their implementation in production. Using computer vision, the automated system analyses the sequence of actions of the assembler, whether it is a robot or a human. This allows it to be determined if an important step was missed, or if an assembly action was incorrect. The system also evaluates compliance with safety techniques: the usage of personal protective equipment and absence of third persons or unrelated objects in the assembly area. With all actions logged in the system, individual performance indicators for each employee can be obtained. For the assembler, the system is useful because it alerts them if they forgot to perform a step or did it incorrectly. Ultimately, it significantly reduces the output percentage of defective products.

Viktor Minchenkov, project leader, Deputy Head of the Unit for Software Systems Development at HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM HSE)

‘At the moment, we can track the sequences of semi knocked down and screw driving assembly. We can track the quantity and quality of an item placement on the workbench. We can track safety violations related to the use of personal protective equipment. In the final implementation, we can adapt the stand for controlling any technological process if it can be implemented through means of visual control.’

The project ‘Intelligent Automation of Manual Operations in Production’ of the AI Research Centre is being carried out by specialists from HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM HSE) within the framework of the federal project ‘Artificial Intelligence.’

The main advantage of this developed system is that AI allows many actions to be performed equally well and even better than humans. One particular difference is that computer vision does not suffer from ‘fatigue’ as does human vision.

Sergey Slastnikov, Associate Professor at HSE School of Applied Mathematics of HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM HSE)

‘Analogues of the developed system undoubtedly exist—there are quite a few local solutions for highly specialised tasks. In turn, we are developing our own approach, which potentially can be applied to a wider range of problems. As part of its testing, we are in collaboration with various domestic companies, both industrial and technological, for example, in the field of video analytics and IT services development. Several pilot projects have already been launched.’

The range of applications for this development is very wide. For example, the technology can be used as simulators for training with automatic assessment of the level of training or in video surveillance and video analytics systems, where malicious actions of humans can also be controlled.

Anton Sergeev, Director of the Centre for Software Development and Digital Services at HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM HSE)

‘Our professional engineering and mathematical school and a project-based training model at MIEM allowed us to quickly put together a young but qualified team and implement computer vision technology for production. The created system is like an experienced digital mentor: it watches, advises, teaches, points out mistakes, and impartially and fairly evaluates efficiency. Data on process efficiency is automatically sent to the enterprise's ERP system.’

An important parameter of these systems is the speed of data collection (video or photos) for training embedded AI algorithms. This stage is also automated in the system and reduces the time and cost of implementation. The details of the approach were presented by the project team in the paper ‘Method of Automatic Images Datasets Sampling for the Manual Operations Control Systems’ in 2023.

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