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May 25, 2026
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Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
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Applying MAPP Algorithm for Cooperative Path Finding in Urban Environments

P. 1–10.
Andreychuk A., Yakovlev K.

The paper considers the problem of planning a set of non-conflict trajectories for the coalition of intelligent agents (mobile robots). Two divergent approaches, e.g. centralized and decentralized, are surveyed and analyzed. Decentralized planner – MAPP is described and applied to the task of finding trajectories for dozens UAVs performing nap-of-the-earth flight in urban environments. Results of the experimental studies provide an opportunity to claim that MAPP is a highly efficient planner for solving considered types of tasks.

Language: English
DOI
Text on another site
Keywords: path planningheuristic searchPath findingmulti-agent path findingMAPP

In book

Interactive Collaborative Robotics: Second International Conference, ICR 2017, Hatfield, UK, September 12-16, 2017, Proceedings
Springer, 2017.
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