Evgeny A. Semenishchev

About

Evgeny A. Semenishchev is a researcher specializing in machine vision, robotics, and intelligent manufacturing systems, with a particular focus on human-robot collaboration, autonomous navigation, and image processing for industrial applications. His work sits at the intersection of computer vision and robotics, addressing practical challenges in smart manufacturing environments. Among his most significant contributions is his development of the 3-D Binary Micro-block Difference method for action recognition, his most-cited work with 35 citations, which enables robots to interpret human actions in real-time manufacturing settings. He has also made notable advances in SLAM-based robot navigation through depth image reconstruction and multimodal image inpainting, both addressing the persistent challenge of missing depth data in robotic perception systems. His research into automated thermal and visible-spectrum image analysis for numerically controlled machining further demonstrates his commitment to bridging sensing technologies with industrial automation. Semenishchev has additionally explored hardware acceleration of neural networks using FPGAs, reflecting a deep interest in deploying AI efficiently at the edge. With over 90 cumulative citations across his key publications since 2019, his work is increasingly recognized as valuable to researchers and engineers advancing collaborative robotics, autonomous systems, and AI-driven quality control in modern industrial settings.

Research Focus

Key Achievements

Top Papers

1. 1
   Action recognition for the robotics and manufacturing automation using 3-D binary micro-block difference

   35 citations · 2021
2. 2
   Algorithm for the automated determination of heating areas during metal processing with numerically controlled machines

   13 citations · 2021
3. 3
   Human activity recognition for efficient human-robot collaboration

   12 citations · 2020
4. 4
   Robot navigation using modified SLAM procedure based on depth image reconstruction

   10 citations · 2021
5. 5
   Multimodal image inpainting for an autonomous robot navigation application

   10 citations · 2021
6. 6
   Fusing Data Processing in the Construction of Machine Vision Systems in Robotic Complexes

   9 citations · 2019
7. 7
   Using neuro-accelerators on FPGAs in collaborative robotics tasks

   5 citations · 2021
8. 8
   Algorithm for the automated analysis of the shape of objects located on the cutting table of industrial robotic systems

   4 citations · 2021
9. 9
   Action Recognition for the Robotics and Manufacturing Automation Using 3-D Binary Micro-block Difference

   4 citations · 2021
10. 10
    Deep learning-based depth map defect removal for industrial applications

    3 citations · 2023

Key Collaborators

Contact & Links