Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Fedor Chervinskii

dblp:266/3209 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
1since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Motion planning and robot control · 54% Robot manipulation · 46%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation
assembly
0.712023
Auto-Assembly: a framework for automated robotic assembly directly from CAD · ICRA 2023
Robotics › Robot manipulation › assembly
automated assembly
0.712023
Auto-Assembly: a framework for automated robotic assembly directly from CAD · ICRA 2023
Robotics › Motion planning and robot control
motion planning
0.712023
Auto-Assembly: a framework for automated robotic assembly directly from CAD · ICRA 2023
Robotics › Motion planning and robot control
path planning
0.712023
Auto-Assembly: a framework for automated robotic assembly directly from CAD · ICRA 2023
Robotics › Motion planning and robot control › assembly planning
assembly sequence generation
0.212023
Auto-Assembly: a framework for automated robotic assembly directly from CAD · ICRA 2023

Methods — techniques the papers use, named apart from their topics

simulation · 0.7CAD-based planning · 0.7
YearPublicationVenuePosition
2023 Auto-Assembly: a framework for automated robotic assembly directly from CAD
abstract
In this work, we propose a framework called Auto-Assembly for automated robotic assembly from design files and demonstrate a practical implementation on modular parts joined by fastening using a robotic cell consisting of two robots. We show the flexibility of the approach by testing it on different input designs. Auto-Assembly consists of several parts: design analysis, assembly sequence generation, bill-of-process (BOP) generation, conversion of the BOP to control code, path planning, simulation, and execution of the control code to assemble parts in the physical environment.
Fedor Chervinskii, Sergei Zobov, Aleksandr Rybnikov, Danil Petrov, Komal Vendidandi
ICRA1