Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Fedoseev, Aleksey"'
Autor:
Sautenkov, Oleg, Asfaw, Selamawit, Yaqoot, Yasheerah, Mustafa, Muhammad Ahsan, Fedoseev, Aleksey, Trinitatova, Daria, Tsetserukou, Dzmitry
The swift advancement of unmanned aerial vehicle (UAV) technologies necessitates new standards for developing human-drone interaction (HDI) interfaces. Most interfaces for HDI, especially first-person view (FPV) goggles, limit the operator's ability
Externí odkaz:
http://arxiv.org/abs/2410.16943
In the area of multi-drone systems, navigating through dynamic environments from start to goal while providing collision-free trajectory and efficient path planning is a significant challenge. To solve this problem, we propose a novel SwarmPath techn
Externí odkaz:
http://arxiv.org/abs/2410.07848
Autor:
Davletshin, Denis, Zhura, Iana, Cheremnykh, Vladislav, Rybiyanov, Mikhail, Fedoseev, Aleksey, Tsetserukou, Dzmitry
The paper focuses on the algorithm for improving the quality of 3D reconstruction and segmentation in DSP-SLAM by enhancing the RGB image quality. SharpSLAM algorithm developed by us aims to decrease the influence of high dynamic motion on visual obj
Externí odkaz:
http://arxiv.org/abs/2410.05405
TiltXter: CNN-based Electro-tactile Rendering of Tilt Angle for Telemanipulation of Pasteur Pipettes
Autor:
Cabrera, Miguel Altamirano, Tirado, Jonathan, Fedoseev, Aleksey, Sautenkov, Oleg, Poliakov, Vladimir, Kopanev, Pavel, Tsetserukou, Dzmitry
The shape of deformable objects can change drastically during grasping by robotic grippers, causing an ambiguous perception of their alignment and hence resulting in errors in robot positioning and telemanipulation. Rendering clear tactile patterns i
Externí odkaz:
http://arxiv.org/abs/2409.15838
Autor:
Lykov, Artem, Cabrera, Miguel Altamirano, Konenkov, Mikhail, Serpiva, Valerii, Gbagbe, Koffivi Fid`ele, Alabbas, Ali, Fedoseev, Aleksey, Moreno, Luis, Khan, Muhammad Haris, Guo, Ziang, Tsetserukou, Dzmitry
This paper presents the concept of Industry 6.0, introducing the world's first fully automated production system that autonomously handles the entire product design and manufacturing process based on user-provided natural language descriptions. By le
Externí odkaz:
http://arxiv.org/abs/2409.10106
Autor:
Satsevich, Sergei, Savotin, Yaroslav, Belov, Danil, Pestova, Elizaveta, Erhov, Artem, Khabibullin, Batyr, Bazhenov, Artem, Kovalev, Vyacheslav, Fedoseev, Aleksey, Tsetserukou, Dzmitry
This paper introduces a system of data collection acceleration and real-to-sim transferring for surface recognition on a quadruped robot. The system features a mechanical single-leg setup capable of stepping on various easily interchangeable surfaces
Externí odkaz:
http://arxiv.org/abs/2407.15622
Autor:
Serpiva, Valerii, Fedoseev, Aleksey, Karaf, Sausar, Abdulkarim, Ali Alridha, Tsetserukou, Dzmitry
This paper presents the OmniRace approach to controlling a racing drone with 6-degree of freedom (DoF) hand pose estimation and gesture recognition. To our knowledge, it is the first-ever technology that allows for low-level control of high-speed dro
Externí odkaz:
http://arxiv.org/abs/2407.09841
Autor:
Peter, Robinroy, Ratnabala, Lavanya, Aschu, Demetros, Fedoseev, Aleksey, Tsetserukou, Dzmitry
Autonomous drone navigation faces a critical challenge in achieving accurate landings on dynamic platforms, especially under unpredictable conditions such as wind turbulence. Our research introduces TornadoDrone, a novel Deep Reinforcement Learning (
Externí odkaz:
http://arxiv.org/abs/2406.16164
Achieving safe and precise landings for a swarm of drones poses a significant challenge, primarily attributed to conventional control and planning methods. This paper presents the implementation of multi-agent deep reinforcement learning (MADRL) tech
Externí odkaz:
http://arxiv.org/abs/2406.04159
Autor:
Lykov, Artem, Karaf, Sausar, Martynov, Mikhail, Serpiva, Valerii, Fedoseev, Aleksey, Konenkov, Mikhail, Tsetserukou, Dzmitry
This article presents the world's first rapid drone flocking control using natural language through generative AI. The described approach enables the intuitive orchestration of a flock of any size to achieve the desired geometry. The key feature of t
Externí odkaz:
http://arxiv.org/abs/2405.05872