Dr. Péter Galambos
Óbuda University, Budapest, Hungary
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20Years (2007-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications17 September 2020 in IEEE Transactions on Systems, Man, and Cybernetics: Systems
Journal Article
AKArtúr István Károly
Artúr István KárolyPéter GalambosJKJozsef Kuti
Jozsef Kuti
Imre RudasThe ever-increasing complexity of robot applications induces the need for methods to approach problems with no (viable) analytical solution. Deep learning (DL) provides a set of tools to address this kind of problems. This survey presents a categorization of the major challenges in robotics that leverage DL technologies and introduces representative examples of successful solutions for the described problems. We also consider the question when and whether to use modular, monolithic models or end-to-end DL, in order to provide a guideline for the selection of the correct model structure and training strategy. By doing so, the current role and adaptability of different techniques at different hierarchical levels of a robot-application can be highlighted, thus providing a well-structured basis to assist future approaches.
1 June 2015 in Robotics and Computer-Integrated Manufacturing
Journal Article
Péter GalambosÁCÁdám Csapó
Ádám Csapó
PZPéter Zentay
Péter Zentay
IFIstván Marcell Fülöp
István Marcell Fülöp
Tamás HaideggerIRImre J. Rudas
Imre J. Rudas
+1
15 January 2020 in IEEE Systems, Man, and Cybernetics Magazine
Journal Article
Péter GalambosAs we are witnessing the dawn of the artificial intelligence revolution, it is a good time to discuss the system-architecture challenges of the new age of robotics. Most of the disputes are related to the wide variety of cloud-computing arrangements along the questions of why, with what, and how to exploit existing information technologies to cope effectively with the increasing demands. Traditional industrial robot makers are very conservative, relying exclusively on onboard execution. However, the rising user demand for advanced robot skills, such as dexterous manipulation and free navigation in an unstructured environment, has inspired newcomers to break with tradition and move robot software architecture to the next level. Inspired by the talks of Prof. Imre Rudas on cloud robotics, this article highlights some substantial factors of system design and discusses a real-world industrial example from my R&D practice.
31 August 2016 in Asian Journal of Control
Journal Article
7 January 2015 in Asian Journal of Control
Journal Article
Péter GalambosPBPéter Baranyi
Péter Baranyi
12 July 2022 in IEEE Transactions on Cybernetics
Journal Article
Advanced robotics and autonomous vehicles rely on filtering and sensor fusion techniques to a large extent. These mobile applications need to handle the computations onboard at high rates while the computing capacities are limited. Therefore, any improvement that lowers the CPU time of the filtering leads to more accurate control or longer battery operation. This article introduces a generic computational relaxation for the unscented transformation (UT) that is the key operation of the Unscented Kalman filter-based applications. The central idea behind the relaxation is to pull out the linear part of the filtering model and avoid the calculations for the kernel of the nonlinear part. The practical merit of the proposed relaxation is demonstrated through a simultaneous localization and mapping (SLAM) implementation that underpins the superior performance of the algorithm in the practically relevant cases, where the nonlinear dependencies influence only an affine subspace of the image space. The numerical examples show that the computational demand can be mitigated below 50% without decreasing the accuracy of the approximation. The method described in this article is implemented and published as an open-source C++ library RelaxedUnscentedTransformation on GitHub.
About Me
Prof. Dr. Péter Galambos received his M.Sc. and Ph.D.
degrees in mechanical engineering from Budapest University of Technology
and Economics (BME) in 2006 and 2013, respectively. He was a Research Intern
with Toshiba Corporate Research and Development Center, Kawasaki, Japan, from
2007 to 2008, then joined the Institute for Computer Science and Control,
Hungarian Academy of Sciences (MTA-SZTAKI), Budapest, where he held a “Young Researcher”
Scholarship from 2010 to 2012. From 2011 to 2015, he served as a Team Leader
with MTA SZTAKI and coordinated the development of the VirCA VR system and its
research applications. He joined Obuda University in 2013, where he
participated in robotics-related R&D activities and education. He is
currently a full professor and director of the Antal Bejczy Center for
Intelligent Robotics at Obuda University, Budapest. Starting in July 2024, he
has been acting as the vice-rector for innovation. His current research
interests include Advanced Industrial Robotics and Control Systems,
Cyber–Physical Systems, and Virtual Reality. He is the author and co-author of
more than 160 scientific papers and has received more than 2400 citations. In
addition to his career in academia, he is a successful entrepreneur,
co-founder, and CTO of the MAXWHERE 3D Digital Twin software platform.