Kybernetika a robotika

Browse

Now showing 1 - 5 of 33

Inquiry-Based Linear Algebra Teaching and Learning in a Flipped Classroom Framework: A Case Study
(Taylor & Francis, 2024-07-17) Fredriksen, Helge; Rebenda, Josef; Rensaa, Ragnhild Johanne; Pettersen, Petter
Flipped Classroom (FC) approaches, which utilize video distribution via modern internet platforms, have recently gained interest as a pedagogical framework. Inquiry Based Mathematics Education (IBME) has proven to be a valid form of task design to motivate active learning and enhance classroom interactivity. This article presents a practical combination of introductory videos and inquiry-based class activities adoptable in a basic linear algebra course for stimulating students’ exploration of the underlying mathematics. Teachers’ and students’ work addressed in the article was realized in two case studies in engineering programs in Norway and the Czech Republic. The learning objective was to connect different interpretations of the matrix equation Ax=b, which is often perceived as challenging for engineering students. Feedback from classroom sessions, interviews, and questionnaires encourage further research and inspired us as teachers to closely examine the mathematics behind the task design.
Localizing multiple radiation sources actively with a particle filter
(Elsevier, 2025-02-01) Lázna, Tomáš; Žalud, Luděk
We discuss the localization of radiation sources whose number and other relevant parameters are not known in advance. The data collection is ensured by an autonomous mobile robot that performs a survey in a defined region of interest populated with static obstacles. The measurement trajectory is information-driven rather than pre-planned, and the localization exploits a regularized particle filter estimating the sources’ parameters continuously. Regarding the dynamic robot control, this switches between two modes, one attempting to minimize the Shannon entropy and the other aiming to reduce the variance of expected measurements in unexplored parts of the target area; both of the modes maintain safe clearance from the obstacles. The performance of the algorithms was tested in a simulation study based on real-world data acquired previously from three radiation sources exhibiting various activities. Our approach reduces the time necessary to explore the region and to find the sources by approximately 40 %; at present, however, the method is unable to reliably localize sources that have a relatively low intensity. In this context, additional research has been planned to increase the credibility and robustness of the procedure and to improve the robotic platform autonomy.
Diagnostics of Interturn Short Circuits in PMSMs With Online Fault Indicators Estimation
(IEEE, 2024-02-27) Zezula, Lukáš; Kozovský, Matúš; Blaha, Petr
This article presents novel model-based diagnostics of interturn short circuits in permanent magnet synchronous machines that enable estimating fault location and its severity, even during transients. The proposed method utilizes recursive parametric estimation and model comparison approaches cast in a decision-making framework to track motor parameters and fault indicators from a machine's discrete-time model. The discrete-time prototype is derived from an advanced motor model that reflects the stator winding arrangement in a motor's case. The fault detection is then performed by tracking the changes in the estimated probability density function of the electrical parameters, using the Kullback-Leibler divergence. The fault location is subsequently evaluated by performing a recursive comparison of the predefined fault models in the different phases, utilizing a growing-window approach. Ultimately, a parametric estimation algorithm applied to the fault current model allows identifying the fault severity. The diagnostic algorithm has been validated via laboratory experiments, and its capabilities are compared with other approaches enabling severity estimation.
Using an Automated Heterogeneous Robotic System for Radiation Surveys
(2020-06-29) Gábrlík, Petr; Lázna, Tomáš; Jílek, Tomáš; Sládek, Petr; Žalud, Luděk
During missions involving radiation exposure, unmanned robotic platforms may embody a valuable tool, especially thanks to their capability of replacing human operators in certain tasks to eliminate the health risks associated with such an environment. Moreover, rapid development of the technology allows us to increase the automation rate, making the human operator generally less important within the entire process. This article presents a multi-robotic system designed for highly automated radiation mapping and source localization. Our approach includes a three-phase procedure comprising sequential deployment of two diverse platforms, namely, an unmanned aircraft system (UAS) and an unmanned ground vehicle (UGV), to perform aerial photogrammetry, aerial radiation mapping, and terrestrial radiation mapping. The central idea is to produce a sparse dose rate map of the entire study site via the UAS and, subsequently, to perform detailed UGV-based mapping in limited radiation-contaminated regions. To accomplish these tasks, we designed numerous methods and data processing algorithms to facilitate, for example, digital elevation model (DEM)-based terrain following for the UAS, automatic selection of the regions of interest, obstacle map-based UGV trajectory planning, and source localization. The overall usability of the multi-robotic system was demonstrated by means of a one-day, authentic experiment, namely, a fictitious car accident including the loss of several radiation sources. The ability of the system to localize radiation hotspots and individual sources has been verified.
Bayesian Inference of Total Least-Squares With Known Precision
(IEEE, 2022-09-06) Friml, Dominik; Václavek, Pavel
This paper provides a Bayesian analysis of the total least-squares problem with independent Gaussian noise of known variance. It introduces a derivation of the likelihood density function, conjugate prior probability-density function, and the posterior probability-density function. All in the shape of the Bingham distribution, introducing an unrecognized connection between orthogonal least-squares methods and directional analysis. The resulting Bayesian inference expands on available methods with statistical results. A recursive statistical identification algorithm of errors-in-variables models is laid- out. An application of the introduced inference is presented using a simulation example, emulating part of the identification process of linear permanent magnet synchronous motor drive parameters. The paper represents a crucial step towards enabling Bayesian statistical methods for problems with errors in variables.

Browse

Recent Submissions