00354nas a2200133   4500000000100000000000100001008004100002260002000043100002000063700002100083700001600104700001400120245008600134       2023                            d  aSorrento, Italy1 aPiotr Biernacki1 aAdam Ziębiński1 aJia-Hao Syu1 aJerry Lin00aThe Calibration of Single Beam Distance Sensors based on Machine Learning Methods00481nas a2200181   4500000000100000000000100001008004100002260002000043100001800063700002000081700001900101700002000120700001900140700002100159700001700180700002500197245007700222       2023                            d  aSorrento, Italy1 aDariusz Marek1 aMarcin Paszkuta1 aJakub Szyguła1 aPiotr Biernacki1 aAdam Domański1 aMarta Szczygieł1 aMarcel Król1 aKonrad Wojciechowski00aGeneral Concepts in Swarm of Drones Control: Analysis and Implementation00534nas a2200205   4500000000100000000000100001008004100002260001200043100002100055700001900076700001700095700002000112700001800132700001900150700001800169700001400187700001700201700002000218245009000238       2021                            d  c06/20211 aAdam Ziębiński1 aDariusz Mrozek1 aRafał Cupek1 aDamian Grzechca1 aMarcin Fojcik1 aMarek Drewniak1 aErik Kyrkjebo1 aJerry Lin1 aKnut Ovsthus1 aPiotr Biernacki00aChallenges associated with sensors and data fusion for AGV-driven smart manufacturing00323nas a2200121   4500000000100000000000100001008004100002260002800043100002000071700002100091700002000112245006900132       2021                            d  c09/2021aRhodes, Greece1 aPiotr Biernacki1 aAdam Ziębiński1 aDamian Grzechca00aThe adaptive calibration method for single-beam distance sensors00525nas a2200181   4500000000100000000000100001008004100002260002600043100001900069700002000088700001800108700001900126700002000145700001900165700001800184700001700202245012400219       2022                            d  c12/2022aOsaka, Japan1 aJakub Szyguła1 aPiotr Biernacki1 aDariusz Marek1 aAdam Domański1 aŁukasz Sobczak1 aJarosław Flak1 aDariusz Caban1 aPiotr Pawlas00aAnalysis of web-based geo-visualization methods applied for Automated Guided Vehicle using Satellite Navigation Systems00330nas a2200121   4500000000100000000000100001008004100002260002600043100001800069700002000087700002000107245008100127       2022                            d  c12/2022aOsaka, Japan1 aOlena Pavliuk1 aTomasz Stęclik1 aPiotr Biernacki00aThe forecast of the AGV battery discharging via the machine learning methods00368nas a2200133   4500000000100000000000100001008004100002260002600043100001800069700002000087700001900107700001900126245008900145       2022                            d  c12/2022aOsaka, Japan1 aDariusz Marek1 aPiotr Biernacki1 aJakub Szyguła1 aAdam Domański00aGeneral concepts of a simulation method for automated guided vehicle in Industry 4.000337nas a2200121   4500000000100000000000100001008004100002100001600043700001400059700002000073700002100093245010100114       2024                            d1 aJia-Hao Syu1 aJerry Lin1 aPiotr Biernacki1 aAdam Ziębiński00aMachine learning-based calibration approaches for single-beam and multiple-beam distance sensors00448nas a2200169   4500000000100000000000100001008004100002100001800043700002000061700001900081700002000100700001900120700002100139700001700160700002500177245007600202       2024                            d1 aDariusz Marek1 aMarcin Paszkuta1 aJakub Szyguła1 aPiotr Biernacki1 aAdam Domański1 aMarta Szczygieł1 aMarcel Król1 aKonrad Wojciechowski00aSwarm of drones in a simulation environment - efficiency and adaptation00427nas a2200169   4500000000100000000000100001008004100002100001800043700002000061700001900081700001900100700002000119700002100139700001700160700002500177245005500202       2025                            d1 aDariusz Marek1 aPiotr Biernacki1 aJakub Szyguła1 aAdam Domański1 aMarcin Paszkuta1 aMarta Szczygieł1 aMarcel Król1 aKonrad Wojciechowski00aCollision avoidance mechanism for swarms of drones00283nas a2200097   4500000000100000000000100001008004100002100002100043700002000064245010100084       2025                            d1 aAdam Ziębiński1 aPiotr Biernacki00aHow Accurate Can 2D LiDAR Be? A Comparison of the Characteristics of Calibrated 2D LiDAR Systems00597nas a2200181   4500000000100000000000100001008004100002260001200043100001800055700002000073700002000093700002100113700002200134700001800156700003000174245008000204856013100284       2025                            d  c02/20251 aNur Kelesoglu1 aŁukasz Sobczak1 aPiotr Biernacki1 aJoanna Domańska1 aFrancisco Ferrão1 aShahid Mumtaz1 aFideline Tchuenbou-Magaia00aArtificial Intelligence for Electronic-based Systems in Safety Applications  uhttps://www.researchgate.net/publication/389204536_Artificial_Intelligence_for_Electronic-based_Systems_in_Safety_Applications01474nas a2200157   4500000000100000000000100001008004100002260000800043100002000051700002000071700002100091245008000112856004400192300001400236520106600250       2025                            d  bACM1 aŁukasz Sobczak1 aPiotr Biernacki1 aJoanna Domańska00aVisual Encoding Method for Semantic Mapping with Federated Learning Concept  uhttps://doi.org/10.1145/3704413.3765513  a428 - 4353 a<p>We present a visual encoding method for semantic mapping in indoor environments, designed to minimize redundancy in image data and support federated learning across a fleet of service robots. Our pipeline combines 2D LiDAR-based segmentation with RGB image filtering based on geometric orientation, distance, visibility, and uniqueness. The result is a compact set of representative visual samples suitable for downstream semantic tasks such as object recognition or language grounding. We evaluate our method in a Gazebo simulation using a TurtleBot platform and compare it against a naive odometry-based sampling strategy. Our approach achieves up to 57.5\% reduction in collected images while preserving scene coverage. Additionally, we demonstrate how multiple robots can collaboratively improve the visual map in a federated setup, reducing collection time and enabling model generalization across diverse environments. The proposed method offers an efficient and scalable solution for semantic mapping under bandwidth and computation constraints.</p>
00494nas a2200181   4500000000100000000000100001008004100002260002100043100001800064700001900082700002000101700002000121700001900141700002100160700001800181700002500199245008800224       2025                            d  aHong Kong, China1 aDariusz Marek1 aJakub Szyguła1 aPiotr Biernacki1 aMarcin Paszkuta1 aAdam Domański1 aMarta Szczygieł1 aMarcin Fojcik1 aKonrad Wojciechowski00aNeural Tuning of Parameters in a Collision Avoidance Mechanism for Swarms of Drones00351nas a2200109   4500000000100000000000100001008004100002100002000043700002100063245010200084856005500186       2025                            d1 aPiotr Biernacki1 aAdam Ziębiński00aThe method of reflection-based marker detection and identification to ensure accurate AGV docking  uhttps://www.nature.com/articles/s41598-025-25357-x