TY - CHAP A1 - Dey, Thomas A1 - Elsen, Ingo A1 - Ferrein, Alexander A1 - Frauenrath, Tobias A1 - Reke, Michael A1 - Schiffer, Stefan ED - Makedon, Fillia T1 - CO2 Meter: a do-it-yourself carbon dioxide measuring device for the classroom T2 - PETRA 2021: The 14th PErvasive Technologies Related to Assistive Environments Conference N2 - In this paper we report on CO2 Meter, a do-it-yourself carbon dioxide measuring device for the classroom. Part of the current measures for dealing with the SARS-CoV-2 pandemic is proper ventilation in indoor settings. This is especially important in schools with students coming back to the classroom even with high incidents rates. Static ventilation patterns do not consider the individual situation for a particular class. Influencing factors like the type of activity, the physical structure or the room occupancy are not incorporated. Also, existing devices are rather expensive and often provide only limited information and only locally without any networking. This leaves the potential of analysing the situation across different settings untapped. Carbon dioxide level can be used as an indicator of air quality, in general, and of aerosol load in particular. Since, according to the latest findings, SARS-CoV-2 can be transmitted primarily in the form of aerosols, carbon dioxide may be used as a proxy for the risk of a virus infection. Hence, schools could improve the indoor air quality and potentially reduce the infection risk if they actually had measuring devices available in the classroom. Our device supports schools in ventilation and it allows for collecting data over the Internet to enable a detailed data analysis and model generation. First deployments in schools at different levels were received very positively. A pilot installation with a larger data collection and analysis is underway. KW - embedded hardware KW - sensor networks KW - information systems KW - education KW - do-it-yourself Y1 - 2021 SN - 9781450387927 U6 - http://dx.doi.org/10.1145/3453892.3462697 N1 - PETRA '21: The 14th PErvasive Technologies Related to Assistive Environments Conference Corfu Greece 29 June 2021- 2 July 2021 SP - 292 EP - 299 PB - Association for Computing Machinery CY - New York ER - TY - CHAP A1 - Eichenbaum, Julian A1 - Nikolovski, Gjorgji A1 - Mülhens, Leon A1 - Reke, Michael A1 - Ferrein, Alexander A1 - Scholl, Ingrid T1 - Towards a lifelong mapping approach using Lanelet 2 for autonomous open-pit mine operations T2 - 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE) N2 - Autonomous agents require rich environment models for fulfilling their missions. High-definition maps are a well-established map format which allows for representing semantic information besides the usual geometric information of the environment. These are, for instance, road shapes, road markings, traffic signs or barriers. The geometric resolution of HD maps can be as precise as of centimetre level. In this paper, we report on our approach of using HD maps as a map representation for autonomous load-haul-dump vehicles in open-pit mining operations. As the mine undergoes constant change, we also need to constantly update the map. Therefore, we follow a lifelong mapping approach for updating the HD maps based on camera-based object detection and GPS data. We show our mapping algorithm based on the Lanelet 2 map format and show our integration with the navigation stack of the Robot Operating System. We present experimental results on our lifelong mapping approach from a real open-pit mine. Y1 - 2023 SN - 979-8-3503-2069-5 (Online) SN - 979-8-3503-2070-1 (Print) U6 - http://dx.doi.org/10.1109/CASE56687.2023.10260526 N1 - 19th International Conference on Automation Science and Engineering (CASE), 26-30 August 2023, Auckland, New Zealand. PB - IEEE ER - TY - CHAP A1 - Viehmann, Tarik A1 - Limpert, Nicolas A1 - Hofmann, Till A1 - Henning, Mike A1 - Ferrein, Alexander A1 - Lakemeyer, Gerhard ED - Eguchi, Amy ED - Lau, Nuno ED - Paetzel-Prüsmann, Maike ED - Wanichanon, Thanapat T1 - Winning the RoboCup logistics league with visual servoing and centralized goal reasoning T2 - RoboCup 2022 N2 - The RoboCup Logistics League (RCLL) is a robotics competition in a production logistics scenario in the context of a Smart Factory. In the competition, a team of three robots needs to assemble products to fulfill various orders that are requested online during the game. This year, the Carologistics team was able to win the competition with a new approach to multi-agent coordination as well as significant changes to the robot’s perception unit and a pragmatic network setup using the cellular network instead of WiFi. In this paper, we describe the major components of our approach with a focus on the changes compared to the last physical competition in 2019. Y1 - 2023 SN - 978-3-031-28468-7 (Print) SN - 978-3-031-28469-4 (Online) U6 - http://dx.doi.org/https://doi.org/10.1007/978-3-031-28469-4_25 N1 - Robot World Cup, RoboCup 2022. 17. July 2023. Bangkok, Thailand. Part of the Lecture Notes in Computer Science book series (LNAI,volume 13561) SP - 300 EP - 312 PB - Springer CY - Cham ER - TY - CHAP A1 - Arndt, Tobias A1 - Conzen, Max A1 - Elsen, Ingo A1 - Ferrein, Alexander A1 - Galla, Oskar A1 - Köse, Hakan A1 - Schiffer, Stefan A1 - Tschesche, Matteo T1 - Anomaly detection in the metal-textile industry for the reduction of the cognitive load of quality control workers T2 - PETRA '23: Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments N2 - This paper presents an approach for reducing the cognitive load for humans working in quality control (QC) for production processes that adhere to the 6σ -methodology. While 100% QC requires every part to be inspected, this task can be reduced when a human-in-the-loop QC process gets supported by an anomaly detection system that only presents those parts for manual inspection that have a significant likelihood of being defective. This approach shows good results when applied to image-based QC for metal textile products. KW - Datasets KW - Neural networks KW - Anomaly detection KW - Quality control KW - Process optimization Y1 - 2023 SN - 9798400700699 U6 - http://dx.doi.org/10.1145/3594806.3596558 N1 - PETRA '23: Proceedings of the 16th International Conference on Pervasive Technologies Related to Assistive Environments, Corfu Greece, July 5 - 7, 2023. SP - 535 EP - 542 PB - ACM ER - TY - JOUR A1 - Ferrein, Alexander A1 - Niemüller, Tim A1 - Steinbauer, Gerald T1 - Team Zadeat 2010 : application for participation Y1 - 2010 ER -