TY - CHAP A1 - Ulmer, Jessica A1 - Braun, Sebastian A1 - Cheng, Chi-Tsun A1 - Dowey, Steve A1 - Wollert, Jörg T1 - Adapting augmented reality systems to the users’ needs using gamification and error solving methods T2 - Procedia CIRP - 54th CIRP CMS 2021 - Towards Digitalized Manufacturing 4.0 N2 - Animations of virtual items in AR support systems are typically predefined and lack interactions with dynamic physical environments. AR applications rarely consider users’ preferences and do not provide customized spontaneous support under unknown situations. This research focuses on developing adaptive, error-tolerant AR systems based on directed acyclic graphs and error resolving strategies. Using this approach, users will have more freedom of choice during AR supported work, which leads to more efficient workflows. Error correction methods based on CAD models and predefined process data create individual support possibilities. The framework is implemented in the Industry 4.0 model factory at FH Aachen. KW - Augmented Reality KW - Adaptive Systems KW - Gamification KW - Error Recovery Y1 - 2021 U6 - https://doi.org/10.1016/j.procir.2021.11.024 SN - 2212-8271 N1 - CIRP CMS 2021 - 54th CIRP Conference on Manufacturing Systems, September 22-24, 2021, online VL - 104 SP - 140 EP - 145 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Braun, Sebastian A1 - Cheng, Chi-Tsun A1 - Dowey, Steve A1 - Wollert, Jörg T1 - Performance evaluation of skill-based order-assignment in production environments with multi-agent systems JF - IEEE Journal of Emerging and Selected Topics in Industrial Electronics N2 - The fourth industrial revolution introduces disruptive technologies to production environments. One of these technologies are multi-agent systems (MASs), where agents virtualize machines. However, the agent's actual performances in production environments can hardly be estimated as most research has been focusing on isolated projects and specific scenarios. We address this gap by implementing a highly connected and configurable reference model with quantifiable key performance indicators (KPIs) for production scheduling and routing in single-piece workflows. Furthermore, we propose an algorithm to optimize the search of extrema in highly connected distributed systems. The benefits, limits, and drawbacks of MASs and their performances are evaluated extensively by event-based simulations against the introduced model, which acts as a benchmark. Even though the performance of the proposed MAS is, on average, slightly lower than the reference system, the increased flexibility allows it to find new solutions and deliver improved factory-planning outcomes. Our MAS shows an emerging behavior by using flexible production techniques to correct errors and compensate for bottlenecks. This increased flexibility offers substantial improvement potential. The general model in this paper allows the transfer of the results to estimate real systems or other models. KW - cyber-physical production systems KW - event-based simulation KW - multi-agent systems KW - digital factory KW - industrial agents Y1 - 2021 U6 - https://doi.org/10.1109/JESTIE.2021.3108524 SN - 2687-9735 IS - Early Access PB - IEEE CY - New York ER - 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 '21: Proceedings of 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 - https://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 - Chajan, Eduard A1 - Schulte-Tigges, Joschua A1 - Reke, Michael A1 - Ferrein, Alexander A1 - Matheis, Dominik A1 - Walter, Thomas T1 - GPU based model-predictive path control for self-driving vehicles T2 - IEEE Intelligent Vehicles Symposium (IV) N2 - One central challenge for self-driving cars is a proper path-planning. Once a trajectory has been found, the next challenge is to accurately and safely follow the precalculated path. The model-predictive controller (MPC) is a common approach for the lateral control of autonomous vehicles. The MPC uses a vehicle dynamics model to predict the future states of the vehicle for a given prediction horizon. However, in order to achieve real-time path control, the computational load is usually large, which leads to short prediction horizons. To deal with the computational load, the control algorithm can be parallelized on the graphics processing unit (GPU). In contrast to the widely used stochastic methods, in this paper we propose a deterministic approach based on grid search. Our approach focuses on systematically discovering the search area with different levels of granularity. To achieve this, we split the optimization algorithm into multiple iterations. The best sequence of each iteration is then used as an initial solution to the next iteration. The granularity increases, resulting in smooth and predictable steering angle sequences. We present a novel GPU-based algorithm and show its accuracy and realtime abilities with a number of real-world experiments. KW - Heuristic algorithms KW - Computational modeling KW - model-predictive control KW - GPU KW - autonomous driving Y1 - 2021 SN - 978-1-7281-5394-0 U6 - https://doi.org/10.1109/IV48863.2021.9575619 N1 - 2021 IEEE Intelligent Vehicles Symposium (IV), July 11-17, 2021. Nagoya, Japan SP - 1243 EP - 1248 PB - IEEE CY - New York, NY ER - TY - CHAP A1 - Ferrein, Alexander A1 - Meeßen, Marcus A1 - Limpert, Nicolas A1 - Schiffer, Stefan ED - Lepuschitz, Wilfried T1 - Compiling ROS schooling curricula via contentual taxonomies T2 - Robotics in Education N2 - The Robot Operating System (ROS) is the current de-facto standard in robot middlewares. The steadily increasing size of the user base results in a greater demand for training as well. User groups range from students in academia to industry professionals with a broad spectrum of developers in between. To deliver high quality training and education to any of these audiences, educators need to tailor individual curricula for any such training. In this paper, we present an approach to ease compiling curricula for ROS trainings based on a taxonomy of the teaching contents. The instructor can select a set of dedicated learning units and the system will automatically compile the teaching material based on the dependencies of the units selected and a set of parameters for a particular training. We walk through an example training to illustrate our work. Y1 - 2021 SN - 978-3-030-67411-3 U6 - https://doi.org/10.1007/978-3-030-67411-3_5 N1 - RiE: International Conference on Robotics in Education (RiE); Advances in Intelligent Systems and Computing book series (AISC, volume 1316) SP - 49 EP - 60 PB - Springer CY - Cham ER - TY - CHAP A1 - Kirsch, Maximilian A1 - Mataré, Victor A1 - Ferrein, Alexander A1 - Schiffer, Stefan T1 - Integrating golog++ and ROS for Practical and Portable High-level Control T2 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2 N2 - The field of Cognitive Robotics aims at intelligent decision making of autonomous robots. It has matured over the last 25 or so years quite a bit. That is, a number of high-level control languages and architectures have emerged from the field. One concern in this regard is the action language GOLOG. GOLOG has been used in a rather large number of applications as a high-level control language ranging from intelligent service robots to soccer robots. For the lower level robot software, the Robot Operating System (ROS) has been around for more than a decade now and it has developed into the standard middleware for robot applications. ROS provides a large number of packages for standard tasks in robotics like localisation, navigation, and object recognition. Interestingly enough, only little work within ROS has gone into the high-level control of robots. In this paper, we describe our approach to marry the GOLOG action language with ROS. In particular, we present our architecture on inte grating golog++, which is based on the GOLOG dialect Readylog, with the Robot Operating System. With an example application on the Pepper service robot, we show how primitive actions can be easily mapped to the ROS ActionLib framework and present our control architecture in detail. Y1 - 2020 U6 - https://doi.org/10.5220/0008984406920699 N1 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence: ICAART 2020, Valletta, Malta SP - 692 EP - 699 PB - SciTePress CY - Setúbal, Portugal ER - TY - JOUR A1 - Franko, Josef A1 - Du, Shengzhi A1 - Kallweit, Stephan A1 - Duelberg, Enno Sebastian A1 - Engemann, Heiko T1 - Design of a Multi-Robot System for Wind Turbine Maintenance JF - Energies N2 - The maintenance of wind turbines is of growing importance considering the transition to renewable energy. This paper presents a multi-robot-approach for automated wind turbine maintenance including a novel climbing robot. Currently, wind turbine maintenance remains a manual task, which is monotonous, dangerous, and also physically demanding due to the large scale of wind turbines. Technical climbers are required to work at significant heights, even in bad weather conditions. Furthermore, a skilled labor force with sufficient knowledge in repairing fiber composite material is rare. Autonomous mobile systems enable the digitization of the maintenance process. They can be designed for weather-independent operations. This work contributes to the development and experimental validation of a maintenance system consisting of multiple robotic platforms for a variety of tasks, such as wind turbine tower and rotor blade service. In this work, multicopters with vision and LiDAR sensors for global inspection are used to guide slower climbing robots. Light-weight magnetic climbers with surface contact were used to analyze structure parts with non-destructive inspection methods and to locally repair smaller defects. Localization was enabled by adapting odometry for conical-shaped surfaces considering additional navigation sensors. Magnets were suitable for steel towers to clamp onto the surface. A friction-based climbing ring robot (SMART— Scanning, Monitoring, Analyzing, Repair and Transportation) completed the set-up for higher payload. The maintenance period could be extended by using weather-proofed maintenance robots. The multi-robot-system was running the Robot Operating System (ROS). Additionally, first steps towards machine learning would enable maintenance staff to use pattern classification for fault diagnosis in order to operate safely from the ground in the future. Y1 - 2020 U6 - https://doi.org/10.3390/en13102552 SN - 1996-1073 VL - 13 IS - 10 SP - Article 2552 PB - MDPI CY - Basel ER - TY - CHAP A1 - Engemann, Heiko A1 - Du, Shengzhi A1 - Kallweit, Stephan A1 - Ning, Chuanfang A1 - Anwar, Saqib T1 - AutoSynPose: Automatic Generation of Synthetic Datasets for 6D Object Pose Estimation T2 - Machine Learning and Artificial Intelligence. Proceedings of MLIS 2020 N2 - We present an automated pipeline for the generation of synthetic datasets for six-dimension (6D) object pose estimation. Therefore, a completely automated generation process based on predefined settings is developed, which enables the user to create large datasets with a minimum of interaction and which is feasible for applications with a high object variance. The pipeline is based on the Unreal 4 (UE4) game engine and provides a high variation for domain randomization, such as object appearance, ambient lighting, camera-object transformation and distractor density. In addition to the object pose and bounding box, the metadata includes all randomization parameters, which enables further studies on randomization parameter tuning. The developed workflow is adaptable to other 3D objects and UE4 environments. An exemplary dataset is provided including five objects of the Yale-CMU-Berkeley (YCB) object set. The datasets consist of 6 million subsegments using 97 rendering locations in 12 different UE4 environments. Each dataset subsegment includes one RGB image, one depth image and one class segmentation image at pixel-level. Y1 - 2020 SN - 978-1-64368-137-5 U6 - https://doi.org/10.3233/FAIA200770 N1 - Frontiers in Artificial Intelligence and Applications. Vol 332 SP - 89 EP - 97 PB - IOS Press CY - Amsterdam ER - TY - JOUR A1 - Engemann, Heiko A1 - Du, Shengzhi A1 - Kallweit, Stephan A1 - Cönen, Patrick A1 - Dawar, Harshal T1 - OMNIVIL - an autonomous mobile manipulator for flexible production JF - Sensors Y1 - 2020 SN - 1424-8220 U6 - https://doi.org/10.3390/s20247249 N1 - Special issue: Sensor Networks Applications in Robotics and Mobile Systems VL - 20 IS - 24, art. no. 7249 SP - 1 EP - 30 PB - MDPI CY - Basel ER - TY - CHAP A1 - Ulmer, Jessica A1 - Braun, Sebastian A1 - Cheng, Chi-Tsun A1 - Dowey, Steve A1 - Wollert, Jörg T1 - Gamified Virtual Reality Training Environment for the Manufacturing Industry T2 - Proceedings of the 2020 19th International Conference on Mechatronics – Mechatronika (ME) N2 - Industry 4.0 imposes many challenges for manufacturing companies and their employees. Innovative and effective training strategies are required to cope with fast-changing production environments and new manufacturing technologies. Virtual Reality (VR) offers new ways of on-the-job, on-demand, and off-premise training. A novel concept and evaluation system combining Gamification and VR practice for flexible assembly tasks is proposed in this paper and compared to existing works. It is based on directed acyclic graphs and a leveling system. The concept enables a learning speed which is adjustable to the users’ pace and dynamics, while the evaluation system facilitates adaptive work sequences and allows employee-specific task fulfillment. The concept was implemented and analyzed in the Industry 4.0 model factory at FH Aachen for mechanical assembly jobs. Y1 - 2020 U6 - https://doi.org/10.1109/ME49197.2020.9286661 N1 - 2020 19th International Conference on Mechatronics – Mechatronika (ME), Prague, Czech Republic, December 2–4, 2020 SP - 1 EP - 6 PB - IEEE CY - New York, NY ER - TY - CHAP A1 - Reke, Michael A1 - Peter, Daniel A1 - Schulte-Tigges, Joschua A1 - Schiffer, Stefan A1 - Ferrein, Alexander A1 - Walter, Thomas A1 - Matheis, Dominik T1 - A Self-Driving Car Architecture in ROS2 T2 - 2020 International SAUPEC/RobMech/PRASA Conference, Cape Town, South Africa N2 - In this paper we report on an architecture for a self-driving car that is based on ROS2. Self-driving cars have to take decisions based on their sensory input in real-time, providing high reliability with a strong demand in functional safety. In principle, self-driving cars are robots. However, typical robot software, in general, and the previous version of the Robot Operating System (ROS), in particular, does not always meet these requirements. With the successor ROS2 the situation has changed and it might be considered as a solution for automated and autonomous driving. Existing robotic software based on ROS was not ready for safety critical applications like self-driving cars. We propose an architecture for using ROS2 for a self-driving car that enables safe and reliable real-time behaviour, but keeping the advantages of ROS such as a distributed architecture and standardised message types. First experiments with an automated real passenger car at lower and higher speed-levels show that our approach seems feasible for autonomous driving under the necessary real-time conditions. Y1 - 2020 SN - 978-1-7281-4162-6 U6 - https://doi.org/10.1109/SAUPEC/RobMech/PRASA48453.2020.9041020 N1 - 2020 International SAUPEC/RobMech/PRASA Conference, 29-31 Jan. 2020, Cape Town, South Africa SP - 1 EP - 6 PB - IEEE CY - New York, NY ER - TY - CHAP A1 - Ferrein, Alexander A1 - Scholl, Ingrid A1 - Neumann, Tobias A1 - Krückel, Kai A1 - Schiffer, Stefan T1 - A system for continuous underground site mapping and exploration Y1 - 2019 U6 - https://doi.org/10.5772/intechopen.85859 ER - TY - CHAP A1 - Mataré, Victor A1 - Schiffer, Stefan A1 - Ferrein, Alexander ED - Steinbauer, Gerald ED - Ferrein, Alexander T1 - golog++ : An integrative system design T2 - CogRob 2018. Cognitive Robotics Workshop : Proceedings of the 11th Cognitive Robotics Workshop 2018 co-located with 16th International Conference on Principles of Knowledge Representation and Reasoning (KR 2018) Tempe, AZ, USA, October 27th, 2018 Y1 - 2019 SN - 1613-0073 SP - 29 EP - 35 ER - TY - CHAP A1 - Scholl, Ingrid A1 - Bartella, Alex A1 - Moluluo, Cem A1 - Ertural, Berat A1 - Laing, Frederic A1 - Suder, Sebastian T1 - MedicVR : Acceleration and Enhancement Techniques for Direct Volume Rendering in Virtual Reality T2 - Bildverarbeitung für die Medizin 2019 : Algorithmen – Systeme – Anwendungen Y1 - 2019 SN - 978-3-658-25326-4 U6 - https://doi.org/10.1007/978-3-658-25326-4_32 SP - 152 EP - 157 PB - Springer Vieweg CY - Wiesbaden ER - TY - JOUR A1 - Claer, Mario A1 - Ferrein, Alexander A1 - Schiffer, Stefan T1 - Calibration of a Rotating or Revolving Platform with a LiDAR Sensor JF - Applied Sciences Y1 - 2019 U6 - https://doi.org/10.3390/app9112238 SN - 2076-3417 VL - Volume 9 IS - issue 11, 2238 PB - MDPI CY - Basel ER - TY - CHAP A1 - Hofmann, Till A1 - Limpert, Nicolas A1 - Mataré, Victor A1 - Ferrein, Alexander A1 - Lakemeyer, Gerhard T1 - Winning the RoboCup Logistics League with Fast Navigation, Precise Manipulation, and Robust Goal Reasoning T2 - RoboCup 2019: Robot World Cup XXIII. RoboCup Y1 - 2019 SN - 978-3-030-35699-6 U6 - https://doi.org/10.1007/978-3-030-35699-6_41 N1 - Lecture Notes in Computer Science, vol 11531 SP - 504 EP - 516 PB - Springer CY - Cham ER - TY - CHAP A1 - Alhwarin, Faraj A1 - Ferrein, Alexander A1 - Scholl, Ingrid T1 - An Efficient Hashing Algorithm for NN Problem in HD Spaces T2 - Lecture Notes in Computer Science Y1 - 2019 SN - 978-303005498-4 U6 - https://doi.org/10.1007/978-3-030-05499-1_6 N1 - 7th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2018; Funchal; Portugal; 16 January 2018 through 18 January 2018; Code 222779 SP - 101 EP - 115 ER - TY - CHAP A1 - Steinbauer, Gerald A1 - Ferrein, Alexander T1 - CogRob 2018 : Cognitive Robotics Workshop. Proceedings of the 11th Cognitive Robotics Workshop 2018 co-located with 16th International Conference on Principles of Knowledge Representation and Reasoning (KR 2018). Tempe, AZ, USA, October 27th, 2018. T2 - CEUR workshop proceedings Y1 - 2019 SN - 1613-0073 N1 - edited by Gerald Steinbauer, Alexander Ferrein IS - Vol-2325 ER - TY - CHAP A1 - Ferrein, Alexander A1 - Bharatheesha, Mukunda A1 - Schiffer, Stefan A1 - Corbato, Carlos Hernandez T1 - TRROS 2018 : Teaching Robotics with ROS Workshop at ERF 2018; Proceedings of the Workshop on Teaching Robotics with ROS (held at ERF 2018), co-located with European Robotics Forum 2018 (ERF 2018), Tampere, Finland, March 15th, 2018 T2 - CEUR Workshop Proceedings Y1 - 2019 SN - 1613-0073 IS - Vol-2329 ER - TY - CHAP A1 - Wiesen, Patrick A1 - Engemann, Heiko A1 - Limpert, Nicolas A1 - Kallweit, Stephan T1 - Learning by Doing - Mobile Robotics in the FH Aachen ROS Summer School T2 - European Robotics Forum 2018, TRROS18 Workshop Y1 - 2018 SP - 47 EP - 58 ER -