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  - CHAP
A1  - Nikolovski, Gjorgji
A1  - Limpert, Nicolas
A1  - Nessau, Hendrik
A1  - Reke, Michael
A1  - Ferrein, Alexander
T1  - Model-predictive control with parallelised optimisation for the navigation of autonomous mining vehicles
T2  - 2023 IEEE Intelligent Vehicles Symposium (IV)
N2  - The work in modern open-pit and underground mines requires the transportation of large amounts of resources between fixed points. The navigation to these fixed points is a repetitive task that can be automated. The challenge in automating the navigation of vehicles commonly used in mines is the systemic properties of such vehicles. Many mining vehicles, such as the one we have used in the research for this paper, use steering systems with an articulated joint bending the vehicle’s drive axis to change its course and a hydraulic drive system to actuate axial drive components or the movements of tippers if available. To address the difficulties of controlling such a vehicle, we present a model-predictive approach for controlling the vehicle. While the control optimisation based on a parallel error minimisation of the predicted state has already been established in the past, we provide insight into the design and implementation of an MPC for an articulated mining vehicle and show the results of real-world experiments in an open-pit mine environment.
KW  - Mpc
KW  - Control
KW  - Path-following
KW  - Navigation
KW  - Automation
Y1  - 2023
SN  - 979-8-3503-4691-6 (Online)
SN  - 979-8-3503-4692-3 (Print)
U6  - http://dx.doi.org/10.1109/IV55152.2023.10186806
N1  - IEEE Symposium on Intelligent Vehicle, 4.-7. June 2023, Anchorage, AK, USA.
PB  - IEEE
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 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  - JOUR
A1  - Schulte-Tigges, Joschua
A1  - Förster, Marco
A1  - Nikolovski, Gjorgji
A1  - Reke, Michael
A1  - Ferrein, Alexander
A1  - Kaszner, Daniel
A1  - Matheis, Dominik
A1  - Walter, Thomas
T1  - Benchmarking of various LiDAR sensors for use in self-driving vehicles in real-world environments
JF  - Sensors
N2  - Abstract
In this paper, we report on our benchmark results of the LiDAR sensors Livox Horizon, Robosense M1, Blickfeld Cube, Blickfeld Cube Range, Velodyne Velarray H800, and Innoviz Pro. The idea was to test the sensors in different typical scenarios that were defined with real-world use cases in mind, in order to find a sensor that meet the requirements of self-driving vehicles. For this, we defined static and dynamic benchmark scenarios. In the static scenarios, both LiDAR and the detection target do not move during the measurement. In dynamic scenarios, the LiDAR sensor was mounted on the vehicle which was driving toward the detection target. We tested all mentioned LiDAR sensors in both scenarios, show the results regarding the detection accuracy of the targets, and discuss their usefulness for deployment in self-driving cars.
KW  - Lidar
KW  - Benchmark
KW  - Self-driving
Y1  - 2022
U6  - http://dx.doi.org/10.3390/s22197146
SN  - 1424-8220
N1  - This article belongs to the Special Issue "Sensor Fusion for Vehicles Navigation and Robotic Systems"
VL  - 22
IS  - 19
PB  - MDPI
CY  - Basel
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  - http://dx.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
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
Y1  - 2021
SN  - 978-3-030-67411-3
U6  - http://dx.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  - Hofmann, Till
A1  - Limpert, Nicolas
A1  - Mataré, Viktor
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  - http://dx.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  - Eltester, Niklas Sebastian
A1  - Ferrein, Alexander
A1  - Schiffer, Stefan
T1  - A smart factory setup based on the RoboCup logistics league
T2  - 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS)
N2  - In this paper we present SMART-FACTORY, a setup for a research and teaching facility in industrial robotics that is based on the RoboCup Logistics League. It is driven by the need for developing and applying solutions for digital production. Digitization receives constantly increasing attention in many areas, especially in industry. The common theme is to make things smart by using intelligent computer technology. Especially in the last decade there have been many attempts to improve existing processes in factories, for example, in production logistics, also with deploying cyber-physical systems. An initiative that explores challenges and opportunities for robots in such a setting is the RoboCup Logistics League. Since its foundation in 2012 it is an international effort for research and education in an intra-warehouse logistics scenario. During seven years of competition a lot of knowledge and experience regarding autonomous robots was gained. This knowledge and experience shall provide the basis for further research in challenges of future production. The focus of our SMART-FACTORY is to create a stimulating environment for research on logistics robotics, for teaching activities in computer science and electrical engineering programmes as well as for industrial users to study and explore the feasibility of future technologies. Building on a very successful history in the RoboCup Logistics League we aim to provide stakeholders with a dedicated facility oriented at their individual needs.
Y1  - 2020
U6  - http://dx.doi.org/10.1109/ICPS48405.2020.9274766
N1  - 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS), 10-12 June 2020, Tampere, Finland.
SP  - 297
EP  - 302
PB  - IEEE
ER  -