TY - CHAP A1 - Niemueller, Tim A1 - Neumann, Tobias A1 - Henke, Christoph A1 - Schönitz, Sebastian A1 - Reuter, Sebastian A1 - Ferrein, Alexander A1 - Jeschke, Sabina A1 - Lakemeyer, Gerhard T1 - Improvements for a robust production in the RoboCup logistics league 2016 T2 - RoboCup 2016: Robot World Cup XX. RoboCup 2016. Y1 - 2017 SN - 978-3-319-68792-6 U6 - https://doi.org/10.1007/978-3-319-68792-6_49 SP - 589 EP - 600 PB - Springer CY - Cham 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 - Leingartner, Max A1 - Maurer, Johannes A1 - Steinbauer, Gerald A1 - Ferrein, Alexander T1 - Evaluation of sensors and mapping approaches for disasters in tunnels T2 - IEEE International Symposium on Safety, Security, and Rescue Robotics : SSRR : 21-26 Oct. 2013, Linkoping, Sweden Y1 - 2013 SN - 978-1-4799-0879-0 SP - 1 EP - 7 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 - Niemueller, Tim A1 - Neumann, Tobias A1 - Henke, Christoph A1 - Schönitz, Sebastian A1 - Reuter, Sebastian A1 - Ferrein, Alexander A1 - Jeschke, Sabina A1 - Lakemeyer, Gerhard T1 - International Harting Open Source Award 2016: Fawkes for the RoboCup Logistics League T2 - RoboCup 2016: RoboCup 2016: Robot World Cup XX. RoboCup 2016 Y1 - 2017 SN - 978-3-319-68792-6 U6 - https://doi.org/10.1007/978-3-319-68792-6_53 N1 - Lecture Notes in Computer Science, LNCS, Vol 9776 SP - 634 EP - 642 PB - Springer CY - Cham ER - TY - CHAP A1 - Niemueller, Tim A1 - Zwilling, Frederik A1 - Lakemeyer, Gerhard A1 - Löbach, Matthias A1 - Reuter, Sebastian A1 - Jeschke, Sabina A1 - Ferrein, Alexander T1 - Cyber-Physical System Intelligence T2 - Industrial Internet of Things N2 - Cyber-physical systems are ever more common in manufacturing industries. Increasing their autonomy has been declared an explicit goal, for example, as part of the Industry 4.0 vision. To achieve this system intelligence, principled and software-driven methods are required to analyze sensing data, make goal-directed decisions, and eventually execute and monitor chosen tasks. In this chapter, we present a number of knowledge-based approaches to these problems and case studies with in-depth evaluation results of several different implementations for groups of autonomous mobile robots performing in-house logistics in a smart factory. We focus on knowledge-based systems because besides providing expressive languages and capable reasoning techniques, they also allow for explaining how a particular sequence of actions came about, for example, in the case of a failure. KW - Smart factory KW - Industry 4.0 KW - Multi-robot systems KW - Autonomous mobile robots KW - RoboCup Y1 - 2017 SN - 978-3-319-42559-7 U6 - https://doi.org/10.1007/978-3-319-42559-7_17 N1 - Springer Series in Wireless Technology SP - 447 EP - 472 PB - Springer CY - Cham ER - TY - CHAP A1 - Krückel, Kai A1 - Nolden, Florian A1 - Ferrein, Alexander A1 - Scholl, Ingrid T1 - Intuitive visual teleoperation for UGVs using free-look augmented reality displays T2 - 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA Y1 - 2015 U6 - https://doi.org/10.1109/ICRA.2015.7139809 SP - 4412 EP - 4417 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 - https://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 - 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 - Marco, Heather G. A1 - Ferrein, Alexander T1 - AGNES: The African-German Network of Excellence in Science T2 - Proceedings of the 2nd Developing World Robotics Forum, Workshop at IEEE AFRICON 2017 Y1 - 2017 SP - 1 EP - 2 ER -