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 - 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 - 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 - 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 - 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 - Stopforth, Riaan A1 - Davrajh, Shaniel A1 - Ferrein, Alexander T1 - South African robotics entity for a collaboration initiative T2 - Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech), 2016 Y1 - 2017 SN - 978-1-5090-3335-5 U6 - https://doi.org/10.1109/RoboMech.2016.7813144 N1 - PRASA-RobMech, Nov. 30 2016-Dec. 2 2016, Stellenbosch, South Africa SP - 1 EP - 6 PB - IEEE ER - TY - JOUR A1 - Schiffer, Stefan A1 - Ferrein, Alexander T1 - Decision-Theoretic Planning with Fuzzy Notions in GOLOG JF - International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems N2 - In this paper we present an extension of the action language Golog that allows for using fuzzy notions in non-deterministic argument choices and the reward function in decision-theoretic planning. Often, in decision-theoretic planning, it is cumbersome to specify the set of values to pick from in the non-deterministic-choice-of-argument statement. Also, even for domain experts, it is not always easy to specify a reward function. Instead of providing a finite domain for values in the non-deterministic-choice-of-argument statement in Golog, we now allow for stating the argument domain by simply providing a formula over linguistic terms and fuzzy uents. In Golog’s forward-search DT planning algorithm, these formulas are evaluated in order to find the agent’s optimal policy. We illustrate this in the Diner Domain where the agent needs to calculate the optimal serving order. Y1 - 2016 U6 - https://doi.org/10.1142/S0218488516400134 SN - 1793-6411 VL - 24 IS - Issue Suppl. 2 SP - 123 EP - 143 PB - World Scientific CY - Singapur ER - TY - CHAP A1 - Walenta, Robert A1 - Schellekens, Twan A1 - Ferrein, Alexander A1 - Schiffer, Stefan T1 - A decentralised system approach for controlling AGVs with ROS T2 - AFRICON, Proceedings Y1 - 2017 SN - 978-1-5386-2775-4 U6 - https://doi.org/10.1109/AFRCON.2017.8095693 SN - 2153-0033 N1 - AFRICON <2017, 18-20 Sept., Cape Town, South Africa> SP - 1436 EP - 1441 PB - IEEE ER - TY - CHAP A1 - Niemueller, T. A1 - Lakemeyer, G. A1 - Reuter, S. A1 - Jeschke, S. A1 - Ferrein, Alexander T1 - Benchmarking of Cyber-Physical Systems in Industrial Robotics: The RoboCup Logistics League as a CPS Benchmark Blueprint T2 - Cyber-Physical Systems: Foundations, Principles and Applications N2 - In the future, we expect manufacturing companies to follow a new paradigm that mandates more automation and autonomy in production processes. Such smart factories will offer a variety of production technologies as services that can be combined ad hoc to produce a large number of different product types and variants cost-effectively even in small lot sizes. This is enabled by cyber-physical systems that feature flexible automated planning methods for production scheduling, execution control, and in-factory logistics. During development, testbeds are required to determine the applicability of integrated systems in such scenarios. Furthermore, benchmarks are needed to quantify and compare system performance in these industry-inspired scenarios at a comprehensible and manageable size which is, at the same time, complex enough to yield meaningful results. In this chapter, based on our experience in the RoboCup Logistics League (RCLL) as a specific example, we derive a generic blueprint for how a holistic benchmark can be developed, which combines a specific scenario with a set of key performance indicators as metrics to evaluate the overall integrated system and its components. KW - Smart factory KW - Industry 4.0 KW - Cyber-physical systems KW - Multi-robot systems KW - Autonomous mobile robots Y1 - 2017 U6 - https://doi.org/10.1016/B978-0-12-803801-7.00013-4 SP - 193 EP - 207 PB - Academic Press CY - London 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 -