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 - Reuter, Sebastian A1 - Ewert, Daniel A1 - Ferrein, Alexander A1 - Jeschke, Sabina A1 - Lakemeyer, Gerhard T1 - Decisive Factors for the Success of the Carologistics RoboCup Team in the RoboCup Logistics League 2014 T2 - RoboCup 2014: Robot World Cup XVIII Y1 - 2015 SN - 978-3-319-18615-3 N1 - Lecture Notes in Computer Science ; 8992 SP - 155 EP - 167 PB - Springer ER - TY - CHAP A1 - Niemueller, Tim A1 - Reuter, Sebastian A1 - Ewert, Daniel A1 - Ferrein, Alexander A1 - Jeschke, Sabina A1 - Lakemeyer, Gerhard ED - Almeida, Luis T1 - The Carologistics Approach to Cope with the Increased Complexity and New Challenges of the RoboCup Logistics League 2015 T2 - RoboCup 2015: Robot World Cup XIX Y1 - 2016 SN - 978-3-319-29339-4 U6 - https://doi.org/10.1007/978-3-319-29339-4_4 N1 - Lecture Notes in Computer Science ; 9513 SP - 47 EP - 59 PB - Springer International Publishing CY - Cham ER - TY - CHAP A1 - Niemueller, Tim A1 - Reuter, Sebastian A1 - Ferrein, Alexander ED - Almeida, Luis T1 - Fawkes for the RoboCup Logistics League T2 - RoboCup 2015: Robot World Cup XIX Y1 - 2016 SN - 978-3-319-29339-4 U6 - https://doi.org/10.1007/978-3-319-29339-4_31 N1 - Lecture Notes in Computer Science ; 9513 SP - 365 EP - 373 PB - Springer International Publishing CY - Cham ER - TY - CHAP A1 - Niemueller, Tim A1 - Reuter, Sebastian A1 - Ferrein, Alexander A1 - Jeschke, Sabina A1 - Lakemeyer, Gerhard ED - Almeida, Luis T1 - Evaluation of the RoboCup Logistics League and Derived Criteria for Future Competitions T2 - RoboCup 2015: Robot World Cup XIX Y1 - 2016 SN - 978-3-319-29339-4 U6 - https://doi.org/10.1007/978-3-319-29339-4_3 N1 - Lecture Notes in Computer Science ; 9513 SP - 31 EP - 43 PB - Springer International Publishing 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 - 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 - https://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 - 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 - Schiffer, Stefan A1 - Ferrein, Alexander T1 - A System Layout for Cognitive Service Robots T2 - Cognitive Robot Architectures. Proceedings of EUCognition 2016 Y1 - 2017 SN - 1613-0073 N1 - CEUR-WS Vol-1855 SP - 44 EP - 45 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 -