TY - GEN A1 - Zindler, Klaus T1 - Wohin steuert das automatisierte Fahren? N2 - Vortrag KW - Autonomes Fahren KW - Autonomes Fahrzeug Y1 - 2018 ER - TY - CHAP A1 - Zindler, Klaus T1 - Welcome & Key Research Areas of Aschaffenburg University of Applied Sciences T2 - Proceedings of International Research Days KW - Key Research Areas KW - Hochschule Aschaffenburg KW - Forschung Y1 - 2021 ER - TY - CHAP A1 - Zindler, Klaus A1 - Sauer, Timm T1 - Self learning control for automatic track guidance T2 - Proceedings of International Research Days KW - automatic track guidance KW - self learning control KW - Artificial Intelligence KW - automated vehicle guidance KW - Autonomes Fahrzeug Y1 - 2021 VL - 2021 SP - 64 EP - 64 ER - TY - JOUR A1 - Sauer, Timm A1 - Gorks, Manuel A1 - Spielmann, Luca A1 - Zindler, Klaus T1 - Automatische Spurführung von Flurförderzeugen mittels KI JF - ATZ heavyduty N2 - Im Verbundprojekt KAnIS forscht die TH Aschaffenburg mit Linde Material Handling an neuen KI-basierten Algorithmen zur querdynamischen Fahrzeugführung von Flurförderzeugen. Ziel des Projekts ist die Entwicklung eines universell einsetzbaren Regelungskonzepts, das verschiedene Arten von Flurförderzeugen automatisch führt, im Betrieb auftretenden Fahrzeugparameterschwankungen gezielt Rechnung trägt und darüber hinaus auch den jeweiligen Fitnesszustand der Fahrzeuge berücksichtigt. KW - Flurförderer KW - Künstliche Intelligenz Y1 - 2022 VL - 15 IS - 04/2022 SP - 44 EP - 47 ER - TY - JOUR A1 - Zindler, Klaus A1 - Sauer, Timm A1 - Spielmann, Luca A1 - Gorks, Manuel T1 - Chancen durch kooperative Logistikflotten JF - Fördern und Heben N2 - In Zeiten einer globalen Weltwirtschaft und einem von zunehmendem Wettbewerb geprägten Markt ist die Automatisierung logistischer Prozesse eine Voraussetzung für den Unternehmenserfolg. Vor allem die Steigerung der Produktivität und der Effizienz des innerbetrieblichen Materialflusses nimmt einen hohen Stellenwert ein. Mit dem Einsatz automatisch fahrender Flurförderzeuge (FFZ) lässt sich dies erreichen. Als Vision sollten jedoch nicht einzelne automatisch fahrende FFZ gelten. Ziel muss es sein, die Mitglieder einer heterogenen Logistikflotte miteinander zu vernetzen und eine Kommunikations- und Rechenplattform einzurichten. Dies bietet ein großes Potenzial im Hinblick auf die Auftragsplanung zur Verbesserung der Wirtschaftlichkeit sowie in den Bereichen Lokalisierung und Sicherheit zur Optimierung automatisch fahrender FFZ. KW - Flurförderer KW - Autonomes Fahrzeug Y1 - 2022 VL - 2022 IS - 10 SP - 36 EP - 40 ER - TY - JOUR A1 - Sauer, Timm A1 - Gorks, Manuel A1 - Spielmann, Luca A1 - Zindler, Klaus A1 - Jumar, Ulrich T1 - Adaptive self-learning controllers with disturbance compensation for automatic track guidance of industrial trucks JF - SICE Journal of Control, Measurement and System Integration N2 - This paper presents an extended control concept for automatic track guidance of industrial trucks in intralogistic systems. It is based on Reinforcement Learning (RL), a method of Artificial Intelligence (AI). The presented approach is able to adapt itself to different industrial truck variants and to the associated specific vehicle parameters. In order to avoid starting the whole training of the controller for each truck variant from scratch, the training process is divided into two steps. In the first step, the controller is trained on a simplified linear model using parameters of a nominal vehicle variant. Based on this, the control parameters are only fine-tuned in the second step using a more complex nonlinear model, representing the real industrial truck. In this way, the controller is adapted to the actual truck variant and the corresponding parameter values. By using the nonlinear model, it can be ensured that the forklift’s dynamic is approximated within the entire operating range, even at high steering angles. Moreover, the influence of the disturbance variable of the system (path curvature) is compensated by considering this a priori knowledge within the control design. Therefore, the Artificial Neural Networks (ANN) of the RL controller and the observation vector are suitably adjusted. In this way, the occurring path curvatures can be considered in both training steps and the control parameters can be optimized accordingly. Thus, the influence of the disturbance variable can be compensated, which significantly improves the control quality. In order to demonstrate this, the new approach is compared to an RL control concept, which is not considering the disturbance variable and to a classical two-degrees-of-freedom (2DoF) control approach. KW - Flurförderer KW - Spurführung KW - Künstliche Intelligenz Y1 - 2023 U6 - https://doi.org/10.1080/18824889.2023.2183009 VL - 2023 IS - Vol. 16, No. 1 SP - 84 EP - 97 ER - TY - CHAP A1 - Sauer, Timm A1 - Spielmann, Luca A1 - Gorks, Manuel A1 - Zindler, Klaus A1 - Jumar, Ulrich T1 - Model Predictive Control of Industrial Trucks with AI-based Plant Model Selection T2 - 9th International Conference on Control, Decision and Information Technologies (CoDIT) N2 - In this paper, a new control concept for automatic track guidance of forklifts within a heterogeneous logistics fleet is presented. The proposed control scheme is universally applicable and based on Artificial Intelligence (AI). The lateral control is realized using a Model Predictive Controller (MPC). In order to take into account the diversity of the industrial truck variants, a multi-model approach is used. Therefore, a plant model for each truck variant is integrated into the MPC. In a practical application, the most suitable model has to be selected. This decision is based on AI in the form of an Artificial Neural Network (ANN). It is able to distinguish the different truck variants based on that part of the plant's state vector, which represents the vehicle dynamic characteristics. Thus, the appropriate model can be used, which significantly improves the control quality and guarantees an accurate track guidance of different forklifts. Due to the AI-based selection of the model, the computational effort can be kept low and real-time capability can be ensured. In order to classify the performance of the proposed control concept, its simulation results are compared to the closed-loop behavior, using a classical MPC. KW - automatic control KW - robust control KW - AI KW - ANN KW - Flurförderer KW - Künstliche Intelligenz Y1 - 2023 UR - https://ieeexplore.ieee.org/abstract/document/10284427 U6 - https://doi.org/10.1109/CoDIT58514.2023.10284427 VL - 2023 IS - Conference Proceedings SP - 263 EP - 268 ER - TY - CHAP A1 - Sauer, Timm A1 - Gorks, Manuel A1 - Spielmann, Luca A1 - Hepp, Nils A1 - Zindler, Klaus A1 - Jumar, Ulrich T1 - AI-based control approaches for lateral vehicle guidance of industrial trucks T2 - IFAC WC 2023 – The 22nd World Congress of the International Federation of Automatic Control 2023, Yokohama, Japan, 09.07.2023 - 14.07.2023 N2 - Two different control concepts for the automatic track guidance of forklifts are proposed. Both approaches are based on Reinforcement Learning (RL), a method of Artificial Intelligence (AI), and are able to take into account time-variant parameters, such as the vehicle velocity, and to reduce the influence of the path curvature, the most important disturbance variable of lateral vehicle control. In the first approach, both, the path curvature and the vehicle velocity signal, are provided to the controller in addition to the state variables of the controlled system. By varying the corresponding parameters in the training process, both signals can be considered and the control parameters can be optimized accordingly. In the second approach, several controllers (multi-model concept) considering the path curvature are used and the varying vehicle velocity is taken into account using a gain-scheduling concept. Considering time-variant vehicle parameters and the influence of the disturbance variable during operation, a stable track guidance is guaranteed within the whole speed range of the industrial trucks. KW - Artificial intelligence, Lateral vehicle control, Intelligent transportation systems KW - Flurförderer KW - Künstliche Intelligenz Y1 - 2023 UR - https://www.sciencedirect.com/science/article/pii/S2405896323019092 U6 - https://doi.org/https://doi.org/10.1016/j.ifacol.2023.10.1501. ER - TY - JOUR A1 - Sauer, Timm A1 - Zindler, Klaus A1 - Jumar, Ulrich T1 - KI-basierte Regelungskonzepte zur automatischen Spurregelung von Flurförderzeugen JF - at - Automatisierungstechnik N2 - In diesem Beitrag werden zwei KI-basierte Regelungskonzepte zur automatischen Spurführung von Flurförderzeugen (FFZ) einer heterogenen Logistikflotte vorgestellt. Während die modellfreien Verfahren des bestärkenden Lernens für diese Anwendung als Vertreter der direkten neuronalen Regelung vorgestellt werden, lässt sich das zweite Regelungskonzept in die Klasse der indirekten neuronalen Regelungen einordnen. Beide Konzepte zeichnen sich dadurch aus, dass sie dazu in der Lage sind, die Reglerparameter an verschiedene Varianten von FFZ anzupassen sowie vorab bekanntes Wissen bezüglich des Regelstreckenverhaltens beim Entwurf der Regler gezielt zu berücksichtigen. KW - Automatische Spurführung; Künstliche Intelligenz KW - Flurförderer KW - Künstliche Intelligenz KW - Spurführung Y1 - 2024 UR - https://www.degruyter.com/document/doi/10.1515/auto-2023-0155/html U6 - https://doi.org/https://doi.org/10.1515/auto-2023-0155 VL - 72 IS - 4 SP - 336 EP - 353 ER - TY - GEN A1 - Zindler, Klaus A1 - Doll, Konrad T1 - Starke Partner für eine starke Region BT - Bereichsvorstellung AUTOMOTIVE N2 - Vortrag KW - Kraftfahrzeugtechnik Y1 - 2014 ER - TY - CHAP A1 - Zindler, Klaus A1 - Geiß, Niklas A1 - Doll, Konrad A1 - Heinlein, Sven T1 - Real-Time Ego-Motion Estimation using Lidar and a Vehicle Model Based Extended Kalman Filter T2 - Proceedings of the IEEE 17th International Conference on Intelligent Transportation Systems (ITSC 2014), Qingdao, China, October 8-11, 2014 KW - Kraftfahrzeug KW - Kalman-Filter Y1 - 2014 U6 - https://doi.org/10.1109/ITSC.2014.6957728 VL - 2014 IS - Beitrag Nr. ThA6.1 SP - 431 EP - 438 PB - IEEE ER - TY - CHAP A1 - Köhler, Sebastian A1 - Schreiner, Brian A1 - Ronalter, Steffen A1 - Doll, Konrad A1 - Brunsmann, Ulrich A1 - Zindler, Klaus T1 - Autonomous Evasive Maneuvers Triggered by Infrastructure-Based Detection of Pedestrian Intentions T2 - IEEE Intelligent Vehicles Symposium (IV' 13), Gold Coast, Australien, 23.-26. Juni KW - Evasive Maneuvers KW - Fahrerassistenzsystem KW - Bildverarbeitung Y1 - 2013 U6 - https://doi.org/10.1109/IVS.2013.6629520 SN - 1931-0587 SP - 519 EP - 526 ER - TY - CHAP A1 - Goldhammer, Michael A1 - Hubert, Andreas A1 - Köhler, Sebastian A1 - Zindler, Klaus A1 - Brunsmann, Ulrich A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Analysis on Termination of Pedestrians‘ Gait at Urban Intersections T2 - Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2014 U6 - https://doi.org/10.1109/ITSC.2014.6957947 SP - 1758 EP - 1763 PB - IEEE CY - Qingdao, China ER - TY - CHAP A1 - Hahn, Stefan A1 - Zindler, Klaus A1 - Doll, Konrad A1 - Jumar, Ulrich T1 - New Control Scheme for a Lane-Keeping Evasive Maneuver Exploiting the Free Space Optimally T2 - Proceedings of the 20th International Conference on Methods and Models in Automation and Robotics,Miedzyzdroje, Poland, 24-27 August, 2015 KW - Fahrerassistenzsystem Y1 - 2015 SP - 856 EP - 861 ER - TY - GEN A1 - Doll, Konrad A1 - Zindler, Klaus T1 - Hochautomatisiertes Fahren: Sensorik, Sensordatenverarbeitung und Fahrzeugführung T2 - Technologieforum Innovative Sensorik-Anwendungen im Automotive-Bereich N2 - Vortrag KW - Autonomes Fahrzeug KW - Sensortechnik KW - Vortrag Y1 - 2016 VL - 2016 ER - TY - JOUR A1 - Zindler, Klaus A1 - Doll, Konrad A1 - Huber, Bertold ED - WILEY-VCH, Verlag T1 - Sicher unterwegs - Fortschritte beim aktiven Fußgängerschutz JF - messtec drives Automation KW - aktiver Fußgängerschutz, automatische Brems- und Ausweichmanöver KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2017 UR - https://www.wileyindustrynews.com/restricted-files/161702 VL - 25 IS - 03 SP - 82 EP - 82 ER -