TY  - CHAP
A1  - Donner, Ralf
A1  - Rabel, Matthias
A1  - Scholl, Ingrid
A1  - Ferrein, Alexander
A1  - Donner, Marc
A1  - Geier, Andreas
A1  - John, André
A1  - Köhler, Christian
A1  - Varga, Sebastian
T1  - Die Extraktion bergbaulich relevanter Merkmale aus 3D-Punktwolken eines untertagetauglichen mobilen Multisensorsystems
T2  - Tagungsband Geomonitoring
Y1  - 2019
U6  - http://dx.doi.org/10.15488/4515
SP  - 91
EP  - 110
ER  - 
TY  - CHAP
A1  - Steinbauer, Gerald
A1  - Ferrein, Alexander
T1  - CogRob 2018 : 
Cognitive Robotics Workshop.
Proceedings of the 11th Cognitive Robotics Workshop 2018
co-located with 16th International Conference on Principles of Knowledge Representation and Reasoning (KR 2018).
Tempe, AZ, USA, October 27th, 2018.
T2  - CEUR workshop proceedings
Y1  - 2019
SN  - 1613-0073
N1  - edited by Gerald Steinbauer, Alexander Ferrein
IS  - Vol-2325
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  - 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  - Kirsch, Maximilian
A1  - Mataré, Victor
A1  - Ferrein, Alexander
A1  - Schiffer, Stefan
T1  - Integrating golog++ and ROS for Practical and Portable High-level Control
T2  - 12th International Conference on Agents and Artificial Intelligence
Y1  - 2020
U6  - http://dx.doi.org/10.5220/0008984406920699
ER  - 
TY  - JOUR
A1  - Hofmann, Till
A1  - Limpert, Nicolas
A1  - Mataré, Victor
A1  - Schönitz, Sebastian
A1  - Niemueller, Tim
A1  - Ferrein, Alexander
A1  - Lakemeyer, Gerhard
T1  - The Carologistics RoboCup Logistics Team 2018
N2  - The Carologistics team participates in the RoboCup Logistics League for the seventh year. The RCLL requires precise vision,
manipulation and path planning, as well as complex high-level decision
making and multi-robot coordination. We outline our approach with an
emphasis on recent modifications to those components.
The team members in 2018 are David Bosen, Christoph Gollok, Mostafa
Gomaa, Daniel Habering, Till Hofmann, Nicolas Limpert, Sebastian Schönitz,
Morian Sonnet, Carsten Stoffels, and Tarik Viehmann.
This paper is based on the last year’s team description.
Y1  - 2018
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
Y1  - 2020
SN  - 978-1-7281-4162-6
U6  - http://dx.doi.org/10.1109/SAUPEC/RobMech/PRASA48453.2020.9041020
SP  - 1
EP  - 6
ER  - 
TY  - JOUR
A1  - Limpert, Nicolas
A1  - Wiesen, Patrick
A1  - Ferrein, Alexander
A1  - Kallweit, Stephan
A1  - Schiffer, Stefan
T1  - The ROSIN Project and its Outreach to South Africa
JF  - R&D Journal
Y1  - 2019
VL  - 35
SP  - 1
EP  - 6
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  - 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  -