TY  - CHAP
A1  - Eichenbaum, Julian
A1  - Nikolovski, Gjorgji
A1  - Mülhens, Leon
A1  - Reke, Michael
A1  - Ferrein, Alexander
A1  - Scholl, Ingrid
T1  - Towards a lifelong mapping approach using Lanelet 2 for autonomous open-pit mine operations
T2  - 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)
N2  - Autonomous agents require rich environment models for fulfilling their missions. High-definition maps are a well-established map format which allows for representing semantic information besides the usual geometric information of the environment. These are, for instance, road shapes, road markings, traffic signs or barriers. The geometric resolution of HD maps can be as precise as of centimetre level. In this paper, we report on our approach of using HD maps as a map representation for autonomous load-haul-dump vehicles in open-pit mining operations. As the mine undergoes constant change, we also need to constantly update the map. Therefore, we follow a lifelong mapping approach for updating the HD maps based on camera-based object detection and GPS data. We show our mapping algorithm based on the Lanelet 2 map format and show our integration with the navigation stack of the Robot Operating System. We present experimental results on our lifelong mapping approach from a real open-pit mine.
Y1  - 2023
SN  - 979-8-3503-2069-5 (Online)
SN  - 979-8-3503-2070-1 (Print)
U6  - http://dx.doi.org/10.1109/CASE56687.2023.10260526
N1  - 19th International Conference on Automation Science and Engineering (CASE), 26-30 August 2023, Auckland, New Zealand.
PB  - IEEE
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  - http://dx.doi.org/10.1109/IV55152.2023.10186806
N1  - IEEE Symposium on Intelligent Vehicle, 4.-7. June 2023, Anchorage, AK, USA.
PB  - IEEE
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  -