Authors:
Alexander Ferrein
;
Michael Reke
;
Ingrid Scholl
;
Benjamin Decker
;
Nicolas Limpert
;
Gjorgji Nikolovski
and
Stefan Schiffer
Affiliation:
Mobile Autonomous Systems and Cognitive Robotics Institute (MASCOR), FH Aachen University of Applied Sciences, Aachen, Germany
Keyword(s):
Automation, Mining, Control, Autonomous Fleet, Object Detection, Planning, Robotics, Semantic Mapping.
Abstract:
Like many industries, the mining industry is facing major transformations towards more sustainable and decarbonised operations with smaller environmental footprints. Even though the mining industry, in general, is quite conservative, key drivers for future developments are digitalisation and automation. Another direction forward is to mine deeper and reduce the mine footprint at the surface. This leads to so-called hybrid mines, where part of the operation is open pit, and part of the mining takes place underground. In this paper, we present our approach to running a fleet of autonomous hauling vehicles suitable for hybrid mining operations. We present a ROS 2-based architecture for running the vehicles. The fleet of currently three vehicles is controlled by a SHOP3-based planner which dispatches missions to the vehicles. The basic actions of the vehicles are realised as behaviour trees in ROS 2. We used a deep learning network for detection and classification of mining objects train
ed with a mixing of synthetic and real world training images. In a life-long mapping approach, we define lanelets and show their integration into HD maps. We demonstrate a proof-of-concept of the vehicles in operation in simulation and in real-world experiments in a gravel pit.
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