Author ORCID Identifier
Date Available
5-7-2024
Year of Publication
2024
Document Type
Master's Thesis
Degree Name
Master of Science in Mining Engineering (MSMIE)
College
Engineering
Department/School/Program
Mining Engineering
Advisor
Dr. Joseph Sottile
Abstract
A great deal of research is currently being conducted in automating mining equipment to improve worker health and safety and increase mine productivity. Significant progress has been made in some applications, e.g., autonomous haul trucks for surface mining. However, little progress has been made in autonomous face haulage in underground room-and pillar coal mines. Accordingly, this thesis addresses automating the operation of a shuttle car, focusing on positioning the shuttle car under the continuous miner coal-discharge conveyor during cutting and loading operations. The approach uses a stereo depth camera as the sensor, and machine-learning algorithms are used to identify various objects in the mine environment, such as the continuous miner coal-discharge conveyor, continuous miner body, roof, ribs, etc. An occupancy map is generated, a path to the continuous miner discharge conveyor is planned, and a controller is used to execute the path. The approach is developed and tested on a 1/6th-scale mock mine and in a simulated mine laboratory using full-scale equipment and manual controls.
Digital Object Identifier (DOI)
https://doi.org/10.13023/etd.2024.96
Funding Information
The research reported in this thesis was supported by the National Institute for Occupational Safety and Health under Contract Number 75D30120C08908. This thesis was supported by Central Appalachian Region Education and Research Center through Grant 6T42OH010278.
Recommended Citation
Rose, Sky, "AUTONOMOUS SHUTTLE CAR DOCKING TO A CONTINUOUS MINER USING RGB-DEPTH IMAGERY" (2024). Theses and Dissertations--Mining Engineering. 83.
https://uknowledge.uky.edu/mng_etds/83
