Abstract
This paper describes a vision-based autonomous docking solution that moves a coalmine shuttle car to the continuous miner in GPS-denied underground environments. The solution adapts and improves state-of-the-art autonomous docking techniques using a RGBD camera specifically in under-ground mine environments. It includes five processing modules: scene segmentation, segmented point-cloud generation, occupancy grid estimation, path planner, and controller. A two-stage approach is developed to train the scene segmentation network for adapting to the changes from normal environments to dark mines. The resulting network detects both the continuous miner and other objects accurately in mines. Based upon these recognized objects, a path is planned for moving the shuttle car from its initial position to the continuous miner, while avoiding obstacles and other workers. Experiments are conducted using the system in a 1/6th-scale lab environment and data collected in a full-scale realistic mine environment with full-size equipment. The results show the potential of this solution, which can significantly enhance the safety of workers in mining operations.
Original language | English |
---|---|
Title of host publication | SSRR 2022 - IEEE International Symposium on Safety, Security, and Rescue Robotics |
Pages | 327-334 |
Number of pages | 8 |
ISBN (Electronic) | 9781665456807 |
DOIs | |
State | Published - 2022 |
Event | 2022 IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR 2022 - Sevilla, Spain Duration: Nov 8 2022 → Nov 10 2022 |
Publication series
Name | SSRR 2022 - IEEE International Symposium on Safety, Security, and Rescue Robotics |
---|
Conference
Conference | 2022 IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR 2022 |
---|---|
Country/Territory | Spain |
City | Sevilla |
Period | 11/8/22 → 11/10/22 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Funding
ACKNOWLEDGMENTS This material is based upon work supported by the NIOSH (National Institute for Occupational Safety and Health) Autonomous Docking of Face Haulage Mining Machinery in GPS-Denied Environments Program under Contract 75D30120C08908. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the US government.
Funders | Funder number |
---|---|
National Institute for Occupational Safety and Health) Autonomous Docking of Face Haulage Mining Machinery | 75D30120C08908 |
National Institute for Occupational Safety and Health |
ASJC Scopus subject areas
- Artificial Intelligence
- Aerospace Engineering
- Automotive Engineering
- Mechanical Engineering
- Safety, Risk, Reliability and Quality
- Control and Optimization
- Safety Research