ST18 Autonomous enhancement strategies (detection of wall impacts and development of a self-cleaning laser scanner)

Antonio, Simbarashe (2020) ST18 Autonomous enhancement strategies (detection of wall impacts and development of a self-cleaning laser scanner). [USQ Project]

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Fatalities and major accidents continue to occur in mines. While technology has continued to play a pivotal role in assisting mining companies to safely and successfully conduct their operations, a high level of innovation is still required to attain greater technological advancements. The industry needs competent and skilled personnel to design, implement and rectify processes and systems within the mining industry to make autonomous systems safe and reliable.

Epiroc Australia (Epiroc) is in manufacturing front-end loaders that are highly optimised for underground mining environments to perform the load-haul-dump (LHD) function of the mining process. This family of machines is known as Scoop Trams (ST) and comes in various bucket capacities from seven metric tonnes up to 18. To complement the ST-family of LHD loaders, Epiroc has developed an autonomous system for the ST18 to enhance safety, workplace environment and productivity for clients in underground operations. The autonomous system's challenges in an underground mining operation system impact aspects that include the environment, network systems and dust and rock structure instability.

The autonomous system utilises the Kalman filter for position localisation using the odometer wheel sensor, articulation angle sensor, Inertial Measurement Unit (IMU) and two LIDAR scanners. The Kalman filter runs recursively to continually update the loader's position by optimally estimating the system variables (speed, attitude, gyroscopic data, acceleration and the laser scan data). If any of the sensors fails, the automation system will detect this and stop the machine to prevent any damage from occurring. This research paper focused on the reliability issues caused by path tracking and localisation errors affected by sensor contamination or failure. The focus was on the laser scanner sensor and the IMU, two major components that stop the autonomous operation. The paper looked at current technologies on the market for cleaning contaminated laser scanners and how the IMU data is currently used for impact detection. After identifying the gap, a solution for laser contamination was developed to clean a dirty laser scanner to minimise stoppages. An application was developed to utilise IMU data from the loader to detect and minimise machine damage by detecting wall impact events. The paper discussed the methods used in the development, the testing and verification of these systems. Further research into a long term replacement for the laser scanner was conducted. In the future, the IMU data use was discussed on how the autonomous algorithm could be enhanced to improve continually without degrading the environment.

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Item Type: USQ Project
Item Status: Live Archive
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 - 31 Dec 2021)
Supervisors: Nowen, Peter; Wen, Paul
Qualification: Bachelor of Engineering (Honours) / Bachelor of Science (Instrumentation, Controls & Automation and Computer Systems)
Date Deposited: 22 Jul 2021 00:24
Last Modified: 26 Jun 2023 03:41
Uncontrolled Keywords: autonomous systems; front-end loaders; St18; sensors; self-cleaning
Fields of Research (2008): 09 Engineering > 0906 Electrical and Electronic Engineering > 090602 Control Systems, Robotics and Automation
09 Engineering > 0913 Mechanical Engineering > 091302 Automation and Control Engineering
09 Engineering > 0914 Resources Engineering and Extractive Metallurgy > 091405 Mining Engineering
Fields of Research (2020): 40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400702 Automation engineering
40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400706 Field robotics
40 ENGINEERING > 4019 Resources engineering and extractive metallurgy > 401905 Mining engineering

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