Coal Preparation » General
The automation of bulldozer operations on stockpiles presents a significant opportunity to enhance safety and operational efficiency. Manual stockpile management exposes operators to risks such as whole body vibration, engulfment hazards, and unstable terrain conditions. Semi-autonomous solutions offer the prospect of mitigating these risks by removing the operator from the machine while also delivering operational improvements. This project, building on previous ACARP projects, explores the technical feasibility and efficiency potential of semi-autonomous bulldozing systems for stacking and reclamation tasks as needed in stockyard management.
The primary objectives of this project were to:
- develop a capability for real-time monitoring of stockpile topography,
- benchmark the performance of manual bulldozer operations,
- quantify potential efficiency improvements through automated planning algorithms, and
- evaluate operational benefits of semi-autonomous systems. The study focused on enhancing situational awareness for operators and optimizing material movement to maximize efficiency.
The methodology included a performance study of manual bulldozer operations for stacking and reclamation tasks, the development of a validated stockyard simulation model providing a digital twin of the stockyard, and the creation of strategic and tactical planning algorithms to achieve efficient material movement. These algorithms were applied to determine and simulate sequenced operations, which were then compared to observed manual operations. Key performance metrics such as time, energy, and undercarriage travel were used to evaluate efficiency gains.
The findings indicate potential efficiency improvements through sequenced operations. For stacking, sequenced operations reduced moving time by 33% (from 3 hours and 53 minutes to 2 hours and 36 minutes), energy consumption by 35%, and undercarriage travel by 46%. Similarly, for reclamation tasks, sequenced operations achieved an 18% reduction in moving time (from 1 hour and 28 minutes to 1 hour and 12 minutes), a 37% reduction in energy consumption, and a 10% reduction in undercarriage travel. Analysis of manual operations revealed inefficiencies, such as suboptimal push strategies and underutilized stockyard volume, highlighting the potential for improvement through automation. Furthermore, the stockyard model demonstrated its capability to accurately track and predict terrain geometry, providing the foundation for effective material movement planning and situational awareness.
The significance of this project is that it has demonstrated the technical viability of semi-autonomous bulldozing systems and their potential to improve operational efficiency while eliminating human exposure risks. Reducing energy consumption, travel distances, and moving times, supports the economic case for adoption of autonomous stockpile management.