1. Principles

The effectiveness of fleet management systems is driven by the integration of several principles, including those derived from operational research and interoperability. These disciplines help in systematically improving the coordination of resources and identifying bottlenecks that affect the flow of operations.

1. Operational Research and Optimization:

• Operational research involves using advanced analytical methods to make better decisions. In fleet management for AHS, it enables the optimization of routes, resource allocation, and the timing of vehicle operations. By modeling the operation of autonomous haulage systems and analyzing real-time data, fleet management systems can predict and mitigate inefficiencies, ensure optimal vehicle utilization, and improve the overall productivity of the mine.
• Techniques such as linear programming, queuing theory, and simulation are applied to allocate resources efficiently, reducing idle time, managing traffic flow, and optimizing the usage of limited space in underground environments.

1. Interoperability:

• Interoperability refers to the ability of different systems—such as FMS and AHS from different vendors—to work seamlessly together. Without interoperability, fleet management systems face challenges in data exchange, communication, and coordinating actions between autonomous vehicles and other equipment.

2. Mechanisms

Software

• Fleet Management Software:
  • Centralized Control: The FMS software provides a centralized platform for overseeing all autonomous and human-operated vehicles. It continuously monitors vehicle positions, speed, and status, ensuring that they operate according to the mine's requirements.
  • Route Planning and Optimization: Fleet management software dynamically assigns routes to vehicles based on real-time conditions. It ensures that vehicles take the most efficient paths while avoiding areas of congestion or hazards.
  • Traffic Management: In underground mines, traffic management is particularly challenging. The software coordinates vehicle movements to avoid bottlenecks, ensuring that multiple vehicles can operate in close proximity without causing traffic jams.
• Autonomous Vehicle Management Algorithms:
  • Pathfinding Algorithms: These algorithms help vehicles navigate complex tunnel systems by analyzing current conditions and finding the safest and most efficient route. These algorithms must account for both static obstacles (such as walls or equipment) and dynamic obstacles (such as other vehicles or personnel).
  • Predictive Maintenance Scheduling: The FMS software monitors the condition of each vehicle and schedules maintenance when necessary, ensuring that vehicles are not taken out of service unexpectedly.
  • Task Assignment and Resource Optimization: Fleet management software assigns tasks (e.g., hauling material, loading, or dumping) to vehicles based on their proximity to loading/unloading points, their load capacity, and real-time demand.
• Safety Systems Integration:
  • Collision Avoidance: The FMS software integrates collision avoidance algorithms that analyze sensor data to predict potential collisions and take corrective actions, such as slowing down or rerouting vehicles.
  • Emergency Stop Systems (E-Stops): In case of a critical failure or emergency, FMS can send stop commands to vehicles, halting them immediately to avoid accidents.

3. Challenges

Interoperability Challenges Between FMS and AHS