Harnessing the Power of Autonomous Mining Systems

As underground mines push towards greater autonomy, the spotlight is on the power of simulation and optimization. While a fully autonomous mine is often hailed as the holy grail of modern mining—promising unmatched safety, efficiency, and technological prowess it"s not a one-size-fits-all solution. In fact, full autonomy may not be ideal for every operation or machine, and, in some cases, it could even become a hindrance rather than a help.

To make the most of autonomy, mining companies need to take a smart, data-driven approach. Instead of diving headfirst into automation, they must carefully evaluate the benefits and limitations of autonomous systems. By aligning technology with their specific business goals and operational realities, miners can ensure that capital and resources are invested wisely—maximizing ROI while setting themselves up for long-term success. After all, it"s about the right technology at the right time, not just chasing the buzzword of "autonomy."

Scaling the Automation Maturity Framework

The mining industry has been tapping into autonomous technology for underground equipment for years, and it's clear that automation is a game-changer. As the Global Mining Guidelines (GMG) highlights in its 2019 report, automation is not just about boosting safety and productivity—it's a key to making mining methods more sustainable. Successful implementation of autonomous systems can improve safety, ramp up efficiency, and cut maintenance costs.

But while mining has embraced lower levels of autonomy, such as tele-remote or semi-autonomous assist functions, shifting to highly autonomous machines and fleets that can self-deploy and adapt in real-time is a whole new challenge. According to Steven Donaldson, Co-founder of Polymathian, mines are still figuring out how to truly optimize these advanced systems.

Currently, most mines using autonomy fall into levels 1 or 2 on the GMG’s Mining Automation Maturity Model. At level 1 (assistance), machines have specific automated features, but the operator retains control. At level 2 (semi-autonomous), the system handles certain tasks autonomously within predefined limits, though human oversight is still required. The leap to fully autonomous fleets is a major step—and one that requires careful optimization and planning.

Level 3 systems (conditionally autonomous) can complete operations autonomously in a designated area. They have situational awareness capabilities and enter a halted state when intervention is needed.

Level 4 (highly autonomous) systems can complete sustained operations autonomously in a designated area and have situation-awareness capabilities. These systems can intervene in minimal risk situations and enter a halted state in higher risk situations. Supervisors can also request the system to disengage.

At level 5 (fully autonomous), the system can complete sustained operations autonomously with or without a designated autonomous area, has situational awareness capabilities and can intervene in minimal risk situations. In high-risk situations, it enters a halted state and the supervisor can request disengagement.In mining, level 4 and 5 operations are exceptions today. To achieve this degree of automation requires more than just autonomous equipment, the systems and decision-making processes supporting the operation must also be automated.

Under Rio Tinto’s ownership, Northparkes mine (now owned by CMOC) in Australia became the first fully autonomous underground mine in 2015, with Resolute Mining’s Syama operation in Mali becoming the first autonomous mine with truck haulage in 2018. Despite nearly a decade having passed since the first implementation, there are still relatively few examples of highly autonomous underground mining operations. This speaks volumes about the level of change required to see the full benefit of these technologies.

As with any nascent concept, there have been successes but also failures amongst the first adopters. Today, there are mathematical optimisation and simulation technologies which can help mines determine the level of autonomy and the implementation approach that best suits their business. Leaning on these will help to de-risk deployments and build a stronger case for the introduction or expansion of autonomous operations.

Leveraging Automation Simulations to Drive Success

When implementing autonomy in mining, companies must assess how it aligns with their broader goals, strengths, and limitations, and determine how success will be measured. Achieving a balance between productivity and safety is key. While autonomous systems can boost long-term productivity by ensuring consistent performance and fleet predictability, they may initially be slower than human-operated equipment, potentially reducing short-term productivity.

The value of automation also depends on its application within the operation. For instance, automating remote development areas offers different benefits compared to automating production trucks. Colin Eustace, Head of Simulation at Polymathian, notes that while many view level 5 autonomy as the ultimate goal, levels 2 or 3 may better align with some companies' objectives. It might also be wise to maintain manual operations for certain equipment or apply varying levels of autonomy across different parts of the mine.

Simulation plays a critical role in testing the feasibility of automation and modeling changes over time, helping companies make data-driven decisions about when and how to implement autonomous systems effectively.

Automating Decision-Making for Enhanced Autonomy

At higher levels of automation, success depends on taking a holistic approach, considering the impact on all aspects of the mine’s systems and processes—such as design, KPIs, schedules, haulage routes, and roles—over different time horizons. Optimizing automation is easier at greenfield sites, but retrofitting autonomous equipment into existing brownfield sites typically leads to sub-optimal returns on investment (ROI). Implementing automation requires more than just swapping manned equipment for autonomous units; it often involves significant changes in processes and planning.

Automating decision-making, especially in areas like scheduling and dispatching, can create more dynamic, responsive systems that never tire or make errors, ensuring optimal performance even as business priorities shift. While automated decision-making can function independently of autonomous equipment, combining both leads to a highly efficient, adaptive operation.

A case study from Polymathian illustrates this approach: in 2014, the company developed an optimization tool called ORB for Newcrest’s Cadia mine in Australia. The tool autonomously orchestrated the mine’s loaders based on real-time cave data, improving productivity by 20%. This success helped integrate autonomous loaders into Cadia’s fleet, demonstrating that automation and optimization can significantly boost efficiency and profitability from the start.

By automating and optimizing planning processes, mines can overcome potential productivity losses and unlock the full value of autonomous operations.

Expanding Automation Benefits Across the Value Chain

The blog highlights how simulation and optimisation tools, traditionally used for strategic analysis in mining, are now essential for enabling autonomous mining operations through automated decision-making. These tools are critical in unlocking the full benefits of autonomy, as early use cases show that without them, mines struggle to realise these advantages. As more companies recognise their value, these tools will become more widely adopted. Looking ahead, simulation and optimisation will play a key role in improving efficiency across mining value chains, particularly in supplying metals and minerals vital for the energy transition.