HIGH PROFILE
Founder and CEO Bibhrajit Halder was part of the initial Caterpillar autonomy program , before moving to Ford where he worked on self-driving cars , and Apple ’ s self-driving team , until he set up SafeAI in Silicon Valley .” Other SafeAI staff have similar relevant experience , so it is understandable that the team is confident it can bring back some of these insights into the mining world .
So who is the SafeAI target customer in mining and what has been done so far ? Welford states : “ We have only been going for three years , but we are already moving from proof-of-concept stage to the first onsite deployments of our task-specific autonomy platform at active mine sites . We have automated an ADT — the Caterpillar 725 working with Japanese construction company and SafeAI partner Obayashi – as well as a Bobcat skid steer and several light vehicles .”
Proof-of-concept trials have taken place at SafeAI ’ s own test site in Silicon Valley , a closed copper mine and a US quarry site . In Australia , its first proof-of-concept will begin in 2021 . Welford says : “ Australia represents a huge market opportunity for us – not just in the mid-tier owneroperators , but also from the perspective of the mining contractors . These customers are calling for a more flexible autonomous solution than is currently available , not only for the system itself , but also the application model .”
What makes SafeAI ’ s offering different ?
Crucially , to be able to operate in such a diverse market of large and small miners , across a range of equipment types and sizes , SafeAI ’ s system – both hardware and software – is designed to be interoperable with other OEM systems , both onboard and in terms of FMS . Its Application Programming Interfaces ( APIs ) are fully open and transparent . This makes for a highly flexible solution based on what the customer has in terms of machine types and models , but also whether it is running Wenco , Hexagon , Zyfra , Modular or some other FMS – being FMS agnostic remains a significant challenge for autonomy today , but it can be done . For smaller operators , SafeAI ’ s AIbased software can also be utilised to supply FMS functions such as machine coordination and truck dispatch assignment . Prusinski adds : “ We are also an open system . Customers have full access to system generated data . Increasingly mining companies are data driven so providing this important data to help their business can only help the industry .”
The power of onboard processing and AI
The latest explosion in on-board processing capabilities , powered by GPU and TPU from companies like NVIDIA and Google , unlocked huge potential for autonomous use cases . SafeAI utilises this advanced on-board processing power to enable Its core IP , which is the on-board autonomous software , to address multiple use cases that are critical to accelerate autonomy in mining .
Welford told IM : “ One example is that in the incumbent systems , false positive obstacle detections are a known problem to efficient autonomous operations . Like SafeAI , these systems use truck mounted sensors as part of their safety critical systems , and when these sensors detect an obstacle , such as an unexpected light-vehicle in front , the truck will stop . However , when the vehicle stops , it has no way to classify the obstacle detected , so it waits to be cleared , in the field , often by a human operator who manually inspects the vehicle and determines the obstacle type and status . The trouble is compounded by the number of other objects that the sensors detect as well ; these might be small clouds of dust , rocks , birds , or a number of other objects which don ’ t pose a risk to the vehicle .”
Prusinski adds : “ The current systems in mining are in place and working but that does not mean things have to stand still ; with AI they could be even more productive . SafeAI ’ s technology means the truck is able , in some circumstances , to take action when needed . Autonomy 2.0 incorporates artificial intelligence , enabling it to detect , classify and track obstacles in unstructured environments , coupled with data fusion and advanced predictive algorithms which allow the system to detect and
avoid obstacles in a safe and productive manner . Moreover , the method of in-pit obstacle clearance entails more people in the autonomous zone , something which is best avoided .”
There are some application caveats . The focus for SafeAI remains on variations of haulage ie , taking material from A to B , as once you get into some other equipment types it gets very complex . Taking wheel loaders as an example – they do a lot of fine-tuned work in a variety of use cases – with wheel loaders around stockpiles for example . You need 100 % utilisation in one or two of those cases to make it worthwhile automating . Utilisation rates also affect whether haulage units are worth automating . Welford comments : “ We work with smaller miners and contractors to understand how much of their fleet they need to automate and the level of equipment utilisation required to make it a worthwhile return on investment . This might make the jump to autonomy harder for some operations , for example those operating over 12 hours out of 24 .”
So where to from here for SafeAI ? Welford concludes : “ We are based in the US are hiring aggressively to build up our Perth office , and have a presence in Canada ; so North America and Australia remain the key markets for our mining focus for now . Of course , there is also a big construction and mining market in Asia , and given SafeAI ’ s strategic investor Obayashi that is also on our radar . But we are not restricted to these regions – our aim is to get as many initial sites on the board as we can , with a variety of use cases , operator types , mine types and vehicle types . That way we can showcase the true power but also the flexibility of our system versus what is currently commercially available .” IM
AUGUST 2021 | International Mining 115