Perth-based startup Baraja has deployed its novel LiDAR technology in autonomous haul trucks operating at Pilbara iron ore mines, demonstrating that the company’s approach can enable autonomous mining equipment at substantially lower costs than conventional systems.

LiDAR, which stands for Light Detection and Ranging, uses laser pulses to map surroundings in three dimensions. Autonomous vehicles need LiDAR to navigate safely, but conventional systems cost $50,000-100,000 per vehicle. Baraja’s approach reduces costs to under $5,000 while maintaining the performance required for autonomous operation.

The technology uses a novel beam-steering method called Spectrum-Scan that eliminates expensive mechanical components found in conventional LiDAR. Instead of mechanically rotating mirrors or lasers, the system uses optics that can be manufactured using standard telecommunications industry processes.

Autonomous Mining Context

Mining companies have been deploying autonomous haul trucks for more than a decade, primarily in open-pit iron ore mines. The technology reduces labour costs, improves safety by removing operators from hazardous environments, and enables 24/7 operations without fatigue.

However, autonomous mining systems have been expensive to deploy. Each truck requires multiple sensors including LiDAR, radar, cameras, and GPS, along with computing hardware and communications systems. Total system costs per vehicle can reach $500,000-800,000.

These costs are justified for large mining operations where hundreds of trucks operate. But smaller mines and other applications can’t justify the expense. Lower-cost sensors could expand autonomous vehicle deployment across mining and other industries.

Rio Tinto operates the largest autonomous truck fleet globally, with more than 400 trucks in the Pilbara. BHP and Fortescue Metals Group also run autonomous fleets. These fleets have demonstrated that the technology works reliably and delivers economic benefits.

Expanding autonomous operations to other equipment like excavators, bulldozers, and auxiliary vehicles requires similar sensing capabilities but at lower cost points that justify retrofitting existing equipment.

Baraja’s Technical Approach

Conventional rotating LiDAR systems use motors to spin mirrors or the entire sensor head, sweeping laser beams across the field of view. These mechanical systems are expensive to manufacture and vulnerable to failure from shock and vibration.

Solid-state LiDAR eliminates moving parts but typically has limited field of view or range. Various solid-state approaches use different beam-steering methods, but most involve trade-offs between performance and cost.

Baraja’s Spectrum-Scan approach uses prisms and other optical components to steer beams by changing the wavelength of laser light. Different wavelengths refract at slightly different angles, allowing electronic control of beam direction without mechanical motion.

The system generates multiple wavelengths simultaneously, then separates them optically to create multiple parallel beams scanning different parts of the field of view. This provides the wide field of view and long range needed for autonomous vehicles without expensive mechanical components.

Key components come from the telecommunications industry, where similar technologies are used for wavelength-division multiplexing in fibre optic networks. This means established supply chains and manufacturing processes exist, enabling low-cost production.

Dr Federico Collarte, Baraja’s co-founder and CTO, said the inspiration came from telecommunications where steering light beams through wavelength control is routine. Applying that approach to LiDAR required solving several technical challenges but offered a path to much lower costs.

Mining Deployment

The Pilbara deployment involved months of testing in progressively more challenging conditions. Initial trials used Baraja LiDAR on manually operated trucks to validate performance. Then limited autonomous operation with human oversight confirmed the system could handle real mining conditions.

Full autonomous operation began earlier this year on a subset of Rio Tinto’s fleet. The trucks navigate haul roads, avoid obstacles, and coordinate with other autonomous equipment using the Baraja sensors as their primary environment perception system.

Performance has matched conventional LiDAR systems in detection range, resolution, and reliability. The Baraja sensors detect obstacles and map terrain with sufficient accuracy for safe autonomous operation at the 60-kilometer-per-hour speeds typical on mine haul roads.

Durability in mining conditions is critical. Dust, vibration, temperature extremes, and occasional collisions all challenge sensor reliability. The Baraja systems are sealed and ruggedised to withstand these conditions. Early field experience shows reliability comparable to or better than mechanical LiDAR.

Cost reduction enables different economic calculations for autonomous equipment. Retrofitting existing fleets becomes more attractive when sensor costs fall. Deploying autonomy on lower-utilisation equipment that wouldn’t justify expensive systems becomes feasible.

Broader Applications

While mining provides initial commercial traction, Baraja is pursuing applications in automotive, logistics, agriculture, and construction. Each market has different requirements and cost sensitivities, but all need affordable, reliable LiDAR for autonomous operation.

Automotive has been the holy grail for LiDAR companies because production volumes are enormous if the technology becomes standard in consumer vehicles. However, automotive demands even lower costs than mining, typically targeting $500-1000 per vehicle.

Baraja’s approach could potentially reach automotive cost targets at high volume, though achieving automotive qualification and safety certification requires substantial additional development. The company is working with automotive partners on that pathway.

Agriculture represents a nearer-term opportunity. Autonomous tractors and harvesters can improve farming efficiency and address agricultural labour shortages. LiDAR costs of a few thousand dollars per vehicle are acceptable for high-value agricultural equipment.

Construction equipment automation faces similar opportunities and challenges to mining. The equipment is expensive and benefits from automation, but operations are less structured than mining, creating harder autonomy challenges.

Australian Technology Ecosystem

Baraja’s development reflects Australia’s strengths in optics, telecommunications, and mining technology. The company was founded by researchers from the Australian National University and Macquarie University who developed the core technology through university research.

Venture capital funding came from Australian and international investors attracted by the technology’s potential and the founding team’s expertise. The company has raised approximately $100 million across several funding rounds.

Manufacturing happens both in Australia and Asia. Specialised optical components are produced in Australia, while higher-volume electronic and mechanical components use Asian suppliers. This balances Australian manufacturing capability with cost-competitive volume production.

The company employs about 150 people, primarily engineers and technicians, split between Perth and Sydney offices. Finding qualified staff with expertise in optics, laser systems, and automotive engineering has been challenging given Australia’s relatively small pool of specialists in these areas.

Government support through R&D tax incentives and grants from agencies like Austrade and ARENA has contributed to development funding. These programs help early-stage companies bridge the funding gap between initial technology development and commercial revenue.

Commercialisation Challenges

Transitioning from technology demonstration to commercial production involves substantial challenges. Manufacturing processes must be refined to produce sensors reliably at scale. Quality control, supply chain management, and logistics all require attention.

Customer qualification processes vary by industry. Mining customers require field trials demonstrating reliability and performance. Automotive customers have extensive testing and certification requirements that take years to complete.

Competition is intense. Dozens of companies worldwide are developing LiDAR technologies using various approaches. Some have raised billions in funding and gone public through SPAC mergers. Others have failed and shut down. The LiDAR market remains unsettled.

Baraja differentiates on cost and performance combination. Pure solid-state approaches often can’t match the range and field of view needed for high-speed autonomous vehicles. Mechanical systems have better performance but higher costs. Baraja aims to offer the best of both.

Whether this positioning succeeds commercially depends on execution and market evolution. If autonomous vehicles deploy widely, demand for LiDAR will be enormous and multiple technologies may succeed. If deployment is slower, only the strongest companies will survive.

Impact on Autonomous Systems

Reducing LiDAR costs by an order of magnitude could accelerate autonomous vehicle deployment across industries. Many applications make economic sense with $5,000 sensors that don’t work with $50,000 sensors.

For mining specifically, lower sensor costs enable autonomous operation of more equipment types and in smaller operations. Autonomous drilling rigs, dozing, and auxiliary equipment all become more attractive when sensing costs fall.

The technology could also enable new safety systems on manually operated equipment. Collision avoidance and operator assistance features use similar sensing but can justify lower costs than full autonomy. These features improve safety even before full autonomy is achieved.

International mining companies are watching Australian autonomous mining developments closely. Technology and operational practices developed in Australian mines are being deployed globally. Baraja’s technology could follow that pathway.

The company’s success would validate Australia’s capability in deep-technology commercialisation. The country has strong research but historically struggles to build large technology companies. Baraja is attempting to buck that trend in a capital-intensive, globally competitive sector.

For now, the Pilbara deployment demonstrates that the technology works in demanding real-world conditions. Whether Baraja becomes a major autonomous vehicle sensor supplier or remains a smaller player serving specific niches depends on execution over the next few years. The technical foundations look solid. Commercial success requires navigating manufacturing scale-up, customer diversification, and intense competition.