Europe's ambitious underground infrastructure projects, from the Mediterranean Corridor in Spain to the Brenner Base Tunnel in the Alps, are driving a shift toward autonomous machinery and drones to reduce human risk and carbon emissions. The construction sector accounts for nearly 40% of global energy-related CO₂ emissions, and a single major tunnel can produce emissions equivalent to hundreds of thousands of intercontinental flights. The BEEYONDERS project, led by Spanish partners, is testing a new ecosystem of autonomous loaders and drones to address both safety and environmental challenges.
Removing the Human Factor from Hazardous Zones
After a tunnel blast, visibility drops to near zero and unexploded charges pose a constant threat. The BEEYONDERS project removes workers from this danger zone by deploying autonomous machines. The pilot site is the Fundación Santa Bárbara in Ribera de Folgoso, a former quarry in north-western Spain now repurposed as a training and simulation facility. This controlled environment allows engineers to refine autonomous systems under realistic conditions before they are used in active projects.
“We employ drones in two distinct stages,” explains Marco Montes Grova, Perception and AI Engineer at CATEC. “First, they map the tunnel to create a digital twin. Then, after the detonation, the drone serves as the loader’s eyes, guiding it through the smoke to the excavation front. Its thermal camera can even detect unexploded ordnance.”
This synergy not only saves lives but also optimises the construction cycle. By allowing machinery to enter the tunnel immediately after a blast—while the air is still too thick for humans—the technology significantly reduces downtime. The same autonomous loader technology is being adapted for surface works, with recent testing at a pilot site along the Rome–L’Aquila highway in Italy.
Digital Twins and Real-Time Safety
At the Italian site, the autonomous loader operates within a sophisticated digital ecosystem. Drones perform photogrammetry to map the site and define the machine’s optimal path, while workers wear RTK sensors that allow the loader to identify and avoid personnel in real time, automatically stopping to ensure 100% safety. “This mechanical loader, converted to autonomous driving, can cut fuel use by about 10% in a sector as energy-intensive as road construction,” says Fabrizio Federici, Project Manager at AISCAT Servizi. “The information collected allows the digital twin to calculate the best and safest route, directly reducing environmental pollution.”
The project’s focus on digital twins and real-time data aligns with broader European efforts to integrate AI and automation into critical infrastructure. For instance, Airbus and BMW have partnered with French AI startup Mistral to develop similar technologies for defence and safety systems, underscoring the continent’s push toward autonomous solutions.
As Europe continues to invest in large-scale tunnels and highways, the BEEYONDERS project demonstrates that autonomous machines can simultaneously improve worker safety and reduce environmental impact. The technology is poised to become a standard in the construction industry, offering a blueprint for how to build the continent’s future infrastructure with fewer risks and lower emissions.
