Industrial robots are expensive. But, then, so are construction mistakes. Being off by an inch here or there adds up quickly, and too often crews need to correct costly errors. There’s a reason construction has become the next great target of the robotics and automation industries, with a number of startups vying to create solutions that can constantly monitor sites to detect mistakes before it’s too late.
TechCrunch’s Disrupt Berlin Battlefield winner Scaled Robotics this week is among the early-stage startups tackling the problem. This morning, the small Barcelona-based construction startup announced that it has raised a €2 million seed investment, led by European firms Norwegian Construct Venture and PropTech Fund Surplus. The funding follows a €1 million pre-seed.
Construction has become one of the key focuses of robotics investments in recent years, with names like Built, Toggle and Dusty raising rounds in the last year or so. Even Boston Dynamics is looking to get into the act, mounting lidar sensors to the top of its Spot robots, with construction listed as one of the primary use case for the commercialized version of the product.
Scaled’s robot is low to the ground, with four-wheels. Mounted up top are lasers and cameras that use SLAM technology to essentially build a 3D point map of a space. The map is then compared to a construction model of the space, and differences can be noted down to the centimeter. The robot’s mobility saves construction workers from having to lug around a tripod, as is the case with standard stationary laser scanners.
“The tools being developed by Scaled Robotics not only provide a detailed analysis of the state of a construction project but also provide a centralized repository for all information relating to project quality and progress,” co-founder and CEO Stuart Maggs said in a release tied to the funding. “We envision that our products will allow this global $13 trillion industry to manage risk and uncertainty in ways that were previously impossible. We are very pleased to hav