AI 기반 자율 이동 로봇 솔루션 (Scan&Go)

Surface processing and inspection of large composite structures such as aircraft and wind turbine blades are often performed manually. Existing automation requires pre-programming based on CAD drawings, which is difficult to handle with fixed equipment and takes a long time to prepare when there are many curved or non-standard shapes [1].

This results in slow production cycles and exposes workers to hazards such as noise, dust, and high-altitude work.

Scan&Go uses physical modeling AI and 3D vision technology to scan the shape of large parts in real time and automatically generate the optimal path. The robot plans its own trajectory without a separate CAD model or coding, and is mounted on an autonomous forklift to move around and position itself around the part, then performs polishing, grinding, and inspection.

It also features torque-sensing collision detection and a fence-less, top-level safety design (PL e, Cat 4) so it can work alongside humans. This readily deployable mobile automation flexibly handles a variety of large structure tasks without the setup time of traditional fixed robot equipment.

Large structure manufacturing and maintenance companies in aerospace, wind power, and construction are key customers. It is sold as a B2B industrial solution in the form of mobile deployment to necessary sites, rather than fixed installation on a production line. (Targeting private manufacturing companies rather than government/public, B2B)

It is not tied to specific national regulations and can be applied to large manufacturing sites globally. However, starting with specialized industries such as aircraft, it can be expanded to other large structure fields such as ships and railway vehicles in the future.

Industrial standards such as safety certifications must be met, but the need to replace dangerous human tasks is universally common, so there is great potential for market expansion.

It has been recognized for its technological completeness and ripple effect, earning a Best of Innovation Award in AI at CES [8]. It is a realistic example of automating large manufacturing processes and has received positive feedback as a "game changer."

However, in actual process applications, it is necessary to verify the response to various variables in the field and the maintenance system. Overall, it is highly rated for both technology and marketability, and wind power manufacturers are already paying attention to the quality and safety improvement effects.