The broader impact/commercial potential of this project stems from its ability to transform excavator operation and control from a primarily skill-based activity to a knowledge-based practice, leading to significant increases in productivity and safety. This is turn will help realize enormous cost savings and reduction of potential hazards to the public, improve competitiveness of U.S. construction industry, and reduce life cycle costs of civil infrastructure. Such benefits will also accrue in fields such as manufacturing, transportation, mining, and ship-building where the transition from skill-based to knowledge-based processes is of value. In the long-run, the accidental utility strike warning capabilities of the proposed solution also have the potential to save millions of dollars in liability and opportunity costs for customers, and avoid disruptions to life and commerce, and prevent physical danger to workers and the public. The societal benefit and commercial impact of the project are thus expected to be the significant reductions in construction and underground infrastructure costs that will be possible through safe and efficient excavation.
This Small Business Innovation Research (SBIR) Phase I project will translate fundamental computer-vision based motion-tracking technology into a transformative solution for tracking an excavator end-effector (bucket) in field conditions, and demonstrate the capability to meet target market performance demands. Excavation is a quintessential construction activity where every operator faces two major problems: 1) The need to maintain precise grade control; and 2) The need to avoid accidental utility strikes. This project will overcome these pain points and present operators with a visualization of excavation job plans, target grade profiles, and the evolving grade profile in real-time using augmented reality, allowing operators to achieve target grades with high precision, improved productivity, and safety. The disruptive innovation is the use of inexpensive computer-vision based tracking to: 1) track the position of an excavator bucket in a local coordinate system; and 2) track the position of a cabin-mounted camera in the same coordinate system to visualize buried utility locations in augmented reality. Extensive field testing has demonstrated that this technology tracks markers relative to a camera with an uncertainty of less than one inch, offering significant advantages over current global positioning system based methods that are expensive and unreliable for the pursued application.
