SBIR Phase II: Robotic Pavement Marking

Roadprintz, Inc., Cleveland OH

Investigators

Abstract

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project seeks to significantly reduce the number of injuries or deaths suffered by pavement marking workers on public streets and roads, while increasing the efficiency and lowering costs by using an operator-driven, truck-mounted, mobile robotic pavement marking system. Robotic painting allows workers to remain in the safety of the truck cab, protecting them from traffic. Increased efficiency will help to minimize work zone congestion and duration, while reduced costs will enable safer and more productive streetscapes for the general public. Studies have repeatedly confirmed that streetscapes with a high density of transverse marking features such as shared center turning lanes, dedicated bike lanes with separation buffer zones, and pedestrian-friendly crosswalks result in measurable public health benefits, enhanced quality of life, increased commercial activity, and, counterintuitively, more efficient traffic flow with fewer collisions. The project will also better enable precise placement of new markings and precise overpainting of worn or eroded markings, both important factors for connected and autonomous vehicles. An earlier phase of the project has already resulted in enhancements to camera calibration techniques for machine vision. This Small Business Innovation Research (SBIR) Phase II project seeks to build on a successful Phase I effort to enable a commercially-viable robotic road painting system that addresses the need to protect the lives of human workers engaged in the dangerous task of stenciling roadway markings such as turn arrows or bike symbols. The project may also improve the accuracy and ease of application of markings needed to support autonomous vehicle-ready roadways. The research will apply advanced software, machine vision, an augmented-reality user interface, and proprietary robotic trajectory planning technologies to support ultraprecision painting in the real-world environment while keeping human workers safe in the cab of the operator-driven truck upon which the robot is mounted. The solution will enable robots to paint previously unmarked pavement and to repaint existing marks. The combined hardware and software solution will be capable of recording all planned and completed markings in a cloud-hosted database—an innovation that will streamline the processes. The technology will also form a cloud-based standard for mapping and recording universal road markings and support the ability of autonomous vehicles to operate safely. While much of the research can initially be performed in simulation, significant real-world testing and validation will be required as well. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.