SBIR Phase II: Robotic Pavement Marking

该小企业创新研究 (SBIR) 第二阶段项目的更广泛影响/商业潜力旨在显着减少公共街道和道路上路面标线工人遭受的伤害或死亡人数，同时通过使用操作员驱动、卡车安装的移动机器人路面标线系统来提高效率并降低成本。机器人喷漆可以让工人留在卡车驾驶室的安全范围内，保护他们免受交通堵塞。 效率的提高将有助于最大限度地减少工作区的拥堵和持续时间，而成本的降低将为公众带来更安全、更高效的街景。研究一再证实，具有高密度横向标记特征的街道景观，例如共享中心转弯车道、带分隔缓冲区的专用自行车道和行人友好型人行横道，可以带来可衡量的公共卫生效益，提高生活质量，增加商业活动，并且与直觉相反，交通流量更高效，碰撞更少。该项目还将更好地实现新标记的精确放置以及磨损或侵蚀标记的精确复涂，这都是联网和自动驾驶车辆的重要因素。该项目的早期阶段已经增强了机器视觉相机校准技术。这个小型企业创新研究（SBIR）第二阶段项目旨在以第一阶段成功的努力为基础，实现商业上可行的机器人道路涂漆系统，满足保护从事危险任务的工人的生命安全的需要，这些工人从事对道路标记（如转向箭头或自行车符号）进行模板印刷的危险任务。该项目还可以提高支持自动驾驶车辆道路所需标记的准确性和易用性。该研究将应用先进的软件、机器视觉、增强现实用户界面和专有的机器人轨迹规划技术来支持现实环境中的超精密喷漆，同时保证安装机器人的操作员驾驶卡车驾驶室中人类工人的安全。该解决方案将使机器人能够绘制以前未标记的路面并重新绘制现有标记。组合的硬件和软件解决方案将能够在云托管数据库中记录所有计划和已完成的标记——这一创新将简化流程。 该技术还将形成基于云的标准，用于绘制和记录通用道路标记，并支持自动驾驶车辆安全运行的能力。虽然大部分研究最初可以在模拟中进行，但也需要进行重要的现实世界测试和验证。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.