SBIR Phase I: Aerial manipulation platform for rebar tying

SBIR一期项目的更广泛影响/商业潜力是减少建造混凝土桥梁所需的时间，提高与钢筋安装有关的工地安全，降低与桥梁建设有关的成本，并改善钢铁工人的整体健康状况。在工业职业中，钢铁工人面临的软组织损伤率是最高的，因为他们需要经常弯腰绑钢筋。此外，通过减少桥梁所需的施工时间，社区将能够以更快的速度从自然灾害中恢复过来。这些好处将通过自动化绑筋过程来实现。由于绑扎过程的高度重复性，建筑行业的劳动力短缺，以及钢筋绑扎通常位于混凝土浇筑的关键路径上，这一点很重要。为了实现钢筋捆扎自动化，将开发一种集成捆扎工具、专用飞行控制和导航系统的无人机平台。这将使小型无人机系统（sUAS）从观察角色转变为操作平台。在未来10年里，桥梁上的钢筋捆绑市场将达到2.75亿美元，而在美国，每年的一般建筑市场将达到17亿美元。这个小型企业创新研究（SBIR）第一阶段项目旨在开发一种能够在室外固定装置上捆绑混凝土结构钢筋的sUAS。如果项目成功，这将是第一个商业上可行的空中操纵系统。将采用计算机视觉、机器学习和传感器融合技术的组合来开发一种自主系统，使无人机能够以高水平的精度识别、着陆和捆绑，从而实现钢筋捆绑。将对自动驾驶仪进行修改，使无人机能够触发集成螺纹钢工具。计算机视觉算法将被优化并移植到sUAS上运行。将开发一种视觉伺服控制系统，利用机载计算机视觉算法和其他传感器精确跟踪和降落在钢筋交叉路口。高水平的挑战包括揭示现有钢筋工具对不精确着陆的鲁棒性，提高着陆精度，提高钢筋相交检测的精度，以及无人机的视觉伺服。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识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 SBIR Phase I project is to reduce the time required to construct concrete bridges, improve job site safety relating to rebar installation, reduce costs related to bridge construction, and improve the overall health of ironworkers. Ironworkers face some of the highest rates soft tissue damage of the industrial occupations due the constant bending over required to tie rebar. Additionally, by reducing the required construction time for bridges, communities will be able to recover from natural disasters at a faster pace. These benefits will be accomplished by automating the process of the tying rebar. This is significant due to the highly repetitive nature of the tying process, the labor shortages in the construction industry, and the fact that rebar tying often sits on the critical path of a concrete pour. To automate rebar tying, a drone platform with an integrated tie tool, specialized flight controls, and navigation system will be developed. This will take small unmanned aircraft systems (sUAS) from observation roles to a manipulation platform. Rebar tying on bridges represents a $275 million market over the next 10 years and $1.7 billion market annually across the USA for the larger general construction market. This Small Business Innovation Research (SBIR) Phase I project aims to develop a sUAS capable of tying the rebar for concrete construction on an outdoor fixture. This will represent the first commercially viable aerial manipulation system if the project is successful. A combination of computer vision, machine learning, and sensor fusion techniques will be employed to develop an autonomous system capable of allowing a drone to identify, land and tie with a high level of accuracy to enable rebar tying. Modification to the autopilot will be conducted to allow the drone to trigger the integrated rebar tool. Computer vision algorithms will be optimized and ported to run onboard the sUAS. A control system for visual servo-ing will be developed to utilize onboard computer vision algorithms and other sensors to track and land on rebar intersections accurately. High-level challenges consist of uncovering the robustness of existing rebar tool towards imprecise landing, improving landing precision, improving accuracy of rebar intersection detection, and visual servo-ing of the drone.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.