I/UCRC Phase I: VT Site Addition to the Center for UAS

该项目将在弗吉尼亚理工大学增加一个I/UCRC站点，作为无人机系统中心（C-UAS）的新成员。C-UAS是目前唯一由NSF赞助的无人机研究中心。它成立于2012年，杨百翰大学作为牵头机构，UC-Boulder作为成员。VT教师将与杨百翰大学和CU分享管理研究项目的责任。VT网站的参与成员还将与现有网站的成员合作，以确保技术成功，并促进行业合作伙伴之间的多公司关系。 该网站将专注于以下应用领域：农业（例如，监测植物病原体运输的方法），民用基础设施（例如，桥梁检查）和地理信息系统。它将通过发展以下方面对社会产生影响：（1）竞争前的研究产品和知识产权，这将推动UAS技术的商业和安全，（2）课程改进，以支持学生在UAS领域从事工程职业的特殊技术培训需求，以及（3）一个多元化，紧密联系的前瞻性思维的人类网络，技术优秀的专业人士谁将发明未来的无人机商业部门。弗吉尼亚理工大学的教师将作出贡献的广泛领域（1）先进的飞行控制（例如，构造鲁棒的、安全的和数学认证的控制算法的方法），（2）适航性、网络安全性和可靠性（例如，包含小型UAS固有的不精确不确定性的可靠性预测方法）;（3）机器视觉和机器学习（例如，用于实时、机载智能图像处理的软件），（4）多物理场设计优化（例如，结合了非定常流体/结构相互作用的飞行器设计优化工具），和，和（5）无线通信（例如，使用软件定义的无线电和智能通信协议，使信息流适应环境条件）。

The project will add an I/UCRC site at Virginia Tech, as a new member of Center for Unmanned Aircraft Systems (C-UAS). C-UAS is currently the only NSF-sponsored unmanned aircraft research center. It was established in 2012, with BYU as the lead institution and UC-Boulder as a member. VT faculty will share responsibility for managing research projects with BYU and CU. The participating members of the VT site will also collaborate with members of the current sites to ensure technical success and to foster multi-corporate relationships among industry partners. The Site will focus on the following application areas: agriculture (e.g., methods to monitor plant pathogen transport), civil infrastructure (e.g., bridge inspection), and geographic information systems. It will make an impact on society by developing: (1) pre-competitive research products and intellectual property that will advance UAS technology for commerce and security, (2) curriculum enhancements to support the special technical training needs of students pursuing engineering careers in the UAS domain, and (3) a diverse, strongly connected human network of forward-thinking, technically excellent professionals who will invent the future for the UAS commercial sector.Virginia Tech faculty will make contributions in the broad areas of (1) advanced flight control (e.g., methods to construct robust, secure, and mathematically certified control algorithms), (2) airworthiness, cybersecurity, and reliability (e.g., reliability prediction methods that incorporate imprecise uncertainties intrinsic to small UAS); (3) machine vision and machine learning (e.g., software for real-time, on-board intelligent image processing), (4) multiphysics design optimization (e.g., aircraft design optimization tools that incorporate unsteady fluid/structure interaction), and , and (5) wireless communication (e.g., the use of software defined radio and intelligent communication protocols that adapt the information flow to the environmental conditions).