STTR Phase I: Autonomous Landing of sUAS onto Moving Platforms

STTR 第一阶段：小型无人机在移动平台上自主着陆

• 批准号: 1648563
• 负责人: Gaemus Collins
• 金额: $ 22.49万
• 依托单位: Planck Aerosystems Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2018-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1648563&HistoricalAwards=false
• 关键词: STTR; Phase; Autonomous; Landing; sUAS

The broader impact/commercial potential of this project is the expansion of autonomous unmanned aerial systems (UAS, or drones) to new maritime operational environments and commercial markets. The proposed technology will enable small UAS to operate from vessels moving at sea, without the need for a dedicated pilot or installed hardware, even while far from shore and beyond the reach of established communication networks. Small UAS can offer the same aerial perspective provided by manned helicopters at a fraction of the size, cost, and risk. Real-time aerial imagery from UAS will supply maritime operators with invaluable information about their surroundings at sea, which is not available by any other means. This information is critical for many maritime applications, including fishing, ocean monitoring, scientific exploration, maritime surveillance, and search, and rescue. This information will offer a particularly large and immediate impact for 98% of worldwide commercial fishing vessels (those that do not carry embarked manned helicopters for fish-finding) by dramatically reducing their fuel costs; providing net economic and environmental gains for the industry.This Small Business Technology Transfer (STTR) Phase I project will develop algorithms and software to enable small UAS autonomously and reliably land onto a moving platform at sea. Commercially-available small UAS can offer invaluable real-time aerial imagery for maritime operators. But, this technology is not currently in widespread use due to technological barriers. In particular, the key enabling technology is the ability to autonomously and reliably land a small UAS onto a moving platform. The research objective is to develop algorithms and software that enable small UAS to autonomously operate from moving vessels at sea. Computer vision algorithms automatically detect the host vessel and the dedicated landing area. Data fusion algorithms estimate the relative drone-boat position and orientation in real time, including compensation for vessel roll, pitch, & heave. Precision control algorithms optimize the drone?s trajectory for save, reliable, autonomous launch and landing. A prototype system will be built by integrating the STTR-developed software with commercially available hardware components.

该项目更广泛的影响/商业潜力是将自主无人机系统（UAS或无人机）扩展到新的海上作业环境和商业市场。拟议的技术将使小型无人机系统能够在海上移动的船只上运行，而不需要专门的飞行员或安装硬件，即使远离海岸和现有通信网络的范围。小型无人机系统可以提供与有人驾驶直升机相同的空中视角，其尺寸、成本和风险都是无人机的一小部分。UAS的实时航拍图像将为海事运营商提供有关其海上环境的宝贵信息，这是任何其他手段都无法获得的。这些信息对于许多海事应用至关重要，包括捕鱼、海洋监测、科学探索、海上监视以及搜索和救援。这些信息将对全球98%的商业渔船产生特别大的直接影响（那些不携带载人直升机进行探鱼的国家）大幅降低燃料成本;为行业提供净经济和环境收益。小企业技术转让（STTR）第一阶段项目将开发算法和软件，使小型UAS能够自主可靠地降落到海上移动平台上。商用小型无人机系统可以为海上运营商提供宝贵的实时航空图像。但是，由于技术障碍，这项技术目前还没有广泛使用。特别是，关键的使能技术是能够自主可靠地将小型UAS降落到移动平台上。研究目标是开发算法和软件，使小型UAS能够在海上移动船只上自主操作。计算机视觉算法自动检测宿主船只和专用着陆区。数据融合算法能够真实的估计无人艇的相对位置和方位，包括对船舶横摇、纵摇、升沉的补偿。精确控制算法优化无人机？的轨迹保存，可靠，自主发射和着陆。将通过将STTR开发的软件与商业上可获得的硬件组件相结合来建立一个原型系统。