NRI: Enhancing Mapping Capabilities of Underwater Caves using Robotic Assistive Technology

NRI：利用机器人辅助技术增强水下洞穴的测绘能力

• 批准号: 1637876
• 负责人: Ioannis Rekleitis
• 金额: $ 52.64万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-11-01 至 2019-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637876&HistoricalAwards=false
• 关键词: NRI; Enhancing; Mapping; Capabilities; Underwater; NRI; Enhancing; Mapping; Capabilities; Underwater

This project develops robotic assistive technologies to improve mapping capabilities of underwater caves. The project enables the practical construction of accurate volumetric models for water-filled caves. The technology of this robotic system can also be deployed on underwater vehicles enabling the autonomous exploration of caves and other underwater structures. The developed techniques can also be used in some applications of aerial and ground vehicles. Collected data from field deployments of the developed sensor are made available to the wider robotic, geological, and speleological research community through public-domain releases in order to further innovation. Furthermore, the data and software, released under an open-source license, enable researchers to test algorithms on computer vision, state estimation, and sensor fusion, in challenging environments. The project integrates research and education through training graduate and undergraduate students and enhancing several graduate and undergraduate courses at the University of South Carolina. The project also engages undergraduate students from Benedict College, a Historically Black College or University (HBCU). The collected data are used in outreach activities to recruit high-school students of the greater Columbia area in STEM education, engaging students and educators, particularly in underserved communities.This research develops 3D reconstruction algorithms utilizing the environmental characteristic of a cave system. The research team studies robotic technologies for sensor fusion of multiple data streams in a single unit and validates experimentally the developed system via extensive testing in underwater cave explorations in collaboration with expert cave divers. The project introduces robotic technology to the underwater cave explorer community by capitalizing on existing practices in three steps: (a) deploying stereo cameras to be used in conjunction with structured light carried by the divers, (b) developing a bearing-only Cooperative Localization system for accurately recording the skeleton of explored caves; (c) developing a sensor suite that seamlessly integrates inertial measurement unit, sonar, depth, and visual data with state estimation algorithms for the volumetric mapping of the cave. The project enhances underwater cave mapping abilities by increasing: 1) the scale of the area mapped, 2) the safety of the divers by reducing their cognitive load during exploration and 3) the quality of the produced maps.

该项目开发了机器人辅助技术，以提高水下洞穴的映射功能。该项目实现了用于水填充洞穴的精确体积模型的实用结构。该机器人系统的技术也可以部署在水下车辆上，以实现对洞穴和其他水下结构的自主探索。开发的技术也可以用于航空车辆和地面车辆的某些应用中。从开发传感器的现场部署中收集的数据可通过公共域发行版提供给更广泛的机器人，地质和跨度研究社区，以进一步创新。此外，数据和软件在开源许可下发布，使研究人员能够在具有挑战性的环境中测试计算机视觉，州估计和传感器融合的算法。该项目通过培训研究生和本科生进行研究和教育，并在南卡罗来纳大学增强了几个研究生和本科课程。该项目还参与了本尼迪克特学院的本科生，历史悠久的黑人学院或大学（HBCU）。收集到的数据用于外展活动，以招募大哥伦比亚地区的STEM教育中的高中生，吸引学生和教育工作者，尤其是在服务不足的社区中。这项研究利用洞穴系统的环境特征开发了3D重建算法。研究小组研究了一个机器人技术，用于单个单元中多个数据流的传感器融合，并通过与专家Cave Divers合作在水下洞穴探索中进行大量测试，通过实验验证了开发的系统。该项目通过三个步骤利用现有实践来利用现有实践，向水下洞穴探险家社区介绍了机器人技术：（a）与潜水员携带的结构性光线一起部署立体声摄像机，（b）开发一个仅轴承合作的定位系统，以准确记录Exploreed Faves的骨架； （c）开发一个传感器套件，该套件将无缝整合惯性测量单元，声纳，深度和视觉数据与状态估计算法，用于洞穴的体积映射。该项目通过提高：1）映射面积的规模，2）潜水员在探索过程中减少认知载荷和3）生产地图的质量来增强水下洞穴映射能力。