SBIR Phase I: Robust autonomy for robotics-based data collection in surface water environments

SBIR 第一阶段：地表水环境中基于机器人的数据收集的鲁棒自主性

• 批准号: 1843049
• 负责人: Jeffrey Laut
• 金额: $ 22.5万
• 依托单位: Manifold Robotics Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2020-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1843049&HistoricalAwards=false
• 关键词: SBIR; Phase; Robust; autonomy; robotics

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project lies in the increase in efficiency and reduction in cost of gathering information in surface water environments using unmanned surface vehicles (USVs). USVs have the potential to increase accessibility to bodies of water, enhance coverage, and make data collection safer by removing humans from the task. However, to fully realize the potential of USVs for gathering information, a level of autonomy with minimal need for human supervision and intervention is required. The development of this technology will open the door to the pervasive use of USVs for environmental data collection, and ultimately benefit society through: i) more information upon which to base decisions about resource management and environmental policy; ii) higher frequency of data collection activities through reduced cost; and iii) increased safety by keeping human personnel out of harsh environments.This Small Business Innovation Research (SBIR) Phase I project will lay the foundation for a transformative approach to environmental data collection in surface waters. The intellectual merit of this project lies in addressing technical challenges that will enable unmanned surface vehicles (USVs) to effectively navigate autonomously in complex surface waters that may contain a range of diverse obstacles, which would otherwise be inaccessible or at best require a high degree of human supervision. This will be achieved by advancing the state-of-the-art in computer vision and machine learning toward intelligent obstacle characterization. The proposed framework interprets images or video of obstacles to extract salient features, such as flexibility or rigidity, through the innovative use of machine learning, dimensionality reduction, and tools borrowed from the analysis of collective behavior. This extra layer of information provided through the proposed framework represents a significant advancement in computer vision, which offers direct benefits to environmental monitoring and data collection by empowering USVs with new unmanned capabilities.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.

这项小型企业创新研究（SBIR）I阶段项目的更广泛的影响/商业潜力在于使用无人的地表车辆（USV）在地表水环境中收集信息的效率和降低。 USV有可能增加对水体的可及性，增强覆盖范围，并通过将人类从任务中删除，从而使数据收集更安全。但是，为了充分意识到USV收集信息的潜力，需要对人类监督和干预的需求最少的自治水平。这项技术的开发将为环境数据收集的普遍使用USV打开大门，并最终通过以下方式使社会受益：i）有关资源管理和环境政策的更多信息； ii）通过降低成本收集数据活动的频率更高； iii）通过将人类人员远离恶劣的环境来提高安全性。这项小型企业创新研究（SBIR）I阶段项目将为在地表水中采取环境数据收集的变革性方法奠定基础。该项目的智力优点在于应对技术挑战，这将使无人驾驶的地表车辆（USV）有效地在复杂的地表水中自动导航，这些水域可能包含一系列不同的障碍，否则这些障碍将是无法访问的，或者最多需要高度的人类监督。这将通过将计算机视觉和机器学习中的最新技术发展到智能障碍物表征来实现。提出的框架通过创新使用机器学习，降低维度和从集体行为的分析中借用的工具来解释提取诸如灵活性或僵化之类的障碍的图像或视频。通过提议的框架提供的这种额外的信息代表了计算机视觉的重大进步，该信息通过具有新的无人驾驶功能授权USV来为环境监控和数据收集提供了直接的好处。该奖项反映了NSF的法定任务，并认为通过使用该基金会的智力和更广泛影响的评估来审查Criteria，并被认为是通过评估来获得支持的。