SBIR Phase I: Autonomous harvesting, mapping, and forecasting for fresh produce through application of robotics, computer vision, and machine learning

SBIR 第一阶段：通过应用机器人、计算机视觉和机器学习对新鲜农产品进行自主收获、绘图和预测

• 批准号: 1843162
• 负责人: Tim Brackbill
• 金额: $ 22.5万
• 依托单位: Tortuga Agricultural Technologies
• 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 2020-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1843162&HistoricalAwards=false
• 关键词: SBIR; Phase; Autonomous; harvesting; mapping

The broader impact/commercial potential of this project is significant. Agriculture is one of the most important industries impacting the economy, society, and the environment. By automating harvesting of fresh produce, society's ability to grow healthy and sustainable food will increase substantially. The innovation will enhance scientific and technological understanding of how to deploy commercially viable multi-robot coordinated groups performing complex automated tasks, in agriculture and beyond. The long-term opportunity for agricultural robotics is $150B+. This innovation focuses on controlled-environment methods, which are more intensive in terms of capital and labor, but far less intensive in terms of water, chemical use, and fertilizer use. If robots can reduce manual labor of large-scale production, and enhance human management precision, it will enable intensive agriculture practices to compete with, and in some cases supplant, traditional chemical-, labor-, and water-intensive approaches. This will make the US a more competitive global producer of high-quality produce, increase food security, increase access to healthy food for all people, and protect natural resources and the environment.This Small Business Innovation Research (SBIR) Phase I project will advance the fields of Robotic Applications, including computer vision/machine learning and robotics controls, by solving critical problems faced when operating in highly dynamic yet precise biological environments like farms. 1) By evolving and combining approaches from the forefront of computer vision, the project will improve environment sensing of e.g. clusters of fruit, their locations, conditions, precise locations of stems and obstacles. 2) Using that information, the project will develop innovative approaches to complex planning problems where leaves, stems, and obstacles interfere with optimal harvesting but are movable. This will be done with millimeter-level precision, where current autonomous planning approaches operate on multi-centimeter precision. 3) Lastly, the project will combine environmental data with 3D reconstructions of grow operations and individual fruit tracking in order to provide better sampling data to forecast production on a 1-4-week basis, and eventually build a deep learning model. This project is critical to achieving the commercial threshold of performance for robotic harvesting services, including high percent of berries picked, acceptable speed of picking, and the provision of climate sensing and forecast information.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.

这个项目的广泛影响/商业潜力是巨大的。农业是影响经济、社会和环境的最重要的产业之一。通过自动收获新鲜农产品，社会种植健康和可持续食品的能力将大大提高。这一创新将加强科学和技术的理解，以了解如何部署商业上可行的多机器人协调小组，在农业和其他领域执行复杂的自动化任务。农业机器人的长期机会是1500亿美元以上。这一创新侧重于受控环境方法，这种方法在资本和劳动力方面更加密集，但在水、化学品和肥料使用方面的集约化程度要低得多。如果机器人能够减少大规模生产中的体力劳动，提高人类管理的精确度，它将使集约化农业实践能够与传统的化学、劳动力和水密集型方法竞争，在某些情况下甚至取代这种方法。这将使美国成为一个更具竞争力的全球高质量农产品生产国，增加粮食安全，增加所有人获得健康食品的机会，并保护自然资源和环境。这个小企业创新研究（SBIR）第一阶段项目将通过解决在农场等高度动态但精确的生物环境中运行时面临的关键问题，推进机器人应用领域，包括计算机视觉/机器学习和机器人控制。1)通过发展和结合计算机视觉前沿的方法，该项目将改善对环境的感知，例如，水果集群、它们的位置、条件、茎和障碍物的精确位置。2)利用这些信息，该项目将开发出创新的方法来解决复杂的规划问题，其中叶、茎和障碍物会干扰最佳收获，但它们是可移动的。这将以毫米级的精度完成，而目前的自主规划方法可以实现多厘米的精度。3)最后，该项目将环境数据与种植操作的三维重建和单果跟踪相结合，以提供更好的采样数据来预测1-4周的产量，并最终建立深度学习模型。该项目对于实现机器人采收服务的商业性能门槛至关重要，包括高采摘率、可接受的采摘速度以及提供气候传感和预测信息。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。