NRI: INT: COLLAB: Distributed co-Robots for Strawberry Harvesting

NRI：INT：COLLAB：用于草莓采摘的分布式协作机器人

• 批准号: 1924622
• 负责人: Yunjun Xu
• 金额: $ 57.6万
• 依托单位: The University of Central Florida Board of Trustees
• 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: 2019-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1924622&HistoricalAwards=false
• 关键词: NRI; INT; COLLAB; Distributed; co; NRI; INT; COLLAB; Distributed; co

Recently, strawberry production in the United States is decreasing due to labor shortages and the ever-increasing cost of labor, particularly for harvesting. A high cost of production could put strawberry-growers in the U.S. at a disadvantage in comparison to countries whose cost of labor is much less. In order to make strawberry production in the U.S. competitive with the rest of the world, production costs need to be reduced. This project aims to investigate developing a small, rugged, and cost-effective co-robot system that can be used by growers, which will ultimately lower the harvesting costs and increase profitability of strawberry production. Additionally, the research results can benefit other high value specialty crops, such as tomatoes and blueberries, as they are facing similar labor shortage issues. This multi-disciplinary research will enrich course curricula which benefits a large number of students at three participating universities, and will provide research and development experiences to students of underrepresented and minority backgrounds. The outreach component of the project will educate K-12 students and the general public about robotics in agriculture via conferences, workshops, field days, and other learning engagement activities. Additionally, a graphic user interface will be developed, which is adaptable as an assistive device to benefit users with disabilities.The project includes three primary research thrusts. The first thrust will investigate a decentralized, scalable row allocation algorithm for robots to achieve a consensus about which rows to harvest, in order to achieve an overall minimum time in harvesting. The algorithm is expected to be scalable, efficient, fast, non-conflicting, and simple. The second thrust will develop an integrated multi-exposure fusion, curvature analysis and hierarchical image processing algorithm. Supported by the end-to-end deep learning technique, the algorithm will detect strawberries, stems, and vines in field environments with a high accuracy. The third research thrust involves a study of a parallel delta robot-arm based manipulation system for picking and transporting harvested fruits. The associated amount of movements will be minimized. These three thrusts will be complemented by many other engineering tasks and a detailed cost analysis.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.

最近，由于劳动力短缺和不断增加的劳动成本，美国的草莓生产正在减少，尤其是用于收获。与劳动力成本少得多的国家相比，高生产成本可能使美国的草莓生长处于不利地位。为了使美国其他地区的草莓生产与世界其他地区竞争，需要降低生产成本。该项目旨在调查开发一个可以由种植者使用的小型，坚固且具有成本效益的共同起作机系统，该系统最终将降低收获成本并提高草莓生产的盈利能力。此外，研究结果可以使其他高价值的特种作物（例如西红柿和蓝莓）受益，因为它们面临着类似的劳动力短缺问题。这项多学科研究将丰富课程课程，使三所参与大学的大量学生受益，并将为人数不足和少数族裔背景的学生提供研发经验。该项目的外展部分将通过会议，研讨会，实地日和其他学习参与活动对K-12学生和公众进行农业机器人技术的教育。 此外，还将开发图形用户界面，该界面是一种适应性的辅助设备，可使残疾用户受益。该项目包括三个主要的研究推力。第一个推力将研究机器人的分散，可扩展的行分配算法，以达成共识，以便在收获中达到总体最小时间。该算法有望扩展，高效，快速，不受影响和简单。第二个推力将开发集成的多曝光融合，曲率分析和分层图像处理算法。在端到端的深度学习技术的支持下，该算法将在野外环境中检测出草莓，茎和藤蔓，精度很高。第三项研究推力涉及对基于Delta机器人臂的基于Delta机器人臂的操纵系统的研究，该系统用于采摘和运输收获的水果。相关的运动数量将最小化。这三个推力将得到许多其他工程任务和详细的成本分析的补充。该奖项反映了NSF的法定任务，并认为使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估。