CCRI: Planning-C: A Framework for Development of Robots and IoT for Precision Agriculture

CCRI：Planning-C：精准农业机器人和物联网开发框架

• 批准号: 2213839
• 负责人: Mohammad Khalid Jawed
• 金额: $ 10万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2213839&HistoricalAwards=false
• 关键词: CCRI; Planning; Framework; Development; Robots

Agriculture is one of the least digitized industries. The US can gain a drastic advantage in this field by adopting automation for agriculture. Precision agriculture consists of using robotics and automation to increase economic benefits. Automation using robots and Internet of Things is a key enabling factor for precision agriculture and can cut down greenhouse gas emissions and minimize impact on soil, water, and air. Robotic technologies can save tens of billions of dollars every year in herbicide, pesticide, fertilizer, and irrigation costs. They can eliminate the use of billions of pounds of chemicals that are harmful to the ecosystem. In this project, a team of researchers from California, North Dakota, Texas, and New Hampshire, will plan and design a computer modeling software for robots and Internet of Things for precision agriculture. This software will enable scientists and engineers to deploy and test new robots and devices virtually in a computer without physically building them. Using this award, the project team will collect a large amount of data (e.g., videos and images) from real-world farms and use this data to plan the aforementioned software. This data and open-source pedagogical tools will be made available to the public on the internet.The fourth industrial revolution - characterized by smart automation and inter-connectivity - is about to change farm management practices forever. To hasten this positive change, the project team envisions an infrastructure that enables rapid development of robotic hardware, sensing technologies, software tools, and machine learning algorithms. While computer scientists and roboticists are developing novel software and hardware every day with great potential for precision agriculture, these tools typically fall short of real-world application. The envisioned simulation environment for testing such tools will bridge the gap between fundamental research and real-world deployment. These tools can enable autonomous farm management, including precision weed/pest management, precision irrigation, autonomous crop health monitoring, and precision crop protection. This can dramatically cut down labor costs, reduce chemical usage, lower the impact on water resources, conserve the fertility of soil, and increase the yield of crops. Computer scientists and roboticists will be able to get familiar with real-world challenges of precision agriculture, e.g., dramatic effects of precipitation on agriculture. The project team will collect preliminary data to prototype the infrastructure, organize workshops with interested researchers to plan the infrastructure, and connect with farmers and agronomists to gather feedback.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.

农业是数字化最少的行业之一。美国可以通过自动化农业来获得这一领域的巨大优势。精确农业包括使用机器人技术和自动化来增加经济利益。 使用机器人和物联网的自动化是精确农业的关键因素，可以减少温室气体排放，并最大程度地减少对土壤，水和空气的影响。机器人技术每年可以节省数万美元的除草剂，农药，肥料和灌溉成本。他们可以消除数十亿磅对生态系统有害的化学物质的使用。在这个项目中，来自加利福尼亚州，北达科他州，德克萨斯州和新罕布什尔州的一组研究人员将计划和设计用于机器人和物联网的计算机建模软件，以供精确农业。该软件将使科学家和工程师能够在计算机中几乎在计算机中部署和测试新的机器人和设备，而无需物理构建它们。使用该奖项，项目团队将从现实世界农场收集大量数据（例如，视频和图像），并使用此数据来计划上述软件。这些数据和开源教学工具将在互联网上向公众提供。第四次工业革命（具有智能自动化和连接性）的特征将永远改变农场管理实践。为了加快这种积极的变化，项目团队设想了一个基础架构，该基础架构可以快速开发机器人硬件，传感技术，软件工具和机器学习算法。尽管计算机科学家和机器人每天都在开发新颖的软件和硬件，并具有巨大的精确农业潜力，但这些工具通常没有现实世界的应用。测试此类工具的设想模拟环境将弥合基本研究与现实部署之间的差距。这些工具可以实现自主农场管理，包括精确的杂草/害虫管理，精密灌溉，自主农作物健康监测和精密作物保护。这可以大大降低人工成本，降低化学用法，降低对水资源的影响，保留土壤的生育能力并增加农作物的产量。计算机科学家和机器人主义者将能够熟悉精确农业的现实挑战，例如降水对农业的戏剧性影响。项目团队将收集初步数据以制作基础架构，与有兴趣的研究人员组织研讨会，以计划基础架构，并与农民和农艺师联系以收集反馈。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和广泛的影响来评估CRETERIA的评估。

项目成果

Agronav: Autonomous Navigation Framework for Agricultural Robots and Vehicles Using Semantic Segmentation and Semantic Line Detection

Agronav：使用语义分割和语义线检测的农业机器人和车辆自主导航框架

• 发表时间: 2023
• 期刊: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR
• 作者: Panda, S. K.

Neural-Kalman GNSS/INS Navigation for Precision Agriculture

• DOI: 10.1109/icra48891.2023.10161351
• 发表时间: 2023-05
• 期刊: 2023 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: Yayun Du;Swapnil Sayan Saha;S. Sandha;Arthur Lovekin;Jason Wu;S. Siddharth;M. Chowdhary;M. Jawed;M. Srivastava