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.

农业是数字化程度最低的行业之一。美国可以通过农业自动化在这一领域获得巨大优势。精准农业包括使用机器人和自动化来提高经济效益。 使用机器人和物联网的自动化是精准农业的关键促成因素，可以减少温室气体排放并最大限度地减少对土壤、水和空气的影响。机器人技术每年可以节省数百亿美元的除草剂，杀虫剂，化肥和灌溉成本。它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，来自加州、北达科他州、德克萨斯州和新罕布什尔州的研究人员团队将规划和设计一个用于机器人和物联网的计算机建模软件，用于精准农业。该软件将使科学家和工程师能够在计算机中部署和测试新的机器人和设备，而无需实际构建它们。利用该奖项，项目团队将收集大量数据（例如，视频和图像），并使用这些数据来规划上述软件。这些数据和开源教学工具将在互联网上向公众开放。以智能自动化和互联互通为特征的第四次工业革命即将永远改变农场管理实践。为了加速这一积极的变化，项目团队设想了一个基础设施，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。虽然计算机科学家和机器人专家每天都在开发具有巨大潜力的新型软件和硬件，但这些工具通常无法在现实世界中应用。用于测试这些工具的模拟环境将弥合基础研究和实际部署之间的差距。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、精确灌溉、自主作物健康监测和精确作物保护。这可以大大降低劳动力成本，减少化学品的使用，降低对水资源的影响，保持土壤的肥力，提高作物的产量。计算机科学家和机器人专家将能够熟悉精准农业的现实挑战，例如，降水对农业的巨大影响。该项目团队将收集初步数据以建立基础设施原型，与感兴趣的研究人员组织研讨会以规划基础设施，并与农民和农学家联系以收集反馈。该奖项反映了NSF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而被认为值得支持。

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