CPS: Frontier: Collaborative Research: COALESCE: COntext Aware LEarning for Sustainable CybEr-Agricultural Systems

CPS：前沿：协作研究：COALESCE：可持续网络农业系统的情境感知学习

• 批准号: 1954556
• 负责人: Soumik Sarkar
• 金额: $ 500万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-15 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1954556&HistoricalAwards=false
• 关键词: CPS; Frontier; Collaborative; Research; COALESCE

One of the grand technical challenges of our generation is to get ready to feed 9 billion people by 2050 with sustainable use of water and chemicals. However, we are facing unprecedented challenges in adopting sustainable agricultural management practices, increasing production, keeping agriculture profitable and coping with deadly biotic and abiotic stresses and diseases as well as changing climate that threaten yield. This project aims to transform Cyber-Physical System (CPS) capabilities in agriculture to enable farmers to respond to crop stressors with lower cost, greater agility, and significantly lower environmental impact than current practices. The objective is to make foundational advances in AI, machine learning and robotics to individual plant-level sensing, modeling and reasoning. This enables small autonomous dexterous robots instead of the heavy farm equipment to monitor plants or small plots individually and treat them with minimum amount of chemicals. This also lowers the barrier to entry for small scale farmers, increases safety, minimizes runoff as well as soil compaction. This project includes a significant collaboration with the University of Illinois at Urbana-Champaign that is funded by the National Institute of Food and Agriculture (NIFA) within the U.S. Department of Agriculture.The research investigates multiple areas in data-driven estimation, control, and adaptation of complex cyber-physical systems, such as: (1) rigorous incorporation of domain knowledge and physical principles into a machine learning (ML)-driven estimation/prediction/control framework, (2) cross-modal information fusion for assimilating heterogeneous data streams that differ in type (categorical, discrete, or continuous), quality/accuracy/noise, and sampling frequency. (3) robust ML under a degraded sensing environment, (4) data-driven supervisory decision-making under resource constraints, such as data amount, data quality, privacy, and cost, (5) distributed control and coordination of autonomous teams of robots operating in harsh, changing, and uncertain field environments with partial observability, and (6) soft robotic arms and manipulators, along with embedded control and sensing systems, for agricultural manipulation by small mobile robots. The broader acceptance of the framework is facilitated by the team's unique collaboration with producer groups with direct connections to farmers. A wide range of knowledge dissemination plans target the CPS community, the farming community, and the general public. Education and outreach plans focus on the farming population and the next-generation scientific workforce. Specific activities and programs at the participating institutions are designed to broaden participation of Native American, Hispanic, African-American, and female students in computing and engineering. All research products and educational material generated by the project are being made publicly available through the project webpage.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.

我们这一代人面临的重大技术挑战之一是准备好到2050年通过可持续使用水和化学品来养活90亿人。然而，我们在采取可持续农业管理做法、增加产量、保持农业盈利、应对致命的生物和非生物压力和疾病以及威胁产量的气候变化方面面临着前所未有的挑战。该项目旨在改变农业中的网络物理系统（CPS）能力，使农民能够以更低的成本，更大的灵活性和比当前做法更低的环境影响来应对作物压力。其目标是在人工智能、机器学习和机器人技术方面取得基础性进展，以实现工厂级的传感、建模和推理。这使得小型自主灵巧机器人能够代替重型农场设备，单独监控植物或小地块，并使用最少量的化学品对其进行处理。这也降低了小规模农民的进入门槛，增加了安全性，最大限度地减少了径流和土壤压实。 该项目包括与伊利诺伊大学厄巴纳-香槟分校的重要合作，由美国农业部国家食品和农业研究所（NIFA）资助。该研究调查了复杂网络物理系统的数据驱动估计，控制和适应的多个领域，例如：（1）将领域知识和物理原理严格结合到机器学习（ML）驱动的估计/预测/控制框架中，（2）跨模态信息融合，用于同化在类型（分类、离散或连续）、质量/精度/噪声和采样频率上不同的异构数据流。(3)在退化的传感环境下的鲁棒ML，（4）在资源约束下的数据驱动的监督决策，例如数据量，数据质量，隐私和成本，（5）在恶劣，变化和不确定的现场环境中操作的机器人自治团队的分布式控制和协调，具有部分可观测性，以及（6）软机器人手臂和操纵器，沿着嵌入式控制和传感系统，用于小型移动的机器人的农业操作。该小组与与农民有直接联系的生产者团体进行了独特的合作，促进了该框架得到更广泛的接受。广泛的知识传播计划针对CPS社区、农业社区和公众。教育和推广计划的重点是农业人口和下一代科学工作者。参与机构的具体活动和方案旨在扩大美洲原住民、西班牙裔、非洲裔美国人和女学生对计算机和工程的参与。该项目产生的所有研究产品和教育材料都通过项目网页公开提供。该奖项反映了NSF的法定使命，并且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。

项目成果

Recognizing Principles of AI Ethics through a Role-Play Case Study on Agriculture

通过农业角色扮演案例研究认识人工智能伦理原则

• DOI: 10.18260/1-2--44029
• 发表时间: 2023
• 期刊: ASEE Conferences
• 作者: Hingle, Ashish;Johri, Aditya
• 通讯作者: Johri, Aditya

Visual Servoing for Pose Control of Soft Continuum Arm in a Structured Environment

• DOI: 10.1109/lra.2022.3155821
• 发表时间: 2022-02
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Shivani Kamtikar;Samhita Marri;Benjamin Walt;N. Uppalapati;Girish Krishnan;Girish V. Chowdhary

Design Space Enumerations for Pneumatically Actuated Soft Continuum Manipulators

气动软连续体机械臂的设计空间枚举

• DOI: 10.1115/detc2023-116930
• 发表时间: 2023
• 期刊: American Society of Mechanical Engineers
• 作者: Ripperger, Evan;Krishnan, Girish
• 通讯作者: Krishnan, Girish

Exploring NLP-Based Methods for Generating Engineering Ethics Assessment Qualitative Codebooks

探索基于 NLP 的工程伦理评估定性密码本生成方法

• DOI: 10.1109/fie58773.2023.10342985
• 发表时间: 2023
• 期刊: IEEE
• 作者: Hingle, Ashish;Katz, Andrew;Johri, Aditya
• 通讯作者: Johri, Aditya

Hybrid Eye-in-Hand/Eye-to-Hand Image Based Visual Servoing for Soft Continuum Arms

• DOI: 10.1109/lra.2022.3194690
• 发表时间: 2022-10
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Ali A. AlBeladi;E. Ripperger;Seth Hutchinson;Girish Krishnan