FW-HTF-RL/Collaborative Research: Elevating Farm Worker-Robot Collaborations in Agri-Food Ecosystems

FW-HTF-RL/协作研究：提升农业食品生态系统中的农场工人与机器人协作

• 批准号: 2326310
• 负责人: Thomas Harmon
• 金额: $ 61.06万
• 依托单位: University of California - Merced
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2326310&HistoricalAwards=false
• 关键词: FW; HTF; RL; Collaborative; Research

This Future of Work at the Human-Technology Frontier - Research: Large (FW-HTF-RL) project advances the agricultural workforce and automation technology partnership in the context of future precision farming for fresh fruit tree-crop harvesting (that is, picking and handling fruits that are meant to be sold in a store). The overarching goal of this project is to shape the future farm workplace in which human-aware agricultural robots operate in a seamless partnership with farmworkers to improve future tree-crop harvesting outcomes while improving the job experience and enhancing the productivity of food production processes. Not all tasks in fresh fruit tree-crop harvesting can be automated, and some tasks might be better offloaded to a future robotic co-worker when doing so would augment farmworker efficiency and improve the quality of work. The project brings together experts from Engineering, Computer Science, Social Science, Environmental Science, and Crop Production Management to discover these new agricultural robotics and farmworker interactions. The team aims to create scientific and technological foundations of future agricultural robotics and automation technology developed for and validated by future farmworkers and farm owners. This human worker validation will increase trust and adoption toward future precision farming and understand the implications of this technology’s integration in future agriculture workforce relations. The project investigates the deployment of pervasive, intelligent, and autonomous agricultural robotics at the frontier of the farming workforce and agricultural robotics and automation technology by creating new, expanded, and unique user-centered frameworks. The project uniquely innovates along five fundamental agricultural robotics and automation technology and agricultural workforce research directions. 1) Novel principles to co-design actuation and perception for safe, reliable, and efficient robotic harvesters. 2) Effective machine vision mechanisms to understand farmworker activities in harvesting. 3) Efficient robot planning techniques cognizant of human activities. 4) Participatory design approach for precision farming technology trust and adoption. 5) Advancement of human-robot multitasking toward sustainable agriculture. The project actively engages stakeholders (farmworkers, farm owners, packing house specialists) to assess current standards and practices and then integrate feedback after in-field demonstrations to inform iterative modifications of devices and systems. Taken together, these research directions will help extend human-robot collaboration with multitasking, explicitly exploring the trade-offs between harvesting efficiency and sustainable precision farming while shedding light on the yet-to-be-explored implications of future agriculture robotics technology on future agriculture workforce, notably as it may disrupt current compensation schemes in relation to technology ownership which in turn can further affect the degree of adoption and trust in automation. The rich set of engaging problems will provide abundant research opportunities for a diverse cohort of undergraduate students. The project integrates existing efforts in K-12 outreach events hosted at the project’s three collaborating sites – University of California (UC) Riverside, UC Merced, and UC Davis – to broaden the participation of under-represented minority groups.This project has been funded by the Future of Work at the Human-Technology Frontier cross-directorate program to promote a deeper fundamental understanding of the interdependent human-technology partnership in work contexts by advancing the design of intelligent work technologies that operate in harmony with human workers.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.

人类技术领域的工作未来 - 研究：大型（FW-HTF-RL）项目在未来的Precision Farming进行新鲜的果树杂草收获的背景下​​推进了农业劳动力和自动化技术合作伙伴关系（即，采摘和处理要在商店中销售的果实）。该项目的总体目标是塑造未来的农场工作场所，其中人类意识到的农业机器人与农场工人建立了无缝的合作伙伴关系，以改善未来的树木作品收获成果，同时改善工作经验并提高食品生产过程的生产率。并非所有新鲜的果树卷收集中的任务都可以自动化，并且在这样做时，某些任务可能会更好地卸载为未来的机器人同事，这会提高农场工人的效率并提高工作质量。该项目汇集了工程，计算机科学，社会科学，环境科学和作物生产管理的专家，以发现这些新的农业机器人技术和农场工人的互动。该团队旨在为未来的农场工人和农场所有者开发和验证的未来农业机器人技术和自动化技术创造科学和技术基础。这种人工验证将增加对未来精确农业的信任和采用，并理解该技术在未来农业劳动力关系中的融合的含义。该项目通过创建新的，扩展的，独特的用户以用户为中心的框架来调查农业劳动力和农业机器人技术和自动化技术的边界的普遍，智能和自主农业机器人技术的部署。该项目独特地沿着五个基本农业机器人技术和自动化技术以及农业劳动力研究方向进行创新。 1）针对安全，可靠和有效的机器人收割机共同设计激活和感知的新原则。 2）有效的机器视力机制了解农场工人的收获活动。 3）有效的机器人计划技术认识人类活动。 4）精确农业技术信任和采用的参与设计方法。 5）人手多任务迈向可持续农业的进步。该项目积极聘请利益相关者（农场工人，农场主，包装室专家）来评估当前的标准和实践，然后在现场演示后集成反馈，以告知对设备和系统的迭代修改。 Taken together, these research directions will help extend human-robot collaboration with multitasking, explicitly exploring the trade-offs between harvesting efficiency and sustainable precision farming while shedding light on the yet-to-be-explored implications of future agriculture robotics technology on future agriculture workforce, Notably as it may disrupt current compensation schemes in relation to technology ownership which in turn can further affect the degree of adoption and trust in自动化。丰富的引人入胜的问题将为多样化的本科生提供丰富的研究机会。该项目将现有的努力纳入了该项目的三个合作站点（加利福尼亚大学河滨大学，UC Merced和UC Davis）举办的，以扩大代表性不足的少数群体的参与。该项目是由人类跨领域的“人类跨部门”的未来建立人类的未来资助的，这是人类跨领域的未来，这是人类跨领域的固定性一致性，该项目的基础是深入的，这是一项深入的研究，即深入的研究，即深入的研究，即深入的一项涉及的人类，该项目涉及一项迫切的人类学，这是一项深入的，即涉及涉及人类的研究，即涉及一项迫切的人类的工作。工作环境通过推进与人类工人和谐相处的智能工作技术的设计。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来支持的。