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: 2326311
• 负责人: Martin Kenney
• 金额: $ 37.5万
• 依托单位: University of California-Davis
• 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=2326311&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.

这个未来的工作在人类技术前沿-研究：大（FW-HTF-RL）项目推进农业劳动力和自动化技术合作伙伴关系的背景下，未来的精确农业新鲜果树作物收获（即采摘和处理水果，这是为了在商店出售）。该项目的总体目标是塑造未来的农场工作场所，其中具有人类意识的农业机器人与农场工人无缝合作，以改善未来的树木作物收获成果，同时改善工作体验并提高食品生产过程的生产力。并不是所有的新鲜果树作物收获任务都可以自动化，有些任务可能会更好地卸载到未来的机器人同事身上，这样做可以提高农场工人的效率并提高工作质量。该项目汇集了来自工程，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和农场工人的互动。该团队旨在为未来的农业机器人和自动化技术创造科学和技术基础，为未来的农场工人和农场主开发和验证。这种人类工作者验证将增加对未来精准农业的信任和采用，并了解这项技术在未来农业劳动力关系中的整合的影响。该项目通过创建新的、扩展的和独特的以用户为中心的框架，研究在农业劳动力和农业机器人和自动化技术的前沿部署普遍、智能和自主的农业机器人。该项目独特地创新沿着五个基本的农业机器人和自动化技术和农业劳动力的研究方向。1)共同设计安全、可靠和高效的机器人收割机的驱动和感知的新原理。2)有效的机器视觉机制，以了解农场工人在收获活动。3)有效的机器人规划技术认识到人类活动。4)精准农业技术信任与采用的前瞻性设计方法。5)人类-机器人多任务处理向可持续农业的发展。该项目积极与利益相关者（农场工人、农场主、包装厂专家）合作，评估当前的标准和实践，然后在现场演示后整合反馈，为设备和系统的迭代修改提供信息。总的来说，这些研究方向将有助于扩展人机协作与多任务处理，明确探索收获效率和可持续精准农业之间的权衡，同时揭示未来农业机器人技术对未来农业劳动力的影响，特别是因为它可能会破坏目前与技术所有权有关的补偿计划，这反过来又会进一步影响自动化的采用程度和信任度。丰富的吸引人的问题集将为不同的本科生群体提供丰富的研究机会。该项目整合了现有的K-12外展活动，这些活动在该项目的三个合作地点举办--加州大学（UC）滨江分校、加州大学默塞德分校、和加州大学戴维斯分校-扩大代表性不足的少数群体的参与。这个项目已由未来的工作在人类技术前沿跨董事会计划资助，以促进相互依存的人类更深入的基本理解-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，被认为值得支持。