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.

这项人类技术前沿工作的未来-研究：大型（FW-HTF-RL）项目在未来精准农业的背景下推进了农业劳动力和自动化技术伙伴关系，以收获新鲜水果作物（即采摘和处理打算在商店出售的水果）。该项目的总体目标是塑造未来的农场工作场所，其中人类感知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结果，同时改善工作经验，提高食品生产过程的生产力。并非所有收获新鲜水果作物的任务都可以自动化，有些任务可能会更好地交给未来的机器人同事，因为这样做可以提高农场工人的效率和工作质量。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，这些技术是为未来的农场工人和农场主开发和验证的。这种人工验证将增加对未来精准农业的信任和采用，并理解该技术在未来农业劳动力关系中的整合意义。该项目通过创建新的、扩展的、独特的以用户为中心的框架，在农业劳动力、农业机器人和自动化技术的前沿研究普及、智能和自主农业机器人的部署。该项目沿着农业机器人与自动化五大基础技术和农业劳动力研究方向进行独特创新。1)为安全、可靠、高效的机器人收割机共同设计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过程中的活动。3)识别人类活动的高效机器人规划技术。4)精准农业技术信任与采用的参与式设计方法。5)面向可持续农业的人机多任务研究进展。该项目积极吸引利益相关者（农场工人、农场所有者、包装专家）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多任务协作，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尚未探索的影响。值得注意的是，它可能会破坏当前与技术所有权相关的补偿方案，这反过来又会进一步影响自动化的采用程度和信任程度。丰富的一套引人入胜的问题将提供丰富的研究机会，为不同的本科生队列。该项目整合了在三个合作地点（加州大学河滨分校、加州大学默塞德分校和加州大学戴维斯分校）举办的K-12外展活动的现有努力，以扩大代表性不足的少数群体的参与。该项目由人类-技术前沿跨部门计划的未来工作资助，旨在通过推进与人类工人和谐运作的智能工作技术的设计，促进对工作环境中相互依赖的人类-技术伙伴关系的更深层次的基本理解。该奖项反映了美国国家科学基金会的法定使命，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。