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

这项在人类-技术前沿-研究：大型(FW-HTF-RL)项目的未来工作促进了农业劳动力和自动化技术合作伙伴关系，在未来用于新鲜水果树作物收获(即，采摘和处理打算在商店出售的水果)的精准农业背景下。该项目的总体目标是塑造未来的农场工作场所，在这种工作场所中，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，同时改善工作经验和提高粮食生产过程的生产率。并不是所有新鲜果树作物收获的任务都可以自动化，有些任务可能更好地转移给未来的机器人同事，因为这样做会提高农场工人的效率和工作质量。该项目汇集了工程学、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互动。该团队的目标是为未来的农场工人和农场所有者开发并验证未来农业机器人和自动化技术的科学和技术基础。这项人类工人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意义。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。该项目沿着五个基本的农业机器人和自动化技术以及农业劳动力研究方向进行了独特的创新。1)为安全、可靠和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来理解农场工人在收割过程中的活动。3)能够识别人类活动的高效机器人规划技术。4)精准农业技术信任和采用的参与式设计方法。5)推进人-机器人多任务可持续农业。该项目积极邀请利益攸关方(农场工人、农场所有者、包装厂专家)评估当前的标准和做法，然后在现场演示后整合反馈，为设备和系统的反复修改提供信息。综上所述，这些研究方向将有助于通过多任务来扩大人与机器人的合作，明确探索收获效率和可持续精密农业之间的权衡，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，特别是因为它可能扰乱与技术所有权相关的当前补偿计划，这反过来又可能进一步影响对自动化的采用和信任程度。丰富的一系列引人入胜的问题将为不同类型的本科生提供丰富的研究机会。该项目整合了在该项目的三个合作地点-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外联活动中的现有努力，以扩大代表不足的少数群体的参与。该项目由人-技术前沿交叉发展计划的未来工作资助，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-技术伙伴关系的更深层次的基本理解。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.