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
基本信息
- 批准号:2326309
- 负责人:
- 金额:$ 100.77万
- 依托单位:
- 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:
- 关键词:
项目摘要
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.
Fuman技术边界研究:大型(FW-HTF-RL)项目在未来的Precision Precision Farming的背景下推进了劳动力和自动化伙伴关系,用于新鲜的果树杂种收割的处理水果,将肉类出售的肉类出售在商店中)。 Tural Robots与农场工人的无缝合作伙伴关系,以改善未来的树木收获成果,同时改善工作经验和生产过程从工程,计算机科学,社会科学和克罗德生产的饮食中发现相关的机器人和农场工人的互动,旨在为未来的农业工人和农场所有者开发并验证UTURE农业机器人技术和自动化技术的科学和技术基础。精确耕作并了解农业关系的含义沿着五个基本的农业机器人和自动化和农业劳动力研究方向进行独特的项目。精确的农业技术信托和采用。机器人与多任务合作。这可能会进一步影响自动化的程度和信任。加州大学戴维斯(UC Davis)扩大了抑制不足的少数群体的参与。与人类工人一起使用和谐的工作技术。该奖项反映了NSF的遗迹,它值得使用Toundation的知识分子M ERIT和更广泛的影响标准进行支持。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Konstantinos Karydis其他文献
Uncertainty Quantification for Small Robots Using Principal Orthogonal Decomposition
使用主正交分解对小型机器人进行不确定性量化
- DOI:
- 发表时间:
2016 - 期刊:
- 影响因子:0
- 作者:
Konstantinos Karydis;M. A. Hsieh - 通讯作者:
M. A. Hsieh
Energy efficiency of trajectory generation methods for stop-and-go aerial robot navigation
走走停停的空中机器人导航轨迹生成方法的能源效率
- DOI:
- 发表时间:
2017 - 期刊:
- 影响因子:0
- 作者:
Nadia Kreciglowa;Konstantinos Karydis;Vijay R. Kumar - 通讯作者:
Vijay R. Kumar
Neural Network Memory Architectures for Autonomous Robot Navigation
用于自主机器人导航的神经网络内存架构
- DOI:
- 发表时间:
2017 - 期刊:
- 影响因子:0
- 作者:
Steven W. Chen;Nikolay A. Atanasov;Arbaaz Khan;Konstantinos Karydis;Daniel D. Lee;Vijay R. Kumar - 通讯作者:
Vijay R. Kumar
End-to-End Navigation in Unknown Environments using Neural Networks
使用神经网络在未知环境中进行端到端导航
- DOI:
- 发表时间:
2017 - 期刊:
- 影响因子:0
- 作者:
Arbaaz Khan;Clark Zhang;Nikolay A. Atanasov;Konstantinos Karydis;Daniel D. Lee;Vijay R. Kumar - 通讯作者:
Vijay R. Kumar
OpenRoACH: A Durable Open-Source Hexapedal Platform with Onboard Robot Operating System (ROS)
OpenRoACH:带有板载机器人操作系统 (ROS) 的耐用开源六足平台
- DOI:
10.1109/icra.2019.8794042 - 发表时间:
2019 - 期刊:
- 影响因子:0
- 作者:
Liyu Wang;Yuxiang Yang;Gustavo Correa;Konstantinos Karydis;R. Fearing - 通讯作者:
R. Fearing
Konstantinos Karydis的其他文献
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{{ truncateString('Konstantinos Karydis', 18)}}的其他基金
CAREER: Morphological Computation for Resilient Dynamic Locomotion of Compliant Legged Robots with Application to Precision Agriculture
职业:顺应腿式机器人弹性动态运动的形态计算及其在精准农业中的应用
- 批准号:
2046270 - 财政年份:2021
- 资助金额:
$ 100.77万 - 项目类别:
Standard Grant
NRI: Integrated Soft Wearable Robotics Technology to Assist Arm Movement of Infants with Physical Impairments
NRI:集成软可穿戴机器人技术,协助身体障碍婴儿的手臂运动
- 批准号:
2133084 - 财政年份:2021
- 资助金额:
$ 100.77万 - 项目类别:
Continuing Grant
RI: Small: Collaborative Research: Extracting Dynamics from Limited Data for Modeling and Control of Unmanned Autonomous Systems
RI:小型:协作研究:从有限数据中提取动力学,用于无人自主系统的建模和控制
- 批准号:
1910087 - 财政年份:2019
- 资助金额:
$ 100.77万 - 项目类别:
Standard Grant
Group Travel Award for 2017 Workshop on Learning Perception and Control for Autonomous Flight: Safety, Memory, and Efficiency
2017年自主飞行学习感知与控制研讨会团体旅游奖:安全、记忆和效率
- 批准号:
1743262 - 财政年份:2017
- 资助金额:
$ 100.77万 - 项目类别:
Standard Grant
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2326170 - 财政年份:2023
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FW-HTF-RL/协作研究:数字设施管理的未来(DFM 的未来)
- 批准号:
2326407 - 财政年份:2023
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- 批准号:
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