CPS: Small: Collaborative Research: RUI: Towards Efficient and Secure Agricultural Information Collection Using a Multi-Robot System

CPS：小型：协作研究：RUI：使用多机器人系统实现高效、安全的农业信息收集

• 批准号: 1931767
• 负责人: Ladislau Boloni
• 金额: $ 13.5万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931767&HistoricalAwards=false
• 关键词: CPS; Small; Collaborative; Research; RUI

With the growing world population and diminishing agricultural lands, it becomes imperative to maximize crop yield by protecting crop health and mitigating against pests and diseases. Though there are decades-old practices still in place, there is also growing adoption of so-called precision agriculture solutions, which employ emerging technologies in sensing, automation, and analytics in daily farmland operations. As farmers gain real-time access to critical data (e.g., land and weather conditions) and can quickly share any untoward findings with others, farmland operations are morphing into full-fledged cyber-physical systems. To this end, this project seeks to develop, implement and evaluate a multi-robot agricultural information collection system that is autonomous, efficient and secure.This project led by the University of North Florida (UNF) and supported by the University of Central Florida (UCF) has two main goals: (i) develop and implement novel information collection techniques for autonomous mobile robots that collect, store and share data in an efficient yet secure manner using blockchain,and (ii)and to train undergraduate and graduate students to conduct basic and applied research while closely working with local farmland partners in north-east Florida. Current technologies already use robots for agricultural purposes, but they typically have a high maintenance cost and do not necessarily consider issues related to security and data integrity. The primary objective is to design and deploy a set of autonomous robots that communicate wirelessly and navigate through planned paths in order to collect valuable data. This project will also consider the threat of security attacks by which collected data can be corrupted; seeking new distributed blockchain-based consensus protocols that mitigate the adversarial influence of such attacks. This project also contains a significant research and education component leveraging the leadership of UNF in the context of a primarily undergraduate institution (RUI). Being predominantly an undergraduate institution, there is a lack of opportunity for pursuing higher degrees in the Jacksonville area. This project aligns with an established Memorandum of Understanding (MoU) between UNF and UCF to provide a conduit for computing/engineering students to pursue M.S. degrees at UNF that feed seamlessly into Ph.D. programs at UCF. Students will benefit from the new robotics course to be developed at UNF and the ones being offered at UCF. Research progress will be showcased via technical workshops at both institutions to be held annually. Developed solutions are expected to transfer to other cyber-physical system applications, including search and rescue, patrolling, advanced manufacturing, among others. Most broadly, this project will raise awareness among today's teenagers and young adults of the impending agricultural crisis if worldwide food production falls even further behind meeting demands of an increasing global population.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.

随着世界人口的增长和农业用地的减少，必须通过保护作物健康和减轻病虫害来最大限度地提高作物产量。尽管几十年前的做法仍然存在，但所谓的精准农业解决方案的采用也越来越多，这些解决方案在日常农田运营中采用传感、自动化和分析等新兴技术。随着农民能够实时访问关键数据（例如土地和天气状况）并能够快速与其他人分享任何不良发现，农田运营正在转变为成熟的网络物理系统。为此，该项目旨在开发、实施和评估一个自主、高效、安全的多机器人农业信息采集系统。该项目由北佛罗里达大学（UNF）牵头，并得到中佛罗里达大学（ UCF）有两个主要目标：（i）为自主移动机器人开发和实施新颖的信息收集技术，使用区块链以高效而安全的方式收集、存储和共享数据，以及（ii）培训本科生和研究生进行基础和应用研究，同时与当地农田合作伙伴密切合作佛罗里达州东北部。目前的技术已经将机器人用于农业目的，但它们通常具有较高的维护成本，并且不一定考虑与安全和数据完整性相关的问题。主要目标是设计和部署一组自主机器人，这些机器人可以进行无线通信并通过规划的路径进行导航，以收集有价值的数据。该项目还将考虑安全攻击的威胁，所收集的数据可能会被破坏；寻求新的基于区块链的分布式共识协议，以减轻此类攻击的敌对影响。该项目还包含一个重要的研究和教育部分，利用 UNF 在本科院校 (RUI) 的背景下发挥的领导作用。由于杰克逊维尔地区主要是一所本科院校，因此缺乏攻读更高学位的机会。该项目符合 UNF 和 UCF 之间既定的谅解备忘录 (MoU)，为计算机/工程专业的学生攻读硕士学位提供渠道。 UNF 的学位可无缝衔接至博士学位。 UCF 的项目。学生将受益于 UNF 开发的新机器人课程以及 UCF 提供的课程。研究进展将通过每年在两个机构举办的技术研讨会来展示。开发的解决方案预计将转移到其他网络物理系统应用，包括搜索和救援、巡逻、先进制造等。更广泛地说，如果全球粮食产量进一步落后于满足全球人口增长的需求，该项目将提高当今青少年和年轻人对迫在眉睫的农业危机的认识。该奖项反映了 NSF 的法定使命，并通过评估被认为值得支持利用基金会的智力优势和更广泛的影响审查标准。

项目成果

Robotic Information Gathering via Deep Generative Inpainting

通过深度生成修复收集机器人信息

• DOI: 10.1109/smc53992.2023.10394444
• 发表时间: 2023-10-01
• 期刊: 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
• 作者: Tamim Khatib;O. P. Kreidl;Ayan Dutta;Ladislau Bölöni;Swapnoneel Roy
• 通讯作者: Swapnoneel Roy

Confidence-Guided Path Planning for Mobile Sensors

移动传感器的置信引导路径规划

• DOI: 10.1109/globecom54140.2023.10437189
• 发表时间: 2023-12-04
• 期刊: GLOBECOM 2023 - 2023 IEEE Global Communications Conference
• 作者: D. Turgut;O. P. Kreidl;Ayan Dutta;Ladislau Bölöni
• 通讯作者: Ladislau Bölöni

Secure Multi-Robot Adaptive Information Sampling

安全多机器人自适应信息采样

• DOI: 10.1109/ssrr53300.2021.9597867
• 发表时间: 2021-10
• 期刊: and Rescue Robotics (SSRR
• 作者: Samman, Tamim;Spearman, James;Dutta, Ayan;Kreidl, O. Patrick;Roy, Swapnoneel;Boloni, Ladislau
• 通讯作者: Boloni, Ladislau

Motion Planning for Mobile Robots Using Uncertain Obstacle Estimation

使用不确定障碍物估计的移动机器人运动规划

• DOI: 10.1109/access.2024.3359156
• 发表时间: 2024-09-14
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Z. Gyenes;Barnabás Pajkos;Ladislau Bölöni;E. Szádeczky
• 通讯作者: E. Szádeczky

Energy-Efficient Blockchain-Enabled Multi-Robot Coordination for Information Gathering: Theory and Experiments

用于信息收集的节能区块链多机器人协调：理论与实验

• DOI: 10.3390/electronics12204239
• 发表时间: 2023-10
• 期刊: Electronics
• 影响因子: 2.9
• 作者: Castellon, Cesar E.;Khatib, Tamim;Roy, Swapnoneel;Dutta, Ayan;Kreidl, O. Patrick;Bölöni, Ladislau
• 通讯作者: Bölöni, Ladislau