CAREER: Toward Autonomous Decision Making and Coordination in Intelligent Unmanned Aerial Vehicles' Operation in Dynamic Uncertain Remote Areas

职业：在动态不确定的偏远地区实现智能无人机操作的自主决策和协调

• 批准号: 2232048
• 负责人: Fatemeh Afghah
• 金额: $ 54.19万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2232048&HistoricalAwards=false
• 关键词: CAREER; Toward; Autonomous; Decision; Making

Unmanned aerial vehicles (UAVs) have been increasingly utilized in several commercial and civil applications such as package delivery, traffic monitoring, precision agriculture, remote sensing, border patrol, hazard monitoring, disaster relief, and search and rescue operations to collect data/imagery for a ground command station nearby. Current implementations of UAV-based operations heavily rely on control, inference, task allocation, and planning from a human controller that can limit the operation of drones in missions where the operation field is not fully observable to the human controller prior to the mission and reliable and continuous communication is not available between the UAVs and the ground station or among the teammate UAVs during the mission. The UAVs can be particularly useful in such unstructured and unknown environments to provide agile surveying or search-and-rescue operations. Therefore, the future of UAV technology focuses on the development of small, low-cost, and smart drones with a higher level of autonomy. Such drones can facilitate a wide range of sophisticated missions performed by a fleet of cooperative UAVs with minimum human intervention and lower cost. The objective of this research is to develop theoretical and practical frameworks for operation, situational awareness, coordination, and communication of a network of fully autonomous multi-agent systems (e.g., UAVs) in dynamic and unknown environments with minimum human interventions. This research can facilitate a new set of applications for autonomous multi-agent systems in remote and dynamic environments. This project involves an integrated set of research, implementation, and experimental validation thrusts to develop novel frameworks for autonomous decision making, coalition formation, coordination, spectrum management, and task allocation in UAV systems. The developed techniques can be utilized in other multi-agent cognitive systems such as robotic systems, and autonomous driving vehicles where quick search, surveillance, and reactions are required with limited human interventions.This project also offers a number of educational and outreach activities to integrate the results of this research in curriculum enhancement, student mentorship, engaging underrepresented minority and female students, developing hands-on UAV-based sensing experiments for elementary and middle school students and outreach to the community to enhance public awareness about new applications of UAV systems through collaboration with Flagstaff Festival of Science.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.

无人机 (UAV) 已越来越多地应用于多种商业和民用应用，例如包裹递送、交通监控、精准农业、遥感、边境巡逻、危险监测、救灾以及搜救行动，以收集数据/图像附近有一个地面指挥站。当前基于无人机的操作的实施严重依赖于人类控制器的控制、推理、任务分配和规划，这可能会限制无人机在任务中的操作，因为人类控制器在执行任务之前无法完全观察到操作区域并且可靠任务期间，无人机与地面站之间或队友无人机之间无法进行持续通信。无人机在这种非结构化和未知的环境中特别有用，可以提供敏捷的勘测或搜救行动。因此，无人机技术的未来重点是发展小型、低成本、具有更高自主水平的智能无人机。 此类无人机可以促进由协作无人机机队执行的各种复杂任务，以最少的人为干预和更低的成本。本研究的目的是开发理论和实践框架，用于在动态和未知环境中以最少的人为干预运行完全自主的多智能体系统（例如无人机）网络的操作、态势感知、协调和通信。这项研究可以促进远程和动态环境中自主多代理系统的一组新应用。该项目涉及一系列综合研究、实施和实验验证，旨在开发无人机系统自主决策、联盟形成、协调、频谱管理和任务分配的新颖框架。所开发的技术可用于其他多智能体认知系统，例如机器人系统和自动驾驶车辆，这些系统需要在有限的人工干预下进行快速搜索、监视和反应。该项目还提供了许多教育和外展活动来整合这项研究的成果涉及课程改进、学生辅导、让代表性不足的少数族裔和女学生参与、为中小学生开发基于无人机的动手传感实验以及向社区进行推广，以通过以下方式提高公众对无人机系统新应用的认识：与弗拉格斯塔夫音乐节合作科学。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting

通过时空数据过拟合实现高质量、高效的视频超分辨率

• 发表时间: 2023-01
• 期刊: IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR
• 作者: Li, Gen;Ji, Jie;Qin, Minghai;Niu, Wei;Ren, Bin;Afghah, Fatemeh;Guo, Linke;Ma, Xiaolong
• 通讯作者: Ma, Xiaolong

Evolutionary Deep Reinforcement Learning for Dynamic Slice Management in O-RAN

O-RAN 中动态切片管理的进化深度强化学习

• DOI: 10.1109/gcwkshps56602.2022.10008614
• 发表时间: 2022-12
• 期刊: 2022 IEEE Globecom Workshops (GC Wkshps
• 作者: Lotfi, Fatemeh;Semiari, Omid;Afghah, Fatemeh
• 通讯作者: Afghah, Fatemeh

Heterogeneous Airborne mmWave Cells: Optimal Placement for Power-Efficient Maximum Coverage

异构机载毫米波蜂窝：最佳放置以实现节能的最大覆盖范围

• DOI: 10.1109/infocomwkshps54753.2022.9798023
• 发表时间: 2022-05
• 期刊: IEEE INFOCOM Workshop on Artificial Intelligence and Blockchain-Enabled Secure and Privacy-Preserving Air and Ground Smart Vehicular Networks (AIBESVN
• 作者: Namvar, Nima;Afghah, Fatemeh
• 通讯作者: Afghah, Fatemeh

BlocKP: Key Pre-Distribution Based Secure Data Transfer

BlocKP：基于密钥预分配的安全数据传输

• DOI: 10.1109/jiot.2021.3137900
• 发表时间: 2021-12
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Gharib, Mohammed;Owfi, Ali;Afghah, Fatemeh;Bentley, Elizabeth Serena
• 通讯作者: Bentley, Elizabeth Serena

Triplet Loss-less Center Loss Sampling Strategies in Facial Expression Recognition Scenarios

面部表情识别场景中的三元组无损中心损失采样策略

• DOI: 10.1109/ciss56502.2023.10089734
• 发表时间: 2023-03
• 期刊: 57th Annual Conference on Information Sciences and Systems (CISS
• 作者: Rajoli, Hossein;Lotfi, Fatemeh;Atyabi, Adham;Afghah, Fatemeh
• 通讯作者: Afghah, Fatemeh