CPS: Medium: Aerial Co-Workers: Augmenting Physical and Cognitive Human Capabilities

CPS：中：空中同事：增强人类的身体和认知能力

• 批准号: 2121391
• 负责人: Giuseppe Loianno
• 金额: $ 87.49万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2121391&HistoricalAwards=false
• 关键词: CPS; Medium; Aerial; Co; Workers

This project studies the algorithmic foundations and methodological frameworks to augment human capabilities via a novel form of physical and cognitive collaboration between human and multi-agent robotic systems, creating Aerial Co-Workers. These machines will actively collaborate with each other and with humans and tackle the fundamental gaps related to human-MAV collaboration at both physical and cognitive levels. The project is organized along two main thrust areas: Physical Collaboration and Cognitive Collaboration. The first thrust aims to significantly augment the physical ability of human workers by taking advantage of physical collaboration between the operator and a network of interconnected quadrotors, equipped with a set of flying hands", transporting objects. This will produce novel scientific solutions for human-robot collaborations to account for the complex legibility of the motions, and the variability of the relative positions of the agents. The second thrust aims to address two perception consensus problems to enable MAV-assisted augmented reality (AR) to augment the cognitive ability of operator(s). The key is to consistently collect, analyze, and display contextual information via multiple MAVs for effective and natural human-robot visual interactions. Aerial Co-Workers will get vantage viewpoints of the environment occluded from the humans which can be customized and augmented directly in the workspace to facilitate human actions via novel metric-semantic collaborative space mapping.This project will have a strong societal impact as a disruptive technology for industry as well as the construction market, which is in urgent need of innovative solutions for enhancing the efficacy while maximizing safety. The outcome will enable safer, faster, and simpler task execution in scenarios including maintenance, inspection, transportation, and search and rescue. The project will contribute to lowering the barriers for new researchers in robotics, computer vision, and machine learning by making hardware designs, algorithms, datasets, and code available on open-source forums. The playful nature of AR tools and quadrotors employed in this project will contribute to engaging K-12 and undergraduate audiences.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.

该项目研究算法基础和方法框架，通过人类和多智能体机器人系统之间的物理和认知协作的新形式来增强人类的能力，创建空中合作者。这些机器将积极地相互协作并与人类协作，并在物理和认知层面上解决与人类-MAV协作相关的根本差距。该项目是组织沿着两个主要的推力领域：物理协作和认知协作。第一个推力旨在通过利用操作员与互连四旋翼网络之间的物理协作来显着增强人类工人的体力，该网络配备了一套“飞手”，用于运输物体。这将为人类-机器人协作产生新的科学解决方案，以解决运动的复杂易读性以及代理的相对位置的可变性。第二个推力旨在解决两个感知共识问题，使MAV辅助增强现实（AR），以增强操作员的认知能力。关键是通过多个MAV一致地收集、分析和显示上下文信息，以实现有效和自然的人机视觉交互。Aerial Co-Workers将获得与人类隔绝的环境的Vantage视角，这些视角可以直接在工作空间中进行定制和增强，以通过新颖的度量语义协作空间映射来促进人类的行动。该项目将作为工业和建筑市场的颠覆性技术产生强大的社会影响，这迫切需要创新的解决方案来提高效率，同时最大限度地提高安全性。其结果将在维护、检查、运输和搜索救援等场景中实现更安全、更快速、更简单的任务执行。该项目将通过在开源论坛上提供硬件设计、算法、数据集和代码，为机器人、计算机视觉和机器学习领域的新研究人员降低障碍。该项目中采用的AR工具和四旋翼的有趣性质将有助于吸引K-12和本科生观众。该奖项反映了NSF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。

项目成果

Modeling and High-Definition Control of a Smart Electroadhesive Actuator: Toward Application in Rehabilitation

智能电粘附执行器的建模和高清控制：面向康复应用

• DOI: 10.1109/tmrb.2022.3216906
• 发表时间: 2022
• 期刊: IEEE Transactions on Medical Robotics and Bionics
• 作者: Feizi, Navid;Atashzar, S. Farokh;Kermani, Mehrdad R.;Patel, Rajni V.
• 通讯作者: Patel, Rajni V.

Power-Based Velocity-Domain Variable Structure Passivity Signature Control for Physical Human-(Tele)Robot Interaction

• DOI: 10.1109/tro.2022.3197932
• 发表时间: 2023-02
• 期刊: IEEE Transactions on Robotics
• 影响因子: 7.8
• 作者: Peter Paik;Smrithi Thudi;S. F. Atashzar

Design Optimization and Data-driven Shallow Learning for Dynamic Modeling of a Smart Segmented Electroadhesive Clutch

• DOI: 10.1109/icra48891.2023.10161225
• 发表时间: 2023-05
• 期刊: 2023 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: N. Feizi;Zahra Bahrami;S. F. Atashzar;M. Kermani;Rajnikant V. Patel

Hand Gesture Recognition via Transient sEMG Using Transfer Learning of Dilated Efficient CapsNet: Towards Generalization for Neurorobotics

• DOI: 10.1109/lra.2022.3191238
• 发表时间: 2022-10-01
• 期刊: IEEE ROBOTICS AND AUTOMATION LETTERS
• 影响因子: 5.2
• 作者: Tyacke，Eion;Reddy，Shreyas P. J.;Atashzar，S. Farokh
• 通讯作者: Atashzar，S. Farokh

RotorTM: A Flexible Simulator for Aerial Transportation and Manipulation

• DOI: 10.1109/tro.2023.3336320
• 发表时间: 2022-05
• 期刊: IEEE Transactions on Robotics
• 影响因子: 7.8
• 作者: Guanrui Li;Xinyang Liu;Giuseppe Loianno