NRI: FND: Action-perception loops over 5G millimeter wave wireless for cooperative manipulation

NRI：FND：通过 5G 毫米波无线进行动作感知循环以进行协作操纵

• 批准号: 1925079
• 负责人: Ludovic Righetti
• 金额: $ 75万
• 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: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1925079&HistoricalAwards=false
• 关键词: NRI; FND; Action; perception; loops

As the autonomy of robots performing manipulation or legged locomotion tasks increases, they require an ever-growing amount of computational resources to successfully perceive their environment and make decisions. Yet, mobile robots are fundamentally constrained by weight, shape and power autonomy which impose important limits on the computational capabilities they can carry. As a possible answer to such dilemma, cloud robotics aims to move computation to remote servers, but it has thus far remained an elusive approach for tasks that necessitate low delay communication and high data bandwidth between the robot and the cloud. Such tasks include control, planning and perception algorithms for object manipulation and legged locomotion. The 5th generation of cellular network technology (5G) could revolutionize cloud robotics as it promises unprecedented access to high bandwidth and low latency wireless communication. Yet, formidable challenges remain to ensure communication reliability, safety of robotic operation under communication degradation, and scalability to multi-robot systems. This project aims to fully incorporate 5G technology into robotics systems performing complex manipulation and locomotion tasks. It will develop novel perception, control and planning algorithms that optimally distribute computations between robots and the cloud for guaranteed safe robotic operation. Ultimately, these algorithms will accelerate the ubiquitous deployment of untethered 5G-enabled robots in human environments and unlock a large range of applications for healthcare, service and industrial robotics.The project takes a holistic approach to control, perception and communication to establish the foundations of edge-based wireless real-time action-perception loops for autonomous robots. It is organized along four main thrusts of research. First, it will investigate novel optimal control and planning algorithms distributed between the network edge and the robot with performance guarantees under communication degradation. Second, it will propose efficient computational partitioning techniques for real-time perception using multi-modal sensing with high data rates. Third, it will characterize 5G specific communication channels in a robotics environment via experiments and simulations and design new mmWave communication protocols tailored for real-time robotic action-perception loops. Finally, extensive experiments on single and multi-robot systems, including fixed and mobile manipulators and a quadruped robot, will demonstrate the unique capabilities of 5G-enabled robotic systems. The outreach activities of the project will contribute to lowering barriers to entry for scientists and industries that seek to exploit 5G-enabled robotics through open-source distribution of algorithms and dissemination of results via NYU WIRELESS. This effort will contribute to the education of undergraduate and graduate students, leveraging its outcomes for curriculum development and offering supervised projects with the possibility to work directly on state-of-the-art experimental platforms.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.

随着机器人执行操纵或腿部运动任务的自主性增加，它们需要不断增长的计算资源来成功感知环境并做出决策。然而，移动的机器人从根本上受到重量、形状和功率自主性的约束，这对它们可以携带的计算能力施加了重要限制。作为这种困境的一个可能的答案，云机器人旨在将计算转移到远程服务器，但迄今为止，对于机器人和云之间需要低延迟通信和高数据带宽的任务，它仍然是一种难以捉摸的方法。这些任务包括用于物体操纵和腿部运动的控制、规划和感知算法。第五代蜂窝网络技术（5G）可能会彻底改变云机器人技术，因为它承诺前所未有地获得高带宽和低延迟的无线通信。然而，仍然存在着巨大的挑战，以确保通信可靠性，通信退化下的机器人操作的安全性，以及多机器人系统的可扩展性。该项目旨在将5G技术完全融入执行复杂操作和运动任务的机器人系统。它将开发新的感知、控制和规划算法，在机器人和云之间优化分配计算，以保证机器人的安全操作。最终，这些算法将加速无束缚的5G机器人在人类环境中的无处不在的部署，并为医疗保健，服务和工业机器人的大量应用解锁。该项目采用控制，感知和通信的整体方法，为自主机器人建立基于边缘的无线实时动作感知回路的基础。它是沿着沿着四个主要研究方向组织的。首先，它将研究新的最优控制和规划算法分布在网络边缘和机器人之间的通信退化下的性能保证。其次，它将提出有效的计算分区技术，用于实时感知，使用高数据速率的多模态传感。第三，它将通过实验和模拟来表征机器人环境中的5G特定通信信道，并设计为实时机器人动作感知环路量身定制的新毫米波通信协议。最后，对单个和多个机器人系统进行的广泛实验，包括固定和移动的机械手以及四足机器人，将展示5G机器人系统的独特功能。该项目的推广活动将有助于降低科学家和行业的准入门槛，这些科学家和行业试图通过开源算法分发和通过NYU WIRELESS传播结果来利用5G机器人技术。这一努力将有助于本科生和研究生的教育，利用其成果进行课程开发，并提供有可能直接在最先进的实验平台上工作的监督项目。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

Capacity Bounds for Communication Systems with Quantization and Spectral Constraints

• DOI: 10.1109/isit44484.2020.9174260
• 发表时间: 2020-01
• 期刊: 2020 IEEE International Symposium on Information Theory (ISIT)
• 作者: S. Dutta;Abbas Khalili;E. Erkip;S. Rangan

Millimeter Wave Channel Modeling via Generative Neural Networks

• DOI: 10.1109/gcwkshps50303.2020.9367420
• 发表时间: 2020-08
• 期刊: 2020 IEEE Globecom Workshops (GC Wkshps
• 作者: William Xia;S. Rangan;M. Mezzavilla;A. Lozano;Giovanni Geraci;V. Semkin;Giuseppe Loianno

High-Frequency Nonlinear Model Predictive Control of a Manipulator

机械手的高频非线性模型预测控制

• DOI: 10.1109/icra48506.2021.9560990
• 发表时间: 2021
• 期刊: 2021 IEEE-RAS International Conference on Robotics and Automation (ICRA
• 作者: Kleff, Sebastien;Meduri, Avadesh;Budhiraja, Rohan;Mansard, Nicolas;Righetti, Ludovic
• 通讯作者: Righetti, Ludovic

6G Enabling Technologies

• DOI: 10.1007/978-3-030-72777-2_3
• 发表时间: 2021
• 期刊: Computer Communications and Networks
• 作者: Michele Polese;M. Giordani;M. Mezzavilla;S. Rangan;M. Zorzi

Millimeter Wave Wireless Assisted Robot Navigation With Link State Classification

• DOI: 10.1109/ojcoms.2022.3155572
• 发表时间: 2021-10
• 期刊: IEEE Open Journal of the Communications Society
• 影响因子: 7.9
• 作者: Mingsheng Yin;A. Veldanda;Amee Trivedi;Jeff Zhang;K. Pfeiffer;Yaqi Hu;S. Garg;E. Erkip;L. Righetti;S. Rangan