CPS: Medium: Collaborative Research: Towards optimal robot locomotion in fluids through physics-informed learning with distributed sensing

CPS：中：协作研究：通过分布式传感的物理信息学习实现流体中的最佳机器人运动

• 批准号: 1932130
• 负责人: Bo Cheng
• 金额: $ 55万
• 依托单位: Pennsylvania State Univ University Park
• 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: 2020-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1932130&HistoricalAwards=false
• 关键词: CPS; Medium; Collaborative; Research; Towards

Fishes are masters of locomotion in fluids owing to their highly integrated biological sensing, computing and motor systems. They are adept at collecting and exploiting rich information from the surrounding fluids for underwater sensing and locomotion control. Inspired and informed by fish swimming, this research aims to develop a novel bio-inspired cyber-physical system (CPS) that integrates the "physical" robot fish and fluid environment with the "cyber" robot control & machine learning algorithms. Specifically, this CPS system includes i) a pressure sensory skin with distributed sensing capability to collect flow information, ii) control and learning algorithms that compute robot motor signals, output by central pattern generators (CPGs) which receive pressure sensory feedback, iii) a robot fish platform to implement and validate the CPS framework for underwater sensing and control tasks, and iv) experimental and computational methods to investigate and model the underlying fluid physics. This CPS system will have immediate impacts on the core CPS research areas such as design, control, data analytics, autonomy, and real-time systems. It will also significantly impact a wide range of engineering applications which demand distributed sensing, control and adaptive actuation. Examples include human-machine interactions, medical robots, unmanned aerial/underwater vehicles, drug dosing, medical therapeutics, and space deployable structures among others. Leveraging the multidisciplinary nature of this research, this award will support a variety of educational and outreach activities. In particular, a list of activities in broadening participation in engineering will be carried out.This research project integrates multiple CPS technologies to develop bio-inspired technologies for swarm control of fish. These include innovation in sensing modality via a stretchable, pressure sensitive skin, physics inspired learning and swarm control. The project will first develop a distributed pressure sensitive synthetic skin, which will be installed on robotic fishes to map the pressure distribution on their body and caudal-fin surfaces. The distributed pressure information will then be used in a feedback control policy that modulates CPGs to produce caudal-fin motion patterns of the robotic fishes. The control policy and the caudal-fin motion patterns will be optimized via reinforcement learning first in a surrogate fluid environment and then in the true fluid environment. The surrogate fluid environment will be developed using data-driven non-parametric models informed by physics-based hydrodynamic models of fish swimming, trained using combined experimental and Computational Fluid Dynamics (CFD) simulation data. The above control-learning methods will also be used to achieve efficient schooling in a group of robotic fishes, individually controlled by a CPG, which interacts with each other through surrounding fluids and pressure sensory feedback. The optimized swimming/schooling performance of robotic fishes and the underlying physics will be studied using CFD simulation. Together, this research will advance CPS knowledge on: 1) the design and creation of electronic and sensor materials and devices for robot skin applications; 2) the development of data-efficient, physics-informed learning methods for robotic systems that operate in complex environments, especially leveraging the recent progress on deep learning to exploit the spatial and temporal richness of the pressure data for underwater sensing and robot control; and 3) the flow physics and modeling of fish swimming.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.

鱼类因其高度集成的生物传感、计算和运动系统而成为流体运动的大师。它们擅长收集和利用周围流体的丰富信息进行水下传感和运动控制。受鱼类游泳的启发和启发，这项研究旨在开发一种新型仿生网络物理系统（CPS），将“物理”机器鱼和流体环境与“网络”机器人控制和机器学习算法集成在一起。具体来说，该 CPS 系统包括 i) 具有分布式传感能力的压力传感皮肤，用于收集流量信息，ii) 计算机器人电机信号的控制和学习算法，由接收压力传感反馈的中央模式发生器 (CPG) 输出，iii)机器鱼平台来实施和验证用于水下传感和控制任务的 CPS 框架，以及 iv) 实验和计算方法来研究和建模基础流体物理。该 CPS 系统将对设计、控制、数据分析、自治和实时系统等核心 CPS 研究领域产生直接影响。它还将对需要分布式传感、控制和自适应驱动的广泛工程应用产生重大影响。例子包括人机交互、医疗机器人、无人机/水下航行器、药物剂量、医疗治疗和空间可部署结构等。利用这项研究的多学科性质，该奖项将支持各种教育和外展活动。特别是，将开展一系列扩大工程参与的活动。该研究项目整合了多种 CPS 技术，开发用于鱼类群体控制的仿生技术。其中包括通过可拉伸、压敏皮肤实现的传感方式创新、受物理启发的学习和群体控制。该项目将首先开发一种分布式压力敏感合成皮肤，将其安装在机器鱼上，以绘制其身体和尾鳍表面的压力分布图。然后，分布式压力信息将用于反馈控制策略，该策略调节 CPG 以产生机器鱼的尾鳍运动模式。控制策略和尾鳍运动模式将通过强化学习首先在替代流体环境中然后在真实流体环境中进行优化。替代流体环境将使用数据驱动的非参数模型来开发，该模型由基于物理的鱼类游泳流体动力学模型提供信息，并使用组合实验和计算流体动力学（CFD）模拟数据进行训练。上述控制学习方法还将用于在一组机器鱼中实现高效的群体训练，这些机器鱼由 CPG 单独控制，这些机器鱼通过周围的流体和压力传感反馈相互交互。将使用 CFD 模拟来研究机器鱼的优化游泳/成群性能和基础物理。总之，这项研究将推进 CPS 知识：1）用于机器人皮肤应用的电子和传感器材料和设备的设计和创建； 2）为在复杂环境中运行的机器人系统开发数据高效、基于物理的学习方法，特别是利用深度学习的最新进展，利用压力数据的空间和时间丰富性进行水下传感和机器人控制； 3) 鱼类游泳的流动物理和建模。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势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

Development of an Autonomous Modular Swimming Robot with Disturbance Rejection and Path Tracking

• DOI: 10.1109/iros55552.2023.10341571
• 发表时间: 2023-10-01
• 期刊: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Hankun Deng;Colin Nitroy;Kundan Panta;Donghao Li;S. Priya;Bo Cheng
• 通讯作者: Bo Cheng

Effects of Design and Hydrodynamic Parameters on Optimized Swimming for Simulated, Fish-inspired Robots

设计和水动力参数对模拟鱼类机器人优化游泳的影响

• DOI: 10.1109/iros47612.2022.9981478
• 发表时间: 2022-10
• 期刊: IEEE
• 作者: Li, Donghao;Deng, Hankun;Bayiz, Yagiz E.;Cheng, Bo
• 通讯作者: Cheng, Bo

Data Fusion and Pattern Classification in Dynamical Systems Via Symbolic Time Series Analysis

通过符号时间序列分析进行动态系统中的数据融合和模式分类

• DOI: 10.1115/1.4062830
• 发表时间: 2023-09
• 期刊: and Control
• 作者: Chen, Xiangyi;Ray, Asok
• 通讯作者: Ray, Asok

Deep Reinforcement Learning Control of a Boiling Water Reactor

沸水反应堆的深度强化学习控制

• DOI: 10.1109/tns.2022.3187662
• 发表时间: 2022-08
• 期刊: IEEE Transactions on Nuclear Science
• 影响因子: 1.8
• 作者: Chen, Xiangyi;Ray, Asok
• 通讯作者: Ray, Asok

Robot motor learning shows emergence of frequency-modulated, robust swimming with an invariant Strouhal number

机器人运动学习显示出具有不变斯特劳哈尔数的调频、稳健游泳的出现

• DOI: 10.1098/rsif.2024.0036
• 发表时间: 2024-03
• 期刊: Journal of The Royal Society Interface
• 作者: Deng, Hankun;Li, Donghao;Nitroy, Colin;Wertz, Andrew;Priya, Shashank;Cheng, Bo
• 通讯作者: Cheng, Bo