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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

基本信息

批准号：
1931929
负责人：
Haibo Dong
金额：
$ 32.48万
依托单位：
University of Virginia Main Campus
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 2022-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931929&HistoricalAwards=false
关键词：
CPS Medium Collaborative Research Towards

项目摘要

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 inthanovations in a pressure sensitive skin 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.

鱼类是流体中运动的大师，因为它们高度集成的生物传感、计算和运动系统。它们擅长从周围流体中收集和利用丰富的信息，用于水下传感和运动控制。受鱼类游动的启发和启发，本研究旨在开发一种新颖的、受生物启发的网络物理系统(CPS)，它集成了？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。具体地说，该CPS系统包括：i)具有分布式传感能力的压力传感皮肤以收集流动信息；ii)控制和学习算法，其计算由接收压力传感反馈的中央模式产生器(CPG)输出的机器人电机信号；iii)机器鱼平台，用于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，以调查和模拟潜在的流体物理。这一CPS系统将立即对CPS的核心研究领域产生影响，如设计、控制、数据分析、自治和实时系统。它还将对需要分布式传感、控制和自适应执行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、无人驾驶飞行器/水下航行器、给药、医疗治疗和空间可展开结构等。利用这项研究的多学科性质，该奖项将支持各种教育和外联活动。特别是，将开展一系列扩大工程学参与的活动。这项研究项目整合了多种CPS技术，以开发生物灵感技术来控制鱼类种群。其中包括一个压力敏感皮肤项目的创新，该项目将首先开发一种分布式压力敏感合成皮肤，这种皮肤将安装在机器鱼身上，以绘制它们身体和尾鳍表面的压力分布。然后，分布的压力信息将被用于反馈控制策略，该反馈控制策略调节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首先在代理流体环境中，然后在真实流体环境中通过强化学习进行优化。代理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，并使用组合的实验和计算流体动力学(CFD)模拟数据进行训练。上述控制-学习方法也将用于实现一组机器鱼的有效鱼群，这些机器鱼由CPG单独控制，通过周围的液体和压力感觉反馈相互作用。我们将利用CFD模拟来研究机器鱼的优化游动/鱼群性能及其背后的物理机制。这项研究将促进CPS在以下方面的知识：1)设计和创造用于机器人皮肤应用的电子和传感器材料和设备；2)为在复杂环境中运行的机器人系统开发数据高效、物理知情的学习方法，特别是利用深度学习方面的最新进展，利用压力数据的空间和时间丰富性，用于水下传感和机器人控制；以及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。