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Embodied Sensing and Control in Fish-like Swimming Robots via Passive Degrees of Freedom

通过被动自由度在类鱼游泳机器人中实现传感和控制

基本信息

批准号：
2021612
负责人：
Phanindra Tallapragada
金额：
$ 44.66万
依托单位：
Clemson University
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：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2021612&HistoricalAwards=false
关键词：
Embodied Sensing Control Fish like

项目摘要

This Dynamics, Controls, and System Diagnostics award will support fundamental research into the analysis and design of fish-like swimming robots, enabling the use of passively articulated body segments to sense ambient flow characteristics and respond to hydrodynamic stimuli. This type of embodied sensing and control of locomotion is seen in swimming animals like fish, where sensory awareness of velocity and acceleration is used as feedback for control. The project will also incorporate active and passive internal rotors to extend beyond more familiar bio-mimetic approaches. The project incorporates ideas from several disciplines and combines theoretical models, numerical simulations, and experiments to translate these ideas to mechanical systems. The new capabilities for swimming robots will be particularly significant for power-limited underwater robots in sensory-deprived environments. The results of the project will enable the design of improved underwater robots for exploration, search-and-rescue, and inspection and maintenance of underwater infrastructure. The project will provide research training and career preparation to doctoral students, offer research and educational opportunities for undergraduate students through design projects, and will reach out to K-12 school students to attract them to STEM disciplines.This project seeks to investigate the role of passive degrees of freedom (PDOF) in aquatic robots that can act as both sensors and controllers. The dynamics of PDOF can encode the hydrodynamic forces and thus act as sensors. At the same time the motion of PDOF can be influenced by the hydrodynamic forces; if such motion is harnessed by appropriately designed PDOF, they can act as controllers. The project will improve our understanding of how the swimming systems state in a potential well encodes information about gaits and propulsive efficiency and how transitions between potential wells can be triggered by environmental disturbances or inputs that lead to fast and agile motion. The project will use machine learning methods to understand how the kinematics of a swimmer encode hydrodynamic information and how this can be used as part of multimodal sensory suite. Such embodied sensing and control would greatly reduce or in some cases bypass the role of electronic information processing and computation. The concepts of embodied sensing in aquatic robots can be applied to existing designs of robots by augmenting existing sensors for better sensor fusion algorithms. The modeling framework will also directly be applicable to several classes of terrestrial mobile robotic systems, where passive self-stabilization and agility could be improved by employing PDOF for embodied sensing and control. The specific research goals and plans will combine cross disciplinary modeling techniques from nonlinear dynamics, fluid mechanics, machine learning and bioinspired robotics.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.

该动力学，控制和系统诊断奖将支持对鱼类游泳机器人的分析和设计的基础研究，使被动关节身体部分的使用能够感知环境流动特性并响应流体动力学刺激。这种对运动的具体感知和控制在鱼类等游泳动物中可见，其中对速度和加速度的感知被用作控制的反馈。该项目还将采用主动和被动内部转子，以超越更熟悉的仿生方法。该项目融合了来自多个学科的想法，并结合了理论模型，数值模拟和实验，将这些想法转化为机械系统。游泳机器人的新功能对于在感官剥夺环境中的动力有限的水下机器人将特别重要。该项目的成果将有助于设计改进的水下机器人，用于勘探、搜索和救援以及水下基础设施的检查和维护。该项目将为博士生提供研究培训和职业准备，通过设计项目为本科生提供研究和教育机会，并将接触K-12学校的学生，以吸引他们进入STEM学科。该项目旨在研究被动自由度（PDOF）在可用作传感器和控制器的水生机器人中的作用。PDOF的动力学可以编码流体动力，从而充当传感器。同时，PDOF的运动可以受到水动力的影响;如果这种运动被适当设计的PDOF利用，它们可以充当控制器。该项目将提高我们对游泳系统在势阱中的状态如何编码有关步态和推进效率的信息以及势威尔斯之间的转换如何被环境干扰或导致快速敏捷运动的输入触发的理解。该项目将使用机器学习方法来了解游泳运动员的运动学如何编码流体动力学信息，以及如何将其用作多模态感觉套件的一部分。这种具体化的感测和控制将大大减少或在某些情况下绕过电子信息处理和计算的作用。通过增强现有传感器以获得更好的传感器融合算法，可以将水生机器人中的具体感知概念应用于现有的机器人设计。建模框架也将直接适用于几类陆地移动的机器人系统，其中被动自稳定和敏捷性可以通过采用PDOF具体的传感和控制来提高。具体的研究目标和计划将结合联合收割机跨学科建模技术，从非线性动力学，流体力学，机器学习和bioinspired robotics.This奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。