NRI/Collaborative Research: Improving the Safety and Agility of Robotic Flight with Bat-Inspired Flexible-Winged Robots

NRI/合作研究:利用蝙蝠启发的柔性翼机器人提高机器人飞行的安全性和敏捷性

基本信息

项目摘要

Bat flight, perhaps the most advanced and efficient form of animal flight, has long been a source of inspiration for roboticists and biologists alike. This National Robotics Initiative (NRI) collaborative research award supports research aimed at understanding and reproducing the unparalleled agility and resilience of bat flight. Biological studies of bats (their structure, muscle movement, and flight dynamics) will drive the engineering development of mathematical models of robotic flight and the eventual design and implementation of a prototype 30-80cm bat-like robot. The physical flight capabilities of the robot will be augmented with perception and reasoning abilities, with the aim of providing support for construction site activities such as site monitoring, inspection, and general surveillance of the work site to provide image data to enhance situational awareness of human workers. The research involves several disciplines, including biology, aerodynamics, robotics, control systems engineering, and construction engineering.Aerial robots have nowhere near the agility and efficiency of animal flight, especially in complex, constrained environments. This is not surprising since even the simplest winged robots have complex flight dynamics that pose significant challenges for modeling, design, and control. In the case of bat-inspired robots, these difficulties are exacerbated by the use of under-actuated mechanisms driving wings constructed from flexible membranes. This project will combine biological and engineering research to address these problems. Biological research on the kinematics of bats and their flight will provide a basis for mechanical designs. To control the robot, agile motion planning and flight control algorithms will employ motion primitives that are derived from biological investigation of the dynamics of bat flight. Conversely, models obtained from biological studies will be validated by experimental investigations using the prototype robot, enabling iterative refinement of reduced-order models and control algorithms. Ultimately, the robots will be equipped with sensing systems and planning algorithms, to facilitate localization, mapping, inspection and surveillance at construction sites.
蝙蝠飞行也许是最先进和最有效的动物飞行形式,长期以来一直是机器人学家和生物学家灵感的源泉。这个国家机器人倡议(NRI)合作研究奖支持旨在了解和复制蝙蝠飞行的无与伦比的敏捷性和弹性的研究。对蝙蝠的生物学研究(它们的结构、肌肉运动和飞行动力学)将推动机器人飞行数学模型的工程开发,并最终设计和实现一个30-80厘米长的蝙蝠状机器人原型。机器人的物理飞行能力将增加感知和推理能力,目的是为建筑工地活动提供支持,如工地监控、检查和对工地的一般监视,以提供图像数据,以增强人类工人的情景意识。这项研究涉及多个学科,包括生物学、空气动力学、机器人学、控制系统工程和建筑工程。传统的机器人远不及动物飞行的敏捷性和效率,特别是在复杂、受限的环境中。这并不令人惊讶,因为即使是最简单的有翼机器人也有复杂的飞行动力学,这给建模、设计和控制带来了巨大的挑战。在以蝙蝠为灵感的机器人的情况下,这些困难因使用欠驱动机构来驱动由柔性膜构成的翅膀而加剧。该项目将结合生物学和工程学研究来解决这些问题。对蝙蝠运动学和飞行的生物学研究将为机械设计提供基础。为了控制机器人,灵活的运动规划和飞行控制算法将使用从蝙蝠飞行动力学的生物学研究中衍生出来的运动原语。相反,从生物学研究中获得的模型将通过使用原型机器人的实验研究进行验证,从而能够迭代改进降阶模型和控制算法。最终,这些机器人将配备传感系统和规划算法,以促进建筑工地的定位、测绘、检查和监控。

项目成果

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Seth Hutchinson其他文献

Integration Framework for Improved Visual Servoing in Image and Cartesian Spaces
用于改进图像和笛卡尔空间中的视觉伺服的集成框架
Game theory as a unifying structure for a variety of robot tasks
博弈论作为各种机器人任务的统一结构
Leveraging Dynamic Modeling of Cart-like Nonholonomic Systems to Improve Contact Point’s Location and Control
利用类推车非完整系统的动态建模来改进接触点的定位和控制
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Sergio Aguilera;Seth Hutchinson
  • 通讯作者:
    Seth Hutchinson
Control of Cart-Like Nonholonomic Systems Using a Mobile Manipulator
使用移动机械手控制类车非完整系统
Modeling and Inertial Parameter Estimation of Cart-like Nonholonomic Systems Using a Mobile Manipulator
使用移动机械手的类车非完整系统的建模和惯性参数估计

Seth Hutchinson的其他文献

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{{ truncateString('Seth Hutchinson', 18)}}的其他基金

RI:Small: Capturing, Perceiving, and Rendering of Artistic Skills for Real-time Interactive Creation of Art
RI:Small:捕捉、感知、渲染艺术技巧,实现艺术的实时互动创作
  • 批准号:
    2008302
  • 财政年份:
    2020
  • 资助金额:
    $ 150万
  • 项目类别:
    Continuing Grant
NSF-CONACYT Collaborative Research: Search, Surveillance, and Pursuit by Autonomous Robots
NSF-CONACYT 合作研究:自主机器人的搜索、监视和追踪
  • 批准号:
    0725444
  • 财政年份:
    2007
  • 资助金额:
    $ 150万
  • 项目类别:
    Standard Grant
NSF-CONACyT Collaborative Research on Sensor-based Robotics
NSF-CONACyT 基于传感器的机器人合作研究
  • 批准号:
    0116560
  • 财政年份:
    2002
  • 资助金额:
    $ 150万
  • 项目类别:
    Standard Grant
Real-time Path Planning in Changing Environments
不断变化的环境中的实时路径规划
  • 批准号:
    0083275
  • 财政年份:
    2000
  • 资助金额:
    $ 150万
  • 项目类别:
    Continuing Grant
CONACyT: Visual Servo Control of Robotic Systems
CONACyT:机器人系统的视觉伺服控制
  • 批准号:
    9613737
  • 财政年份:
    1996
  • 资助金额:
    $ 150万
  • 项目类别:
    Standard Grant
Integration of Vision and Force Feedback for the Synthesis and Execution of Error-Tolerant Robot Motion Plans
视觉和力反馈的集成用于容错机器人运动计划的合成和执行
  • 批准号:
    9110270
  • 财政年份:
    1991
  • 资助金额:
    $ 150万
  • 项目类别:
    Standard Grant

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