RI: Small: Collaborative Research: Extracting Dynamics from Limited Data for Modeling and Control of Unmanned Autonomous Systems

RI：小型：协作研究：从有限数据中提取动力学，用于无人自主系统的建模和控制

• 批准号: 1910087
• 负责人: Konstantinos Karydis
• 金额: $ 27万
• 依托单位: University of California-Riverside
• 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: 2019-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910087&HistoricalAwards=false
• 关键词: RI; Small; Collaborative; Research; Extracting

Autonomous robots can contribute to many real-world applications, such as emergency response and search-and-rescue. For some applications, however, the cost of deploying large robots may be too high, and small robots are preferred. A common issue with such small robots is navigating reliably in the presence of uncertainty. Current robot modeling and control approaches cannot capture the intricacies imposed by the effect of uncertainty at small scales. In addition, the small size restricts sensor and computational payloads, which limit the robot's perceptual and control capabilities. This project introduces a data-driven modeling framework to quantify and exploit uncertainty via control for reliable navigation of small robots. The project enables undergraduate students to become involved in research, and capitalizes on the student diversity at UC Riverside, a Hispanic-serving Institution, to broaden participation of under-represented groups. This project investigates the mechanisms that uncertainties in robot-environment interactions affect robot behavior. Small robot motion is more stochastic since errors at the actuators and uncertain interactions with the environment amplify errors in pose. The goal is to introduce a platform-agnostic, data-driven modeling framework to quantify uncertainty and subsequently exploit it via control for reliable robot navigation under uncertainty. The specific aims are to: 1) extract dynamics using limited data for modeling uncertain systems; 2) synthesize uncertainty-aware model-based controllers based on derived reduced-order models; and 3) test and validate theoretical analysis and derived models and control algorithms with aerial, ground, and marine robots. Spectral methods are used to extract spatio-temporal dynamics and to quantify uncertainty. A model-reference adaptive control scheme utilizes extracted dynamics and uncertainty for reliable robot navigation. While the basic principles developed in this research are grounded on small robots, this project's findings may generalize to larger robots with limited sensing and noisy actuation.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.

自主机器人可以为许多现实世界的应用做出贡献，例如应急响应和搜救。 然而，对于某些应用，部署大型机器人的成本可能太高，因此首选小型机器人。此类小型机器人的一个常见问题是在存在不确定性的情况下可靠地导航。当前的机器人建模和控制方法无法捕捉小规模不确定性影响所带来的复杂性。此外，小尺寸限制了传感器和计算负载，从而限制了机器人的感知和控制能力。该项目引入了一个数据驱动的建模框架，通过控制小型机器人的可靠导航来量化和利用不确定性。该项目使本科生能够参与研究，并利用加州大学河滨分校（一所西班牙裔服务机构）的学生多样性，扩大代表性不足群体的参与。该项目研究机器人与环境交互的不确定性影响机器人行为的机制。小型机器人的运动更加随机，因为执行器的误差和与环境的不确定交互会放大姿态误差。目标是引入一个与平台无关、数据驱动的建模框架来量化不确定性，并随后通过控制来利用它，以在不确定性下实现可靠​​的机器人导航。具体目标是： 1）使用有限的数据提取动力学以对不确定系统进行建模； 2）基于导出的降阶模型综合基于不确定性感知模型的控制器； 3) 使用空中、地面和海洋机器人测试和验证理论分析以及衍生模型和控制算法。谱方法用于提取时空动态并量化不确定性。模型参考自适应控制方案利用提取的动力学和不确定性来实现可靠的机器人导航。虽然本研究中开发的基本原理是基于小型机器人，但该项目的研究结果可以推广到传感有限和驱动噪音较大的机器人。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。

项目成果

Toward Impact-resilient Quadrotor Design, Collision Characterization and Recovery Control to Sustain Flight after Collisions

实现抗冲击四旋翼飞行器设计、碰撞特征描述和恢复控制，以在碰撞后维持飞行

• DOI: 10.1109/icra48506.2021.9561089
• 发表时间: 2021-05
• 期刊: Collision Characterization and Recovery Control to Sustain Flight after Collisions
• 作者: Liu, Zhichao;Karydis, Konstantinos
• 通讯作者: Karydis, Konstantinos

SoRX: A Soft Pneumatic Hexapedal Robot to Traverse Rough, Steep, and Unstable Terrain

SoRX：一款软气动六足机器人，可穿越崎岖、陡峭和不稳定的地形

• DOI: 10.1109/icra40945.2020.9196731
• 发表时间: 2020-05-01
• 期刊: 2020 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: Zhichao Liu;Zhouyu Lu;Konstantinos Karydis
• 通讯作者: Konstantinos Karydis

Safely catching aerial micro-robots in mid-air using an open-source aerial robot with soft gripper

使用带有软夹具的开源空中机器人在半空中安全地捕获空中微型机器人

• DOI: 10.3389/frobt.2022.1030515
• 发表时间: 2022-11-02
• 期刊: Frontiers in Robotics and AI
• 影响因子: 3.4
• 作者: Zhichao Liu;Caio Mucchiani;Keran Ye;Konstantinos Karydis
• 通讯作者: Konstantinos Karydis

Online Search-Based Collision-Inclusive Motion Planning and Control for Impact-Resilient Mobile Robots

基于在线搜索的抗冲击移动机器人的包含碰撞的运动规划和控制

• DOI: 10.1109/tro.2022.3211131
• 发表时间: 2022-09-27
• 期刊: IEEE Transactions on Robotics
• 影响因子: 7.8
• 作者: Zhouyu Lu;Zhichao Liu;Merrick Campbell;Konstantinos Karydis
• 通讯作者: Konstantinos Karydis

Enhancement for Robustness of Koopman Operator-based Data-driven Mobile Robotic Systems

增强基于 Koopman 算子的数据驱动移动机器人系统的鲁棒性

• DOI: 10.1109/icra48506.2021.9561343
• 发表时间: 2021-03-01
• 期刊: 2021 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: Lu Shi;Konstantinos Karydis
• 通讯作者: Konstantinos Karydis