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应用做出贡献，例如应急响应和搜索。 但是，对于某些应用程序，部署大型机器人的成本可能太高，并且首选小型机器人。如此小的机器人的一个普遍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获小尺度上不确定性造成的复杂性。此外，小尺寸限制了传感器和计算有效载荷，这限制了机器人的感知和控制功能。该项目引入了一个数据驱动的建模框架，以通过控制可靠的小型机器人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，并利用西班牙裔服务机构UC Riverside的学生多样性，以扩大代表性不足的群体的参与。该项目研究了机器人环境相互作用中不确定性影响机器人行为的机制。小型机器人运动更加随机，因为执行器处的误差和与环境的不确定相互作用会扩大姿势误差。目的是引入一个平台不稳定的，数据驱动的建模框架，以量化不确定性，然后通过控制将其利用，以在不确定性下以可靠的机器人导航。具体目的是：1）使用有限的数据来对不确定的系统进行建模； 1）提取动力学； 2）基于衍生的还原模型合成基于不确定性的模型控制器； 3）测试和验证具有空中，地面和海洋机器人的理论分析以及衍生模型和控制算法。光谱方法用于提取时空动力学并量化不确定性。模型引用自适应控制方案利用了可靠的机器人导航提取的动力学和不确定性。尽管这项研究中制定的基本原则是基于小型机器人，但该项目的发现可能会推广到具有有限感知和嘈杂的驱动的较大机器人。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和知识分子优点和评估的支持。更广泛的影响审查标准。

项目成果

ACD-EDMD: Analytical Construction for Dictionaries of Lifting Functions in Koopman Operator-Based Nonlinear Robotic Systems

• DOI: 10.1109/lra.2021.3133001
• 发表时间: 2021-11
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Lu Shi;Konstantinos Karydis

Position Control and Variable-Height Trajectory Tracking of a Soft Pneumatic Legged Robot

软体气动腿机器人的位置控制和可变高度轨迹跟踪

• DOI: 10.1109/iros51168.2021.9635966
• 发表时间: 2021
• 期刊: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS
• 作者: Liu, Zhichao;Karydis, Konstantinos
• 通讯作者: Karydis, Konstantinos

Optimal Steering of Stochastic Mobile Robots that Undergo Collisions with their Environment

与环境发生碰撞的随机移动机器人的最优转向

• DOI: 10.1109/robio49542.2019.8961434
• 发表时间: 2019
• 期刊: Optimal Steering of Stochastic Mobile Robots that Undergo Collisions with their Environment
• 作者: Lu, Zhouyu;Karydis, Konstantinos
• 通讯作者: Karydis, Konstantinos

Contact-Prioritized Planning of Impact-Resilient Aerial Robots With an Integrated Compliant Arm

• DOI: 10.1109/tmech.2023.3277811
• 发表时间: 2023-06-19
• 期刊: IEEE-ASME TRANSACTIONS ON MECHATRONICS
• 影响因子: 6.4
• 作者: Liu，Zhichao;Lu，Zhouyu;Karydis，Konstantinos
• 通讯作者: Karydis，Konstantinos

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

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