CAREER: MaLPhySiCS - Machine Learning-assisted Physics-based Simulation and Control of Soft robots

职业：MaLPhySiCS - 机器学习辅助的基于物理的软机器人仿真和控制

• 批准号: 2047663
• 负责人: Mohammad Khalid Jawed
• 金额: $ 70万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047663&HistoricalAwards=false
• 关键词: CAREER; MaLPhySiCS; Machine; Learning; assisted

This Faculty Early Career Development (CAREER) grant will support research that will formulate and experimentally validate numerical tools for modeling of soft robots. Soft robots are typically designed and controlled through a painstaking trial-and-error process involving several prototypes. This project seeks to automate this design and control process using a real-time physics-engine. The simulation addresses several key challenges in simulation of robots: (1) large structural deformation, (2) nontrivial coupling with hydrodynamic forces, and (3) preponderance of contact and collision. Model reduction is necessary to cast a complex robot into the simulation framework. Machine learning (ML) provides an exceptional set of tools to reduce complex structures into a model without compromising accuracy. The fast simulation tool, based on physics and ML, will be used to build a control framework for a bacteria-inspired soft robot.The research objective of this project is formulation of fast and efficient physics-based simulation, assisted by machine learning, for autonomous control of soft robots. Using this framework, a macroscale bacteria-inspired robot will be designed and controlled. This robot will use buckling in flagellum (thin flexible tail) to control its swimming direction. This is expected to be the simplest autonomous soft robot with a single scalar control input. Two key challenges to be tackled in the project are: (1) computational efficiency so that the simulation can be used for optimization, and (2) physical accuracy and robustness of the models so that model-based control can be employed on the real robotic systems. Towards this goal, machine learning-assisted modeling of complex systems in a discrete differential geometry-based simulation framework is planned. Neural network-based models for the structure of the robot and the hydrodynamics will be developed. These models are expected to be as fast as simplified heuristic models and as accurate as physics-based fine-grained models. This simulation tool will be used to develop a model-based control framework for the bacteria-inspired robot for untethered autonomous operation. This robot can help us gain insight into bacterial locomotion, e.g., role of instability in bacterial propulsion. From a robotics perspective, the robot has only one control input with minuscule number of moving parts. The design of the robot makes it amenable for miniaturization to sub-millimeter scale with potential biomedical applications.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.

这笔学院早期职业发展(Career)补助金将支持研究，这些研究将制定和实验验证用于软机器人建模的数值工具。软机器人通常是通过涉及几个原型的艰苦试错过程来设计和控制的。该项目寻求使用实时物理引擎来自动化这一设计和控制过程。该仿真解决了机器人仿真中的几个关键挑战：(1)大的结构变形，(2)与流体动力的非平凡耦合，(3)接触和碰撞的优势。为了将一个复杂的机器人引入到仿真框架中，需要进行模型降阶。机器学习(ML)提供了一套特殊的工具，可以在不影响精度的情况下将复杂的结构简化为模型。基于物理和ML的FAST仿真工具将用于构建细菌启发的软机器人的控制框架，本项目的研究目标是建立快速高效的基于物理的仿真，辅助机器学习，用于软机器人的自主控制。利用这个框架，将设计和控制一个大规模的细菌启发机器人。该机器人将利用鞭毛(细长灵活的尾巴)中的屈曲来控制其游泳方向。预计这将是最简单的具有单一标量控制输入的自主软机器人。该项目需要解决的两个关键挑战是：(1)计算效率，以便仿真可以用于优化；(2)模型的物理精度和稳健性，以便基于模型的控制可以应用于真实的机器人系统。为了实现这一目标，基于离散微分几何的仿真框架中的机器学习辅助复杂系统建模被规划出来。将开发基于神经网络的机器人结构和流体动力学模型。这些模型预计将与简化的启发式模型一样快，与基于物理的细粒度模型一样准确。该仿真工具将用于为细菌启发的机器人开发基于模型的控制框架，以实现无绳自主操作。这个机器人可以帮助我们深入了解细菌的运动，例如，不稳定性在细菌推进中的作用。从机器人学的角度来看，机器人只有一个控制输入，移动部件的数量很少。机器人的设计使其适合微型化到亚毫米级，具有潜在的生物医学应用。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。

项目成果

Agronav: Autonomous Navigation Framework for Agricultural Robots and Vehicles Using Semantic Segmentation and Semantic Line Detection

Agronav：使用语义分割和语义线检测的农业机器人和车辆自主导航框架

• 发表时间: 2023
• 期刊: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR
• 作者: Panda, S. K.

Automated Stability Testing of Elastic Rods With Helical Centerlines Using a Robotic System

使用机器人系统对具有螺旋中心线的弹性杆进行自动稳定性测试

• DOI: 10.1109/lra.2021.3138532
• 发表时间: 2022
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Tong, Dezhong;Borum, Andy;Jawed, Mohammad Khalid
• 通讯作者: Jawed, Mohammad Khalid

Neural-Kalman GNSS/INS Navigation for Precision Agriculture

• DOI: 10.1109/icra48891.2023.10161351
• 发表时间: 2023-05
• 期刊: 2023 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: Yayun Du;Swapnil Sayan Saha;S. Sandha;Arthur Lovekin;Jason Wu;S. Siddharth;M. Chowdhary;M. Jawed;M. Srivastava

A Fully Implicit Method for Robust Frictional Contact Handling in Elastic Rods

• DOI: 10.1016/j.eml.2022.101924
• 发表时间: 2022-05
• 期刊: ArXiv
• 作者: Dezhong Tong;Andrew Choi;Jungseock Joo;M. Jawed

Snap Buckling in Overhand Knots

反手结中的卡扣屈曲

• DOI: 10.1115/1.4056478
• 发表时间: 2023
• 期刊: Journal of Applied Mechanics
• 作者: Tong, Dezhong;Choi, Andrew;Joo, Jungseock;Borum, Andy;Khalid Jawed, Mohammad
• 通讯作者: Khalid Jawed, Mohammad