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.

这项教师的早期职业发展（职业）赠款将支持将制定和实验验证用于模拟软机器人的数值工具的研究。软机器人通常是通过涉及多个原型的艰苦试验过程来设计和控制的。该项目旨在使用实时物理引擎来自动化此设计和控制过程。该模拟解决了机器人模拟的几个关键挑战：（1）大结构变形，（2）与流体动力的非平凡耦合，以及（3）接触和碰撞的优势。降低模型对于将复杂的机器人施放到仿真框架中是必要的。机器学习（ML）提供了一组非凡的工具，可以将复杂的结构减少到模型中，而不会损害准确性。基于物理和ML的快速模拟工具将用于为细菌启发的软机器人构建控制框架。该项目的研究目标是根据机器学习的辅助制定基于快速有效的基于物理学的模拟，以自主控制软机器人。使用此框架，将设计和控制一个宏观的细菌启发的机器人。该机器人将在鞭毛（薄柔性尾巴）中使用屈曲来控制其游泳方向。预计这将是具有单个标量控件输入的最简单的自主软机器人。项目中要解决的两个主要挑战是：（1）计算效率，以便模拟可用于优化，以及（2）模型的物理准确性和鲁棒性，以便可以在实际机器人系统上使用基于模型的控制。为了实现这一目标，计划在基于离散差异几何的模拟框架中对机器学习辅助建模。将开发用于机器人和流体动力学结构的基于神经网络的模型。预计这些模型将与简化的启发式模型一样快，并且与基于物理的细粒模型一样准确。该仿真工具将用于开发基于模型的控制框架，用于细菌启发的机器人，用于不受束缚的自主操作。该机器人可以帮助我们深入了解细菌运动，例如不稳定性在细菌推进中的作用。从机器人的角度来看，机器人只有一个控件输入，其运动部件数量很小。机器人的设计使其可以通过潜在的生物医学应用进行小型化到亚毫米量表。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更广泛影响的评估评估来支持的。

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

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

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

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