Towards Accurate and Efficient Dynamics Modeling and Control for Soft Robots in Unstructured Environments

非结构化环境中软机器人的准确高效的动力学建模和控制

• 批准号: 1929729
• 负责人: Chengzhi Yuan
• 金额: $ 44.56万
• 依托单位: University of Rhode Island
• 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: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1929729&HistoricalAwards=false
• 关键词: Towards; Accurate; Efficient; Dynamics; Modeling

The field of soft robotics is growing rapidly in recent years, with the rising demand for advanced robotic systems to operate efficiently, robustly, intelligently, and safely under unstructured environments. Compared to traditional rigid robotic systems, soft robots are highly dexterous in the sense that they can deform their soft body to adapt to clustered environments without damage or jamming. In addition, soft robots can be made to be low-cost, low-power, lightweight, and human friendly, promoting many applications that are otherwise not available, such as picking up fragile objects (e.g., tomatoes) in agriculture harvesting, non-invasive endoscopic surgery in medical applications, and soft robotic grippers in deep-sea exploration, etc. However, implementation of soft robots in real-world applications presents challenges in many technological aspects, including material, sensing, modeling, and control. The research of soft robotics is still in its primitive stage as a unified framework for the design, modeling, and control of highly dexterous soft robots is still lacking. This project supports fundamental research to provide the knowledge needed to fill this important technology void, so as to promote a wide range of soft robot applications, e.g., in-space/underwater exploration, aerospace, healthcare, biomedical, and agricultural industries. Therefore, results from this research will not only promote the progress of robotics science and engineering, but also benefit the U.S. economy, society, and national defense. This research involves multiple disciplines including robotics, control theory, machine learning, computational mechanics, and material science. The interdisciplinary approach will help boost minority involvement in scientific research and promote engineering education.The goal of this project is to investigate an efficient and accurate dynamics modeling and control framework for soft robotic manipulation and locomotion under unstructured environments. Specifically, the research team will develop modular plug-and-play soft robotic arms with distributed power, actuation, and tactile sensing. A three-dimensional, reduced-order, geometrically exact, finite element model of soft robotic arms with internal actuation forces from tendon contraction will be formulated and implemented. An efficient and accurate simulation program for the dynamics of soft robots interacting with the environments will be developed. Dynamic controllers, hybridizing model-based and data-driven control strategies, will also be devised and tested. Through a series of simulations and physical experiments, the team will identify fundamental design principles and develop controllers for enabling autonomous soft robots such as elephant trunk-like dexterous soft manipulators and octopus-like walking/swimming soft robots.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.

近年来，软机器人领域发展迅速，对先进机器人系统的需求不断增加，以在非结构化环境下高效，稳健，智能和安全地运行。与传统的刚性机器人系统相比，柔性机器人非常灵巧，因为它们可以使其柔软的身体变形以适应集群环境而不会损坏或卡住。此外，软机器人可以被制造成低成本、低功耗、重量轻和人性化的，促进了许多其他方式无法实现的应用，例如拾取易碎物体（例如，农业收获中的柔性机器人（例如西红柿）、医疗应用中的非侵入式内窥镜手术以及深海勘探中的柔性机器人抓取器等。然而，柔性机器人在现实应用中的实现在许多技术方面提出了挑战，包括材料、传感、建模和控制。软机器人的研究仍处于初级阶段，作为一个统一的框架，高度灵巧的软机器人的设计，建模和控制仍然缺乏。该项目支持基础研究，以提供填补这一重要技术空白所需的知识，从而促进软机器人的广泛应用，例如，太空/水下探索、航空航天、医疗保健、生物医学和农业行业。因此，这项研究成果不仅将促进机器人科学与工程的进步，而且将有利于美国的经济、社会和国防。这项研究涉及多个学科，包括机器人技术，控制理论，机器学习，计算力学和材料科学。跨学科的方法将有助于促进少数民族参与科学研究和促进工程教育。本项目的目标是研究一个有效和准确的动力学建模和控制框架，用于非结构化环境下的软机器人操作和运动。具体来说，研究团队将开发具有分布式电源、驱动和触觉传感的模块化即插即用软机器人手臂。一个三维的，降阶的，几何精确的，软机器人手臂的内部驱动力肌腱收缩的有限元模型将制定和实施。一个有效的和准确的仿真程序与环境相互作用的软机器人的动力学。动态控制器，混合基于模型和数据驱动的控制策略，也将设计和测试。通过一系列的模拟和物理实验，该团队将确定基本的设计原则，并开发控制器，以实现自主软机器人，如象鼻般灵巧的软机械手和章鱼般行走/游泳的软机器人。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值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

Small Fault Detection of Discrete-Time Nonlinear Uncertain Systems

• DOI: 10.1109/tcyb.2019.2945629
• 发表时间: 2019-10
• 期刊: IEEE Transactions on Cybernetics
• 影响因子: 11.8
• 作者: Jingting Zhang;C. Yuan;P. Stegagno;Haibo He;Cong Wang

Learning-Based Tracking Control of Soft Robots

• DOI: 10.1109/lra.2023.3303724
• 发表时间: 2023-10
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Jingting Zhang;Xiaotian Chen;P. Stegagno;Mingxi Zhou;C. Yuan

Similar Fault Isolation of Discrete-Time Nonlinear Uncertain Systems: An Adaptive Threshold Based Approach

• DOI: 10.1109/access.2020.2991138
• 发表时间: 2020
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Jingting Zhang;Qingbin Gao;C. Yuan;Weizhen Zeng;Shi‐Lu Dai;Cong Wang

Adaptive NN-Based Reference-Tracking Control of Uncertain Nonlinear Parabolic PDE Systems

不确定非线性抛物型偏微分方程系统的自适应神经网络参考跟踪控制

• DOI: 10.1109/cdc45484.2021.9683381
• 发表时间: 2021
• 期刊: IEEE Conference on Decision and Control
• 作者: Zhang, Jingting;Gu, Yan;Stegagno, Paolo;Zeng, Wei;Yuan, Chengzhi
• 通讯作者: Yuan, Chengzhi

Intelligent adaptive learning and control for discrete-time nonlinear uncertain systems in multiple environments

• DOI: 10.1016/j.neucom.2021.07.046
• 发表时间: 2021-08-07
• 期刊: NEUROCOMPUTING
• 影响因子: 6
• 作者: Zhang, Jingting;Yuan, Chengzhi;Dai, Shi-Lu
• 通讯作者: Dai, Shi-Lu