CAREER: Adaptive Actuation and Control in Embodied Biohybrid Robots

职业：生物混合机器人的自适应驱动和控制

• 批准号: 2044785
• 负责人: Victoria Webster-Wood
• 金额: $ 60万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2044785&HistoricalAwards=false
• 关键词: CAREER; Adaptive; Actuation; Control; Embodied

Animals are often a source of inspiration in robotic design. By designing from animal blueprints, engineers can create robotic systems capable of walking, running, crawling, swimming, and even flying. However, even with the advances in robotics over the past decades, robotic systems still fall short of many of the capabilities seen in biological animals. One key difference between existing robots and their animal counterparts is that biological systems are made up of soft, adaptable materials, including muscles for actuation and neurons for control. This CAREER award investigates how to fabricate robust, adaptable actuators for biohybrid robots using living muscle, how these actuators adapt to exercise, and how to control biohybrid robots with living neurons. Additionally, this CAREER award supports educational and outreach initiatives to improve recruitment and retention of diverse students and faculty in robotics and STEM. Accessible age-appropriate educational materials based on the research outcomes will be developed, made available to middle and high school teachers, and integrated into a graduate course on Bioinspired Robotics. The research team will host virtual and in-person outreach events to introduce students to bioinspired and biohybrid robotics. Underrepresented undergraduate students will be recruited for summer research experiences in biohybrid robotics and modeling. Finally, the investigator will promote tools for recruitment and retention of women faculty in robotics. This 5-year CAREER project will result in bioactuators capable of interfacing with a range of robotic structures via tendon-like interfaces, bioinspired neural networks for bioactuator control, and the ability to perform basic ‘programming’ of biohybrid robots. Biohybrid robotics directly harnesses living tissues as renewable engineering materials. In particular, muscle-based bioactuators are self-healing, compliant, and adapt to loading. Whereas most biohybrid research to date has focused on biological materials as individual components of the system, approaches for the integrated design, fabrication, and ‘programming’ of robust bioactuators and biological control networks are needed to improve biohybrid robot performance and broaden applicability. To meet this need, this CAREER project will (1) enable adaptive bioactuation of a wide range of robotic peripheries through the creation of embedded biocompatible interfaces, (2) model and fabricate simple biological neural networks to control bioactuators, and (3) train integrated bioactuators and biological neural networks. Not only will the proposed research approach lead to advances in bioactuation and control, but it will also specifically focus on integrated biohybrid robot development. ‘Programmable’ biohybrid robots have applications in medicine where small-scale biocompatible systems could be used as self-actuating stents or medical implants, or as functional components of neuromuscular tissues-on-a-chip for drug-screening and neuroscience. The proposed research lays the foundation for addressing future challenges in biohybrid robotics, including integrating diverse sensing modalities into biohybrid robot systems, understanding the effect of embodiment on neuromuscular control circuits, and studying emergent dynamics in distributed biohybrid actuation systems. The research approach in this CAREER proposal will be integrated with an educational and outreach plan to (1) incorporate neuromuscular modeling in biohybrid robotics curriculum, (2) improve retention of diverse students in robotics through biohybrid robot experiences, and (3) build tools to improve visibility of women faculty in robotics towards improving retention.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.

动物通常是机器人设计的灵感来源。通过设计动物蓝图，工程师可以创建能够行走，跑步，爬行，游泳甚至飞行的机器人系统。然而，即使机器人技术在过去几十年中取得了进步，机器人系统仍然没有达到生物动物的许多能力。 现有机器人与动物机器人之间的一个关键区别是，生物系统是由柔软的、适应性强的材料组成的，包括用于驱动的肌肉和用于控制的神经元。这个CAREER奖研究如何使用活肌肉为生物混合机器人制造鲁棒，适应性强的执行器，这些执行器如何适应运动，以及如何用活神经元控制生物混合机器人。此外，这个职业奖支持教育和推广活动，以改善招聘和保留不同的学生和教师在机器人和干。将根据研究成果开发适合年龄的教育材料，提供给初中和高中教师，并将其纳入生物启发机器人的研究生课程。研究团队将举办虚拟和面对面的外展活动，向学生介绍生物启发和生物混合机器人。代表性不足的本科生将被招募在生物混合机器人和建模的夏季研究经验。最后，调查员将促进招聘和保留机器人女教师的工具。这个为期5年的CAREER项目将导致生物执行器能够通过肌腱状接口与一系列机器人结构连接，生物激励神经网络用于生物执行器控制，以及能够执行生物混合机器人的基本“编程”。生物混合机器人直接利用活组织作为可再生工程材料。特别地，基于肌肉的生物致动器是自我修复的、顺应的并且适应于负载。虽然大多数生物混合研究迄今已集中在生物材料作为系统的各个组成部分，方法的集成设计，制造和“编程”的强大的生物执行器和生物控制网络需要提高生物混合机器人的性能和扩大适用性。为了满足这一需求，该CAREER项目将（1）通过创建嵌入式生物相容性接口，实现广泛的机器人外围设备的自适应生物驱动，（2）建模和制造简单的生物神经网络来控制生物执行器，以及（3）训练集成的生物执行器和生物神经网络。所提出的研究方法不仅将导致生物驱动和控制的进步，而且还将特别关注集成生物混合机器人的开发。“可编程”生物混合机器人在医学上有应用，其中小规模的生物相容性系统可以用作自驱动支架或医疗植入物，或者作为药物筛选和神经科学的神经肌肉组织芯片的功能组件。拟议的研究奠定了基础，以应对未来的挑战，在生物混合机器人，包括集成不同的传感模式到生物混合机器人系统，了解神经肌肉控制电路的实施效果，并研究分布式生物混合驱动系统的紧急动态。本职业建议中的研究方法将与教育和推广计划相结合，以（1）将神经肌肉建模纳入生物混合机器人课程，（2）通过生物混合机器人体验提高不同学生在机器人领域的保留率，以及（3）建立工具，以提高女性教师在机器人技术方面的知名度，以提高保留率。该奖项反映了NSF的法定使命，并被认为是值得的通过使用基金会的知识价值和更广泛的影响审查标准进行评估来提供支持。

项目成果

GANGLIA: A Tool for Designing Customized Neuron Circuit Patterns

ANGLIA：设计定制神经元电路模式的工具

• 发表时间: 2023
• 期刊: Conference on Biomimetic and Biohybrid Systems: Living Machines 2023
• 作者: Liao, A.S.;Zhang, Y.J.;Webster-Wood, V.A.
• 通讯作者: Webster-Wood, V.A.

The Tall, the Squat, & the Bendy: Parametric Modeling and Simulation Towards Multi-functional Biohybrid Robots

高个子、矮个子、

• 发表时间: 2023
• 期刊: Conference on Biomimetic and Biohybrid Systems: Living Machines 2023
• 作者: Schaffer, S.;Webster-Wood, V.A.
• 通讯作者: Webster-Wood, V.A.

Semi-Automated Quantitative Evaluation of Neuron Developmental Morphology In Vitro Using the Change-Point Test

• DOI: 10.1007/s12021-022-09600-8
• 发表时间: 2022-09-07
• 期刊: NEUROINFORMATICS
• 影响因子: 3
• 作者: Liao，Ashlee S.;Cui，Wenxin;Webster-Wood，Victoria A.
• 通讯作者: Webster-Wood，Victoria A.

An integrated computer vision system for real-time monitoring and control of long-fiber embedded hydrogel 3D printing

用于实时监测和控制长纤维嵌入水凝胶3D打印的集成计算机视觉系统

• DOI: 10.1016/j.matpr.2022.09.272
• 发表时间: 2022
• 期刊: Materials Today: Proceedings
• 作者: Sun, Wenhuan;Webster-Wood, Victoria
• 通讯作者: Webster-Wood, Victoria

Biohybrid robots: recent progress, challenges, and perspectives

• DOI: 10.1088/1748-3190/ac9c3b
• 发表时间: 2023-01-01
• 期刊: BIOINSPIRATION & BIOMIMETICS
• 影响因子: 3.4
• 作者: Webster-Wood，Victoria A.;Guix，Maria;Parker，Kevin Kit
• 通讯作者: Parker，Kevin Kit