EFRI C3 SoRo: Between a Soft Robot and a Hard Place: Estimation and Control Algorithms that Exploit Soft Robots' Unique Abilities

EFRI C3 SoRo：在软机器人和硬机器人之间：利用软机器人独特能力的估计和控制算法

• 批准号: 1935312
• 负责人: Joshua Schultz
• 金额: $ 200万
• 依托单位: University of Tulsa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1935312&HistoricalAwards=false
• 关键词: EFRI; C3; SoRo; Between; Soft

This project will improve the ability to control fabric-reinforced inflatable soft robots. Soft robots offer tremendous promise because they can conform to features in an unknown environment and then change shape to perform tasks, such as entwining objects or squeezing into small gaps. These could be useful in search and rescue, disaster relief, and increasing customization in manufacturing. While scientists and engineers have managed to demonstrate these types of movements by trial and error, getting them to work under computer control has been difficult. Because soft robots are usually made of elastomeric materials, a multitude of sensors can be molded into the robot, measuring touch, proximity, and the shape of objects in the environment. With such a large number of sensors, the robot has access to a rich amount of data that can be used to help determine the current position and shape of the robot. The elastomeric material of soft robots also offers the opportunity to incorporate active materials that can change their properties upon command from a computer. These materials can be used to tune the stiffness of the soft robot or provide additional options for changing its shape. If successful, this research project will produce improved algorithms to process these sensor inputs so that the soft robot can distill them into meaningful information about itself and the world. The techniques will be leveraged to broadly spread the benefits of the research to the wider community. Teams of selected K-12 teachers will participate in a decathlon-style contest, using a single robot to compete in a series of diverse tasks and building their skills with soft robot technology for later use in their classrooms.The research effort centers around developing concise models for the motions of a fabric-reinforced rubber tube that can be evaluated quickly by a computer. Particular attention will be given to behaviors that are difficult to represent by existing techniques, such as wrinkling, pleating and buckling. Based on the simplified model, the algorithm will postulate a variety of possible shapes for the robot at each instant. The algorithm will then decide which is correct by comparing measurements from its many sensors to the predictions of the model. Using the data gathered by the robot platforms, the robot will be able to learn how to select the appropriate commands -- that is, the combinations of pneumatic valve signals and tunable stiffness patches in the rubber walls -- to autonomously complete useful tasks where the robot must push on the environment. The algorithm will be validated in representative tasks such as hanging drywall and retrieving objects in clutter.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.

该项目将提高控制织物增强充气软机器人的能力。软机器人提供了巨大的希望，因为它们可以符合未知环境中的功能，然后改变形状以执行任务，例如将物体纠缠或挤压成小间隙。这些可能在搜索和救援，救灾以及制造业中的定制中增加。尽管科学家和工程师已经通过反复试验证明了这些类型的运动，但在计算机控制下使他们工作却很困难。由于软机器人通常是由弹性材料制成的，因此可以将多种传感器模制成机器人，测量触摸，接近性和环境中物体的形状。使用如此大量的传感器，机器人可以访问大量数据，可用于帮助确定机器人的当前位置和形状。软机器人的弹性材料还提供了合并活性材料的机会，这些材料可以通过计算机在命令时改变其属性。 这些材料可用于调整软机器人的刚度或为改变其形状的其他选择。如果成功，该研究项目将产生改进的算法来处理这些传感器输入，以便软机器人可以将它们提炼成有关自身和世界的有意义的信息。这些技术将被利用，以将研究的好处广泛地传播给更广泛的社区。选定的K-12教师团队将参加十项全能风格的比赛，使用一个机器人参加一系列不同的任务，并使用软机器人技术来培养他们的技能，以便在课堂上使用。可以通过计算机快速评估织物增强橡胶管运动的模型。特别注意通过现有技术（例如皱纹，褶皱和屈曲）来代表的行为。基于简化的模型，该算法将在每次瞬间假设机器人的各种可能形状。然后，该算法将通过将其许多传感器与模型预测的测量值进行比较来确定哪种是正确的。使用机器人平台收集的数据，机器人将能够学习如何选择适当的命令 - 即气动阀信号的组合和橡胶墙中可调节的刚度贴片的组合，以自动完成有用的任务。机器人必须推动环境。该算法将在诸如悬挂式干墙和检索杂物中检索物体之类的代表性任务中得到验证。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的评估来支持的。

项目成果

Soft Robot Shape Estimation: A Load-Agnostic Geometric Method

软机器人形状估计：一种与负载无关的几何方法

• DOI: 10.1109/iros55552.2023.10342225
• 发表时间: 2023
• 期刊: IEEE
• 作者: Sorensen, Christian;Killpack, Marc D.
• 通讯作者: Killpack, Marc D.

Tractable and Intuitive Dynamic Model for Soft Robots via the Recursive Newton-Euler Algorithm

通过递归牛顿欧拉算法建立易于处理且直观的软体机器人动态模型

• DOI: 10.1109/robosoft54090.2022.9762215
• 发表时间: 2022
• 期刊: IEEE International Conference on Soft Robotics (RoboSoft
• 作者: Jensen, Spencer W.;Johnson, Curtis C.;Lindberg, Alexa M.;Killpack, Marc D.
• 通讯作者: Killpack, Marc D.

Extending the reach of single-chamber inflatable soft robots using Magnetorheological Fluids

• DOI: 10.1109/robosoft51838.2021.9479295
• 发表时间: 2021-01-01
• 期刊: 2021 IEEE 4TH INTERNATIONAL CONFERENCE ON SOFT ROBOTICS (ROBOSOFT)
• 作者: Williamson, J. Garrett;Schell, Caroline;Schultz, Joshua
• 通讯作者: Schultz, Joshua

Modeling the dynamics of soft robots by discs and threads

通过圆盘和螺纹对软体机器人的动力学进行建模

• DOI: 10.1109/icra46639.2022.9812286
• 发表时间: 2022
• 期刊: Proceeedings of the 2022 International Conference on Robotics and Automation (ICRA
• 作者: Schultz, Joshua A.;Sanders, Haley;Bui, Phuc Duc;Layer, Brett;Killpack, Marc
• 通讯作者: Killpack, Marc

Stretchable Optical Waveguide Sensor Suitability for Wrinkle Degree Detection in Soft Robots

• DOI: 10.1109/robosoft55895.2023.10122087
• 发表时间: 2023-04
• 期刊: 2023 IEEE International Conference on Soft Robotics (RoboSoft)
• 作者: J. G. Williamson;Joshua A. Schultz