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.

该项目将提高织物增强充气软体机器人的控制能力。软机器人提供了巨大的前景，因为它们可以适应未知环境中的特征，然后改变形状来执行任务，例如缠绕物体或挤入小间隙。这些可用于搜索和救援、救灾以及增加制造的定制化。虽然科学家和工程师已经通过反复试验成功地展示了这些类型的运动，但让它们在计算机控制下工作却很困难。由于软机器人通常由弹性材料制成，因此可以在机器人中模制多个传感器，测量环境中物体的触摸、接近度和形状。有了如此大量的传感器，机器人就可以获得大量数据，这些数据可用于帮助确定机器人当前的位置和形状。软机器人的弹性材料还提供了结合活性材料的机会，这些活性材料可以根据计算机的命令改变其特性。 这些材料可用于调整软机器人的刚度或提供改变其形状的附加选项。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关于自身和世界的有意义的信息。这些技术将被用来将研究的好处广泛传播给更广泛的社区。由选定的 K-12 教师组成的团队将参加十项全能式的竞赛，使用单个机器人完成一系列不同的任务，并利用软机器人技术培养他们的技能，以便以后在课堂上使用。研究工作的重点是开发简洁的机器人织物增强橡胶管的运动模型可以通过计算机快速评估。将特别关注现有技术难以表现的行为，例如起皱、打褶和屈曲。基于简化模型，算法将在每个时刻为机器人假设各种可能的形状。然后，该算法将通过将多个传感器的测量值与模型的预测进行比较来决定哪个是正确的。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，即气动阀信号和橡胶壁中的可调刚度补丁的组合，以自主完成有用的任务，其中机器人必须推动环境。该算法将在悬挂干墙和检索杂乱中的物体等代表性任务中进行验证。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点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

States and Contact Forces Estimation for a Fabric-Reinforced Inflatable Soft Robot *

织物增强充气软体机器人的状态和接触力估计 *

• DOI: 10.1109/icra48506.2021.9561296
• 发表时间: 2021-01
• 期刊: Proceedings of the 2021 International Conference on Robotics and Automation
• 作者: Bui, Phuc D.H.;Schultz, Joshua A.
• 通讯作者: Schultz, Joshua A.

Dynamically Feasible Trajectory Generation for Soft Robots

软体机器人的动态可行轨迹生成

• DOI: 10.1109/robosoft55895.2023.10122016
• 发表时间: 2023-04
• 期刊: IEEE
• 作者: Sanders, Haley P.;Killpack, Marc D.
• 通讯作者: Killpack, Marc D.

Soft Robot Configuration Estimation and Control Using Simultaneous Localization and Mapping

使用同步定位和绘图的软体机器人配置估计和控制

• DOI: 10.1109/iros51168.2021.9635896
• 发表时间: 2021-01
• 期刊: Proceedings of the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS
• 作者: Sorensen, Christian;Hyatt, Phillip;Ricks, Matthew;Nielsen, Seth;Killpack, Marc D.
• 通讯作者: Killpack, Marc D.

Pose Measurement and Contact Training of a Fabric-Reinforced Inflatable Soft Robot

织物增强充气软体机器人的姿态测量和接触训练

• DOI: 10.1109/sii55687.2023.10039331
• 发表时间: 2023-01-17
• 期刊: 2023 IEEE/SICE International Symposium on System Integration (SII)
• 作者: P. D. Bui;Joshua A. Schultz
• 通讯作者: Joshua A. Schultz

Endurance tests for a fabric reinforced inflatable soft actuator

织物增强充气软执行器的耐久性测试

• DOI: 10.3389/fmats.2023.1112540
• 发表时间: 2023-05
• 期刊: Frontiers in Materials
• 影响因子: 3.2
• 作者: Bui, Phuc D.;Prugh, Benjamin;Padilla, Alejandra M.;Schell, Caroline;Keller, Michael;Schultz, Joshua A.
• 通讯作者: Schultz, Joshua A.