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

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前景，因为它们可以在未知环境中符合特征，然后改变形状来执行任务，例如缠绕物体或挤入小间隙。这些可能在搜救、救灾和增加制造业的定制化方面有用。虽然科学家和工程师已经通过反复试验成功地演示了这些类型的运动，但让它们在计算机控制下工作一直是困难的。因为软机器人通常是由弹性材料制成的，所以可以在机器人中模压大量的传感器，测量环境中物体的触摸、接近和形状。有了如此大量的传感器，机器人可以获得丰富的数据，这些数据可以用来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。这些材料可以用来调整软机器人的硬度，或者为改变其形状提供额外的选择。如果成功，这个研究项目将产生改进的算法来处理这些传感器输入，这样软机器人就可以将它们提取成关于自己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到更广泛的社区。由精选的K-12教师组成的团队将参加一场十项全能风格的比赛，使用一个机器人来竞争一系列不同的任务，并利用软机器人技术培养他们的技能，以便稍后在课堂上使用。这项研究的重点是开发一种可以通过计算机快速评估的纤维增强橡胶管运动的简明模型。将特别注意现有技术难以表现的行为，如褶皱、褶皱和屈曲。在简化模型的基础上，算法将假设机器人在每个时刻都有各种可能的形状。然后，该算法将通过将其多个传感器的测量结果与模型的预测进行比较来决定哪一个是正确的。利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--即气动阀门信号和橡胶墙中可调刚度补丁的组合--以自主完成机器人必须推动环境的有用任务。该算法将在悬挂石膏板和取回杂物等典型任务中得到验证。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.