Dynamic Shared Control for Soft Robots

软体机器人的动态共享控制

• 批准号: 2349067
• 负责人: Laura Blumenschein
• 金额: $ 54.96万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2349067&HistoricalAwards=false
• 关键词: Dynamic; Shared; Control; Soft; Robots

This award supports research on human-robot collaboration while controlling soft robots made of flexible or stretchable materials. Soft robots are well-suited for real-world tasks and can interact with humans more safely and robustly than traditional robots. However, predicting humans' actions and reactions to operate can be challenging for the robot, while direct control of the robot by humans may, in turn, require an intuitive understanding of the robot's movements, increasing the workload for the user. The research team will explore the potential of shared control, which can help soft robots benefit from human insights into environmental interactions, while also reducing the burden on the human user. The findings of this research will be instrumental in advancing the integration of soft robots into a range of human environments, from personal to industrial, and could lead to new emergent behaviors that improve our autonomous control of these robots. The project will also reach out to the local community and high school students to increase engagement and awareness of STEM disciplines and human-robot interaction. This research will advance fundamental understanding of human teleoperation of soft robotic systems and the emergent behaviors that arise when control is shared in task-responsive ways. First, real-time features of user workload in the operation of systems with complex passive and active movements will be investigated, giving a better understanding of how tool (robot) features affect the operator's workload. This will provide a first-ever measure of the relationship between the human impression of soft robots and the workload when using a novel robot. Second, mechanical models of soft robot limits to predict buckling and material failure events, providing safer operations for autonomous and teleoperated systems will be developed. Third, the project will investigate how communication of limits affects human operator behavior and soft robot performance. Lastly, sliding shared control algorithms for soft robots will be developed, leading to increased performance of the systems and better adoption as tools for human operators.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.

该奖项支持有关人类机器人协作的研究，同时控制着由灵活或可拉伸材料制成的软机器人。软机器人非常适合现实世界任务，并且比传统机器人更安全，更坚固。但是，预测人类对操作的行为和反应对机器人来说可能具有挑战性，而人类对机器人的直接控制可能又需要直观地了解机器人的运动，从而增加用户的工作量。研究团队将探索共享控制的潜力，这可以帮助软机器人受益于人类对环境互动的见解，同时还减轻了人类用户的负担。这项研究的发现将有助于将软机器人整合到从个人到工业的一系列人类环境中，并可能导致新的新兴行为，从而改善我们对这些机器人的自主控制。该项目还将与当地社区和高中生联系，以提高对STEM学科和人类机器人互动的参与度和认识。这项研究将提高对软机器人系统的人类近距离的基本理解，以及以任务响应的方式共享控制时会产生的新兴行为。首先，将研究用户工作负载在具有复杂被动和主动运动的系统操作中的实时功能，从而更好地了解工具（机器人）功能如何影响操作员的工作负载。这将提供有史以来对软机器人的印象与使用新型机器人的工作量之间关系的第一衡量。其次，将开发自主和遥控系统的安全操作，以预测软机器人限制的机械模型。第三，该项目将调查限制的沟通如何影响人类操作员的行为和软机器人性能。最后，将开发针对软机器人的滑动共享控制算法，从而提高系统的性能，并更好地采用作为人类运营商的工具。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智力优点和更广泛的影响来通过评估来支持的。