NRI: Collaborative Research: Human-Centered Modeling and Control of Cooperative Manipulation with Bimanual Robots

NRI：协作研究：以人为中心的双手机器人协作操纵建模与控制

• 批准号: 1427260
• 负责人: Ruzena Bajcsy
• 金额: $ 67.27万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1427260&HistoricalAwards=false
• 关键词: NRI; Collaborative; Research; Human; Centered

This proposal addresses modeling and control aspects of human-robot interaction by considering constraints imposed by an individual's physiology. The project is motivated by increasing demand for automation in unstructured environments that require high-level cognitive processing and complex decision-making which cannot yet be fully automated. By taking human-centric approach, data-driven musculoskeletal models are incorporated into the robot interaction model to account for differences of individuals. Each cooperative activity is divided into action primitives requiring different control strategies while estimating human intent from various sensors. The framework is based on theory of hybrid systems that provides provable safety and stability criteria. The outcome of this research will facilitate methodology for safer and more reliable human-robot interaction and advance state-of-the-art in human movement analysis and control theory. The broader impacts of this research will be realized through new insights into understanding of human intent and haptic cooperation applicable to general human-machine interaction. With increasing interest in service robotics safe and reliable interaction will be the key to successful introduction of robots in human-occupied environments. The potential economic impact of robots engaged in services and manufacturing alongside humans are significant due to increased productivity and reduced costs. Another emerging area is rehabilitation and assistive robotics. The developed data-driven musculoskeletal models will also be applicable to quantification of physical impairments and estimation of muscular stress in healthcare and ergonomics. This interdisciplinary research provides excellent opportunities for undergraduate and graduate students to be engaged in analytical challenges, laboratory demonstrations of theoretical results, and experimental evaluations.

该建议通过考虑个人生理学所施加的约束来解决人-机器人交互的建模和控制方面。该项目的动机是在非结构化环境中对自动化的需求日益增加，这些环境需要高级认知处理和复杂的决策，而这些决策还不能完全自动化。通过以人为中心的方法，数据驱动的肌肉骨骼模型被纳入机器人交互模型，以考虑个体的差异。每个合作活动被划分为动作原语，需要不同的控制策略，同时估计人类的意图，从各种传感器。该框架是基于混合系统理论，提供可证明的安全性和稳定性标准。这项研究的成果将促进更安全，更可靠的人机交互方法，并推进人类运动分析和控制理论的最新发展。这项研究的更广泛的影响将通过对人类意图的理解和适用于一般人机交互的触觉合作的新见解来实现。随着人们对服务机器人的兴趣越来越大，安全可靠的交互将是在人类居住的环境中成功引入机器人的关键。与人类一起从事服务和制造业的机器人的潜在经济影响是巨大的，因为它提高了生产力并降低了成本。另一个新兴领域是康复和辅助机器人。开发的数据驱动的肌肉骨骼模型也将适用于医疗保健和人体工程学中的身体损伤量化和肌肉应力估计。这种跨学科的研究为本科生和研究生提供了很好的机会，可以参与分析挑战，理论结果的实验室演示和实验评估。