SHB: Small: Computing Robot Motions for Home Healthcare Assistance

SHB：小型：计算家庭医疗保健援助的机器人动作

• 批准号: 1117127
• 负责人: Ron Alterovitz
• 金额: $ 35万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117127&HistoricalAwards=false
• 关键词: SHB; Small; Computing; Robot; Motions

Millions of Americans are unable to independently perform activities of daily living (ADLs) such as dressing and grooming, and this number is rising rapidly as America?s aging and disabled population increases. This research focuses on designing and developing new algorithms and software systems that would help enable personal robots to autonomously compute safe motions to assist disabled and elderly individuals with ADLs. The proposed framework uses kinesthetic demonstrations to teach the robot desirable motion trajectories to accomplish several specific ADL assistance tasks. Based on these demonstrations, the research focuses on developing new computational methods to extract task constraints for desirable motion trajectories using learning methods based on Gaussian mixture models in conjunction with machine learning and 3D registration methods. A key element of this project involves investigation of methods to deformably register and generalize the motion trajectories and task constraints across individuals of different shapes and sizes. In order to generate safe plans in dynamic real-world settings, the proposed research investigates new highly parallel algorithms that effectively utilize the power of modern general purpose graphics processing units (GPUs) for real-time planning in uncertain environments. The framework is evaluated using articulated mannequin testbeds.This project brings together an interdisciplinary team with computer science, robotics, and occupational therapy expertise. The project integrates research with education through community outreach activities. In the long term, the methods developed in the proposed research could have broad societal benefits by helping enable personal robots to assist disabled and elderly individuals with ADLs, allowing them to safely stay in their homes rather than moving to costly institutions.

数以百万计的美国人无法独立进行日常生活活动（ADL），如穿衣和打扮，这一数字正在迅速上升，因为美国？老龄化和残疾人口增加。这项研究的重点是设计和开发新的算法和软件系统，这将有助于使个人机器人自主计算安全的运动，以帮助残疾人和老年人与ADL。建议的框架使用动觉演示教机器人理想的运动轨迹，以完成几个特定的ADL援助任务。基于这些演示，研究重点是开发新的计算方法，使用基于高斯混合模型的学习方法，结合机器学习和3D配准方法，为理想的运动轨迹提取任务约束。该项目的一个关键要素涉及调查方法，以变形登记和概括的运动轨迹和任务限制在不同形状和大小的个人。为了在动态现实世界的设置中生成安全的计划，所提出的研究调查新的高度并行算法，有效地利用现代通用图形处理单元（GPU）的能力，在不确定的环境中进行实时规划。该框架使用铰接式人体模型测试台进行评估。该项目汇集了一个具有计算机科学，机器人技术和职业治疗专业知识的跨学科团队。该项目通过社区外联活动将研究与教育结合起来。从长远来看，拟议研究中开发的方法可以通过帮助个人机器人帮助患有ADL的残疾人和老年人，使他们能够安全地留在家中，而不是搬到昂贵的机构，从而产生广泛的社会效益。