NRI-Small: Collaborative Research: A Dynamic Bayesian Approach to Real-Time Estimation and Filtering in Grasp Acquisition and Other Contact Tasks

NRI-Small：协作研究：在抓取采集和其他接触任务中进行实时估计和过滤的动态贝叶斯方法

• 批准号: 1208463
• 负责人: Siwei Lyu
• 金额: $ 20.03万
• 依托单位: SUNY at Albany
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1208463&HistoricalAwards=false
• 关键词: NRI; Small; Collaborative; Research; Dynamic

Robots cannot currently grasp objects or perform other contact tasks in unstructured environments with speed or reliability. This project is developing techniques for accurate real-time perception in support of contact tasks. In the proposed method, sensor data tracks the continuous motions of manipulated objects, while models of the objects are simultaneously updated. Particle filtering, a kind of Monte-Carlo simulation, ensures consistency of this tracking and updating.The strongest impact of this work will be in robotic grasping and manipulation. Because of the synthesis of modeling and probabilistic inference, further impacts can be expected, for example in real-time haptics for telepresence.

机器人当前无法在具有速度或可靠性的非结构化环境中掌握对象或执行其他联系任务。 该项目正在开发用于支持联系任务的准确实时感知的技术。 在提出的方法中，传感器数据跟踪操纵对象的连续运动，而对象的模型则同时更新。 粒子过滤是一种蒙特卡洛模拟，确保了这种跟踪和更新的一致性。这项工作的最大影响将是机器人抓握和操纵。 由于建模和概率推断的综合，可以预期进一步的影响，例如在实时触觉中进行远程敏感。