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.

机器人目前无法在非结构化环境中快速或可靠地抓取物体或执行其他接触任务。该项目正在开发准确的实时感知技术，以支持接触任务。在该方法中，传感器数据跟踪被操纵对象的连续运动，同时对象的模型被更新。粒子滤波是一种蒙特卡罗模拟，它确保了跟踪和更新的一致性，这项工作最大的影响将是机器人的抓取和操纵。由于模型和概率推理的综合，可以预期进一步的影响，例如在远程呈现的实时触觉方面。