NRI: Collaborative Research: Efficient Algorithms for Contact-Aware State Estimation

NRI：协作研究：接触感知状态估计的有效算法

• 批准号: 1426703
• 负责人: Michael Kaess
• 金额: $ 40万
• 依托单位: Carnegie-Mellon University
• 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: 2014-08-15 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1426703&HistoricalAwards=false
• 关键词: NRI; Collaborative; Research; Efficient; Algorithms

This project addresses the difficult theoretical, computational, and applied challenges required to exploit a deep mathematical relationship between recent advances in machine perception/estimation algorithms and recent advances in algorithms for planning/controlling systems undergoing frictional contact. It explores the immediate applications of these algorithms to perception, robotic object manipulation and parts assembly, and humanoid robots performing complex, multi-contact, whole-body maneuvers. In order to showcase the generality of approach, and simultaneously reach out to the important under-represented minority population, the research team employs a new hands-on short-course curriculum in which students apply the proposed algorithms to predict the outcome of games using visual tracking. The course is being developed in partnership with the MIT Office of Minority Education (OME).The project brings together expertise in simultaneous localization and mapping (SLAM), robot manipulation, robotic automation, legged robots, and optimization and nonlinear control, leading to a cross-fertilization of ideas and techniques. The research team exploits sparsity in the complementarity formulations of contact in Lagrangian dynamics. The project explores a new algebraic approach to nonlinear estimator design. The project produces new theorems, new algorithms, and experimental results on real robots. The project also represents a new partnership with our industrial collaborator, ABB Robotics. The developed algorithms facilitate a broad range of new applications in which perception and control systems monitor and manipulate physical interactions with the world. From palm-sized smart devices to environmental monitoring, sensors are becoming ubiquitous; to reach their full potential these sensor networks must be able to reason about contact - the basic building block of physical interaction.

该项目解决了开发机器感知/估计算法的最新进展与计划/控制系统进行摩擦接触的算法的最新进展之间的深刻数学关系所需的困难理论，计算和应用挑战。 它探讨了这些算法在感知，机器人对象操纵和零件组件上的直接应用，以及人形机器人进行复杂，多接触，全身动作。为了展示方法的普遍性，并同时接触了重要代表性不足的少数民族人群，研究小组采用了一种新的动手短途课程课程，学生将使用拟议的算法应用视觉跟踪来预测游戏的结果。该课程正在与麻省理工学院少数民族教育办公室（OME）合作开发。该项目汇集了同时本地化和映射（SLAM），机器人操纵，机器人自动化，腿部机器人，优化和非线性控制方面的专业知识，从而导致了思想和技术的交叉利用。 研究团队利用拉格朗日动态的接触互补性表述中利用了稀疏性。 该项目探索了一种新的非线性估计器设计代数方法。该项目产生了新的定理，新算法和对实际机器人的实验结果。 该项目还代表了与我们的工业合作者ABB Robotics的新合作伙伴关系。开发的算法促进了广泛的新应用，在这些应用程序中，感知和控制系统监控和操纵与世界的身体互动。 从棕榈大小的智能设备到环境监控，传感器变得无处不在。为了充分发挥其潜力，这些传感器网络必须能够推论联系 - 物理互动的基本构建基础。