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

NRI：协作研究：抓取采集和其他接触任务中实时估计和过滤的动态贝叶斯方法（续）

• 批准号: 1537257
• 负责人: Siwei Lyu
• 金额: $ 22.1万
• 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: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1537257&HistoricalAwards=false
• 关键词: NRI; Collaborative; Research; Dynamic; Bayesian

A current weakness of robots is their inability to quickly and reliably perform contact tasks in unstructured environments. The goal of this project, which represents a collaboration between faculty at two partner institutions, is to alleviate this shortcoming by developing techniques that will afford robots accurate real-time perception in tasks exhibiting intermittent contact. Project outcomes will have a strong impact in manipulation tasks, as robots become more capable and autonomous. The PIs also expect successful applications in other areas, for instance to drive real-time haptic displays in augmented reality systems, to extract human manipulation strategies from observed kinesthetic demonstrations, and to identify model parameters to improve simulation accuracy, not to mention in advancing the level of autonomy for space and undersea exploration. Additional applications outside of robotics are anticipated in situations where a system experiences abrupt state transitions and the goal is either state estimation or real-time feedback control (e.g., chemical, financial, and geological systems). The PIs' labs have a track record of supporting women and under-represented minorities, and the research will be integrated into a variety of pedagogical activities at the graduate and undergraduate level on both campuses.In previous work the team proposed the DBC-SLAM framework, in which continuous states (i.e., poses, velocities and contact impulses), and discrete contact states (i.e., contact-noncontact and stick-slip) of the manipulated objects, are tracked and important model parameters are estimated. In this research, they will extend that work significantly in two directions. First, they will design new parallel, anytime complementarity problem (CP) solvers in order to attain real-time performance. Second, they will enhance the dynamic Bayesian models in DBC-SLAM to allow the use of point-cloud observations and more complex geometric models of the objects, robot links, and environment. The intellectual merit of the project lies in three main activities: first, the creative, yet rigorous, technical process of designing perception algorithms based on fundamental first principles of nonsmooth mechanics and Bayesian estimation in a way that can utilize point-cloud data; second, achieving real-time performance by exploiting the mathematical structure and properties of both the nonsmooth multibody dynamics and CPU/GPU computing systems; and third, pursuing the first two activities in a way that sheds light on the trade-offs between estimation accuracy and speed.

机器人当前的弱点是他们无法在非结构化环境中快速而可靠地执行联系任务。 该项目的目的代表了两个合作伙伴机构的教职员工之间的合作，是通过开发将提供机器人准确的实时意识的技术来减轻这种缺点，以表现出间歇性联系。 随着机器人变得越来越有能力和自主，项目结果将对操纵任务产生强大的影响。 PI还期望在其他领域的成功应用，例如，在增强现实系统中推动实时触觉显示，从观察到的动力学演示中提取人类操纵策略，并识别模型参数以提高模拟准确性，而不是在提高空间自主权水平方面提高空间自主权水平。 在系统经历突然的状态过渡和目标的情况下，预计机器人技术以外的其他应用程序是国家估计或实时反馈控制（例如，化学，金融和地质系统）。 PIS的实验室具有支持妇女和代表性不足的少数群体的记录，该研究将在两个校园的毕业生和本科级别的各种教学活动中融入到各种教学活动中。跟踪操纵对象并估算重要的模型参数。 在这项研究中，他们将在两个方向上大大扩展这项工作。 首先，他们将设计新的平行，任何时间互补问题（CP）求解器，以实现实时性能。 其次，它们将增强DBC-SLAM中动态的贝叶斯模型，以允许使用点云观测以及对象，机器人链路和环境的更复杂的几何模型。 该项目的智力优点在于三个主要活动：首先是基于非平滑力学和贝叶斯估算的基本第一原理设计感知算法的创造性但严格的技术过程，该算法可以利用Point-Cloud Data；其次，通过利用非滑动多体动力学和CPU/GPU计算系统的数学结构和属性来实现实时性能；第三，以一种阐明估计准确性和速度之间的权衡方面的方式追求前两项活动。