SGER: Robust Multiobjective Active Vision Systems

SGER：稳健的多目标主动视觉系统

• 批准号: 9911161
• 负责人: Octavia Camps
• 金额: $ 7.5万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-09-15 至 2001-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9911161&HistoricalAwards=false
• 关键词: SGER; Robust; Multiobjective; Active; Vision

AbstractIIS-9911161Camps, OctaviaPennsylvania State University75,000 - 12 mos.SGER: Robust Multi-objective Active Vision SystemsThis is a SGER standard award. Recent hardware advances have rendered active vision a viable option for a very diverse spectrum of applications ranging from MEMS manufacture to vision assisted surgery. However, in order for active vision to gain widespread application, the problem of fragility (i.e. lack of robustness) of systems designed using classical approachesmust be addressed. This project aims to explore the feasibility of solving this difficulty through a multidisciplinary approach, which brings to bear on the problem new multi-objective robust control and robust vision tools that are just starting to emerge in the respective communities. Specifically, The PIs will use robust feature modeling and identification techniques to recast the problem into a multi-objective control form that can be exactly solved using convex optimization techniques. If successful, this effort will result in an expanded robust active vision framework, capable of addressing multiple performance specifications and uncertainty. The results obtained from this research are expected to show that a tight integration of the control and vision aspects of the problem in a unified framework leads to substantial improvements both in performance and flexibility.

摘要IIS-9911161坎普斯，奥克塔维亚宾夕法尼亚州立大学75,000 - 12月SGER：鲁棒的多目标主动视觉系统这是一个SGER标准奖。 最近的硬件进步使主动视觉成为从MEMS制造到视觉辅助手术的各种应用的可行选择。然而，为了使主动视觉获得广泛的应用，必须解决使用经典方法设计的系统的脆弱性（即缺乏鲁棒性）的问题。该项目旨在探索通过多学科方法解决这一困难的可行性，这带来了新的多目标鲁棒控制和鲁棒视觉工具，这些工具刚刚开始在各自的社区出现。具体来说，PI将使用强大的特征建模和识别技术将问题重新转换为多目标控制形式，可以使用凸优化技术精确解决。如果成功，这一努力将导致一个扩展的强大的主动视觉框架，能够解决多个性能规格和不确定性。从这项研究中获得的结果预计将表明，在一个统一的框架中的问题的控制和视觉方面的紧密集成导致性能和灵活性的大幅改善。