EAGER: Virtual Motion Camouflage based Subspace Optimal Control for Real-Time Trajectory Planning

EAGER：基于虚拟运动伪装的实时轨迹规划子空间最优控制

• 批准号: 0939093
• 负责人: Yunjun Xu
• 金额: $ 5.58万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-15 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0939093&HistoricalAwards=false
• 关键词: EAGER; Virtual; Motion; Camouflage; based

The research objective of this EArly-concept Grants for Exploratory Research (EAGER) award is to investigate, better understand, and validate a novel and unique subspace optimal control methodology that will potentially build a foundation for advancing the state of knowledge in the critical area of real-time trajectory planning for single or cooperative systems. A new method, based on the bio-inspired motion camouflage phenomenon and higher order discretization schemes, will be investigated such that the computational cost in solving constrained nonlinear optimal trajectory problems can be dramatically reduced. A systematic tool of judging the optimality of the solution after it is obtained will be investigated and followed by a theoretically proven guideline on how to select the virtual prey motion beforehand such that a ?good? subspace can be constructed a priori. In this EAGER project, two example problems will be used to validate the methodologies: a Cooperative Electronic Combat Air Vehicles (ECAVs) problem and a mobile robot collision trajectory planning problem.As a potentially transformative technique, the proposed research will not only provide an innovative approach for real-time trajectory planning but also contribute to a wide range of other applications that can be modeled in a standard constrained nonlinear optimal control form. Even more important, the results of this research will promote the implementation of biological motion strategies existing in nature to practical engineering or scientific problems. This research is expected to impact both theories and applications and bridge the gap between them. The PI and graduate students will attend forums organized by the NSF CMMI to disseminate our innovations and findings throughout the project period. Research findings will be evaluated by project reports, journal publications, and peer feedback. The biological phenomenon and research subtasks and findings will be used to enrich the undergraduate dynamics course and the graduate optimal control course. New teaching materials will be linked to the National Science Digital Library.

这项早期概念授予探索性研究的研究目标（渴望）是要调查，更好地理解和验证一种新颖而独特的子空间最佳控制方法，该方法将为单个或合作系统的实时轨迹计划的关键领域中的知识状态奠定基础。将研究一种基于生物启发的运动伪装现象和高阶离散化方案的新方法，以便可以大大减少解决约束非线性非线性最佳轨迹问题的计算成本。在解决方案获得后判断最佳性的系统工具将进行研究，然后是关于如何事先选择虚拟猎物运动的理论上证明的指南，以使其良好？可以先验构建子空间。在这个急切的项目中，将使用两个示例问题来验证方法：合作电子战斗机汽车（ECAV）问题和一个移动机器人碰撞轨迹计划问题。作为一种潜在的变革性技术，拟议的研究不仅会为实时轨迹计划提供创新的方法，还可以为其他应用程序提供广泛的范围，可以建立标准的标准构造。更重要的是，这项研究的结果将促进自然界中存在的生物运动策略的实施，以解决实践工程或科学问题。 预计这项研究将影响理论和应用，并弥合它们之间的差距。 PI和研究生将参加由NSF CMMI组织的论坛，以在整个项目期间传播我们的创新和发现。 研究结果将通过项目报告，期刊出版物和同行反馈进行评估。生物学现象和研究子任务和发现将用于丰富本科动力学课程和研究生最佳控制课程。新的教材将与国家科学数字图书馆链接。