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

EARLY概念探索性研究赠款（EAGER）的研究目标是调查，更好地理解和验证一种新颖独特的子空间最优控制方法，该方法可能为推进知识状态奠定基础。基于仿生运动伪装现象和高阶离散化方法，提出了一种新的求解约束非线性最优轨迹问题的方法，该方法可以大大降低求解约束非线性最优轨迹问题的计算量。一个系统的工具，判断最优的解决方案后，它是获得调查，然后由一个理论上证明的指导方针，就如何选择虚拟猎物运动事先，使？好吗？子空间可以先验地构造。在这个EAGER项目中，将使用两个示例问题来验证方法：协同电子作战飞机（ECAV）问题和移动的机器人碰撞轨迹规划问题。作为一种潜在的变革性技术，拟议的研究不仅将为真实的-时间轨迹规划，而且还有助于可以以标准约束非线性最优控制形式建模的广泛的其它应用。更重要的是，本文的研究成果将促进自然界中存在的生物运动策略在实际工程或科学问题中的应用。 该研究有望对理论和应用产生影响，并弥合两者之间的差距。 PI和研究生将参加由NSF CMMI组织的论坛，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。生物现象和研究子任务和研究结果将用于丰富本科动力学课程和研究生最优控制课程。新教材将与国家科学数字图书馆相连接。

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.