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.
