UC Irvine

This research develops automated, on-board trajectory planning algorithms in order to support current and new mission concepts. These include orbiter missions to Phobos or Deimos, Outer Planet Moon orbiters, and robotic and crewed missions to small bodies. The challenges stem from the limited on-board computing resources which restrict full trajectory optimization with guaranteed convergence in complex dynamical environments. The approach taken consists of leveraging pre-mission computations to create a large database of pre-computed orbits and arcs. Such a database is used to generate a discrete representation of the dynamics in the form of a directed graph, which acts to index these arcs. This allows the use of graph search algorithms on-board in order to provide good approximate solutions to the path planning problem. Coupled with robust differential correction and optimization techniques, this enables the determination of an efficient path between any boundary conditions with very little time and computing effort. Furthermore, the optimization methods developed here based on sequential convex programming are shown to have provable convergence properties, as well as generating feasible major iterates in case of a system interrupt - a key requirement for on-board application. The outcome of this project is thus the development of an algorithmic framework which allows the deployment of this approach in a variety of specific mission contexts. Test cases related to missions of interest to NASA and JPL such as a Phobos orbiter and a Near Earth Asteroid interceptor are demonstrated, including the results of an implementation on the RAD750 flight processor. This method fills a gap in the toolbox being developed to create fully autonomous space exploration systems.