Abstract: In this talk I will discuss a novel approach to autonomous spacecraft maneuvering that leverages recent algorithmic advances in the field of robotic motion planning to spacecraft control. Specifically, I will first present and discuss a novel sampling-based algorithm for motion planning, called the Fast Marching Tree algorithm (FMT*).
The FMT* algorithm is asymptotically optimal and appears to converge to an optimal path faster (and sometimes significantly faster) than its stateof- the-art counterparts. The FMT* algorithm essentially performs a "lazy" dynamic programming recursion on a set of probabilistically-drawn samples to grow a tree of paths, which moves steadily outward in cost-to-come space. The optimality result is proven with a novel analysis technique that provides convergence rate bounds—a first in this field.
In the second part of the talk I will discuss the application of FMT* to spacecraft control. In particular, I will consider tractable inclusion of (spacecraft) differential constraints, planning under uncertainty via Monte Carlo sampling, inclusion of safety specifications, and preliminary experimental results on a spacecraft test bed.
Time: 4:00 – 5:00pm
Location: Varian Physics Lab, Room #355 (Map)
(Light refreshments available 4:00pm; Presentation begis 4:15pm)
Open to All