Abstract:
Many modern control methods, such as model-predictive control, rely heavily on solving optimization problems in real time. In particular, the ability to efficiently solve optimal control problems has enabled many of the recent breakthroughs in achieving highly dynamic behaviors for complex robotic systems. The high computational requirements of these algorithms demand novel algorithms tailor-suited to meeting the tight requirements on runtime performance, memory usage, reliability, and flexibility. This thesis introduces a state-of-the-art algorithm for trajectory optimization that leverages the problem structure while being applicable across a wide variety of problem requirements, including those involving conic constraints and non-Euclidean state vectors such as 3D rotations.

Building off of this existing work, we propose two novel research directions. First, we propose a method for low-cost dynamics emulation of arbitrary aerospace systems using multicopters, leveraging much of the work on online trajectory optimization detailed in this thesis. Second, we propose to further push the computational limits of nonlinear trajectory optimization with algorithms that are more naturally suited to parallelization.

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Thesis Committee Members:
Zac Manchester, Chair
Michael Kaess
Lorentz Biegler
Scott Kuindersma, Boston Dynamics