Convex Optimization for Trajectory Generation Arxiv:2106.09125V1 [Math.OC] 16 Jun 2021
Preprint Convex Optimization for Trajectory Generation A Tutorial On Generating Dynamically Feasible Trajectories Reliably And Efficiently Danylo Malyuta a,? , Taylor P. Reynolds a, Michael Szmuk a, Thomas Lew b, Riccardo Bonalli b, Marco Pavone b, and Behc¸et Ac¸ıkmes¸e a a William E. Boeing Department of Aeronautics and Astronautics, University of Washington, Seattle, WA 98195, USA b Department of Aeronautics and Astronautics, Stanford University, Stanford, CA 94305, USA Reliable and efficient trajectory generation methods are a fundamental need for autonomous dynamical systems of tomorrow. The goal of this article is to provide a comprehensive tutorial of three major con- vex optimization-based trajectory generation methods: lossless convexification (LCvx), and two sequential convex programming algorithms known as SCvx and GuSTO. In this article, trajectory generation is the computation of a dynamically feasible state and control signal that satisfies a set of constraints while opti- mizing key mission objectives. The trajectory generation problem is almost always nonconvex, which typ- ically means that it is not readily amenable to efficient and reliable solution onboard an autonomous ve- hicle. The three algorithms that we discuss use problem reformulation and a systematic algorithmic strat- egy to nonetheless solve nonconvex trajectory generation tasks through the use of a convex optimizer. The theoretical guarantees and computational speed offered by convex optimization have made the algo- rithms popular in both research and industry circles. To date, the list of applications include rocket land- ing, spacecraft hypersonic reentry, spacecraft rendezvous and docking, aerial motion planning for fixed- wing and quadrotor vehicles, robot motion planning, and more. Among these applications are high-profile rocket flights conducted by organizations like NASA, Masten Space Systems, SpaceX, and Blue Origin.
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