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An Introduction to Trajectory Optimization: How to Do Your Own Direct Collocation
Author(s) -
Matthew Kelly
Publication year - 2017
Publication title -
siam review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.683
H-Index - 120
eISSN - 1095-7200
pISSN - 0036-1445
DOI - 10.1137/16m1062569
Subject(s) - collocation (remote sensing) , computer science , trajectory optimization , trajectory , collocation method , debugging , simple (philosophy) , variety (cybernetics) , mathematical optimization , matlab , focus (optics) , optimization problem , sequence (biology) , algorithm , mathematics , artificial intelligence , optimal control , programming language , machine learning , differential equation , ordinary differential equation , mathematical analysis , philosophy , physics , optics , epistemology , astronomy , biology , genetics
This paper is an introductory tutorial for numerical trajectory optimization with a focus on direct collocation methods. These methods are relatively simple to understand and effectively solve a wide variety of trajectory optimization problems. Throughout the paper we illustrate each new set of concepts by working through a sequence of four example problems. We start by using trapezoidal collocation to solve a simple one-dimensional toy problem and work up to using Hermite--Simpson collocation to compute the optimal gait for a bipedal walking robot. Along the way, we cover basic debugging strategies and guidelines for posing well-behaved optimization problems. The paper concludes with a short overview of other methods for trajectory optimization. We also provide an electronic supplement that contains well-documented MATLAB code for all examples and methods presented. Our primary goal is to provide the reader with the resources necessary to understand and successfully implement their own direct collocation...

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