2017
DOI: 10.1137/16m1062569
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An Introduction to Trajectory Optimization: How to Do Your Own Direct Collocation

Abstract: Abstract. 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 comp… Show more

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Cited by 432 publications
(309 citation statements)
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“…We observe that U a1 transmits only within a certain proximity of the static destination node, and more power is allocated for transmission when it is closer to the destination. If instead we allow U a1 to have a free trajectory then the jointly optimal transmission and mobility profiles of U a1 2 Transcription involves conversion of the original continuous time optimal control problem into a nonlinear program [36]. Various transcription methods exist, with the appropriate one often depending on characteristics of the problem.…”
Section: Numerical Resultsmentioning
confidence: 99%
“…We observe that U a1 transmits only within a certain proximity of the static destination node, and more power is allocated for transmission when it is closer to the destination. If instead we allow U a1 to have a free trajectory then the jointly optimal transmission and mobility profiles of U a1 2 Transcription involves conversion of the original continuous time optimal control problem into a nonlinear program [36]. Various transcription methods exist, with the appropriate one often depending on characteristics of the problem.…”
Section: Numerical Resultsmentioning
confidence: 99%
“…There is a fundamental trade-off in polynomial splines: for a given amount of data you can store many low-order segments or few high-order segments. Selecting the correct trade-off is discussed in detail in [21], [13], [22].…”
Section: Discussionmentioning
confidence: 99%
“…Most musculoskeletal simulation problems are naturally posed as optimal control problems: we seek a system's parameters and time-varying controls that minimize a cost (e.g., energy consumption) subject to the dynamics of the system, expressed as differential-algebraic equations. An increasingly popular method for solving optimal control problems is to approximate the system's states and controls as polynomial splines and solve for the knot points that lead the spline to obey the system's dynamics [17][18][19][20]. The dynamics are enforced by requiring the time derivative of the state splines to match the derivative from the system's differential equations at specified time points.…”
Section: Introductionmentioning
confidence: 99%