Unscented Trajectory Optimization
I. M. Ross, R. J. Proulx, M. Karpenko

TL;DR
This paper reintroduces unscented trajectory optimization as a special case of tychastic optimal control, providing a systematic method for generating optimal trajectories under uncertainty without complex stochastic calculus.
Contribution
It presents a novel instantiation of tychastic optimal control using the unscented transform, simplifying stochastic trajectory optimization and enabling effective management of uncertainties.
Findings
Effective risk reduction demonstrated in numerical examples
Rapid formulation and computation of optimal trajectories
Management of trajectory dispersions with nonlinear transformations
Abstract
In a nutshell, unscented trajectory optimization is the generation of optimal trajectories through the use of an unscented transform. Although unscented trajectory optimization was introduced by the authors about a decade ago, it is reintroduced in this paper as a special instantiation of tychastic optimal control theory. Tychastic optimal control theory (from \textit{Tyche}, the Greek goddess of chance) avoids the use of a Brownian motion and the resulting It\^{o} calculus even though it uses random variables across the entire spectrum of a problem formulation. This approach circumvents the enormous technical and numerical challenges associated with stochastic trajectory optimization. Furthermore, it is shown how a tychastic optimal control problem that involves nonlinear transformations of the expectation operator can be quickly instantiated using an unscented transform. These…
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Taxonomy
TopicsRobotic Path Planning Algorithms · Transportation and Mobility Innovations
