Probabilistic Feasibility for Nonlinear Systems with Non-Gaussian Uncertainty using RRT
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How_Probabilistic feasibility.pdf
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Author(s)
Luders, Brandon Douglas
How, Jonathan P.
Date Issued
March 2011
Journal
Infotech@Aerospace 2011
Publisher
American Institute of Aeronautics and Astronautics
Citation
Luders, Brandon, and Jonathan How. “Probabilistic Feasibility for Nonlinear Systems with Non-Gaussian Uncertainty using RRT.” In Infotech@Aerospace 2011, 29 - 31 March 2011, St. Louis, Missouri, American Institute of Aeronautics and Astronautics, 2011.
Version
Author's final manuscript
Abstract
For motion planning problems involving many or unbounded forms of uncertainty, it may
not be possible to identify a path guaranteed to be feasible, requiring consideration of the
trade-o between planner conservatism and the risk of infeasibility. Recent work developed
the chance constrained rapidly-exploring random tree (CC-RRT) algorithm, a real-time
planning algorithm which can e ciently compute risk at each timestep in order to guarantee
probabilistic feasibility. However, the results in that paper require the dual assumptions of
a linear system and Gaussian uncertainty, two assumptions which are often not applicable
to many real-life path planning scenarios. This paper presents several extensions to the
CC-RRT framework which allow these assumptions to be relaxed. For nonlinear systems
subject to Gaussian process noise, state distributions can be approximated as Gaussian by
considering a linearization of the dynamics at each timestep; simulation results demonstrate
the e ective of this approach for both open-loop and closed-loop dynamics. For systems
subject to non-Gaussian uncertainty, we propose a particle-based representation of the
uncertainty, and thus the state distributions; as the number of particles increases, the
particles approach the true uncertainty. A key aspect of this approach relative to previous
work is the consideration of probabilistic bounds on constraint satisfaction, both at every
timestep and over the duration of entire paths.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.2514/6.2011-1589
