Probabilistic Feasibility for Nonlinear Systems with Non-Gaussian Uncertainty using RRT
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 rapi...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
American Institute of Aeronautics and Astronautics
2013
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| Online Access: | http://hdl.handle.net/1721.1/81417 https://orcid.org/0000-0001-8576-1930 |
| Summary: | 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. |
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