| dc.contributor |
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
|
| dc.contributor |
Luders, Brandon Douglas |
|
| dc.contributor |
How, Jonathan P. |
|
| dc.creator |
Luders, Brandon Douglas |
|
| dc.creator |
How, Jonathan P. |
|
| dc.date |
2013-10-17T19:32:05Z |
|
| dc.date |
2013-10-17T19:32:05Z |
|
| dc.date |
2011-03 |
|
| dc.date.accessioned |
2023-03-01T18:09:59Z |
|
| dc.date.available |
2023-03-01T18:09:59Z |
|
| dc.identifier |
978-1-60086-944-0 |
|
| dc.identifier |
AIAA 2011-1589 |
|
| dc.identifier |
http://hdl.handle.net/1721.1/81417 |
|
| dc.identifier |
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. |
|
| dc.identifier |
https://orcid.org/0000-0001-8576-1930 |
|
| dc.identifier.uri |
http://localhost:8080/xmlui/handle/CUHPOERS/279000 |
|
| dc.description |
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. |
|
| dc.description |
United States. Air Force (USAF, grant FA9550-08-1-0086) |
|
| dc.description |
United States. Air Force Office of Scientific Research (AFOSR, Grant FA9550-08-1-0086) |
|
| dc.format |
application/pdf |
|
| dc.language |
en_US |
|
| dc.publisher |
American Institute of Aeronautics and Astronautics |
|
| dc.relation |
http://dx.doi.org/10.2514/6.2011-1589 |
|
| dc.relation |
Infotech@Aerospace 2011 |
|
| dc.rights |
Creative Commons Attribution-Noncommercial-Share Alike 3.0 |
|
| dc.rights |
http://creativecommons.org/licenses/by-nc-sa/3.0/ |
|
| dc.source |
MIT web domain |
|
| dc.title |
Probabilistic Feasibility for Nonlinear Systems with Non-Gaussian Uncertainty using RRT |
|
| dc.type |
Article |
|
| dc.type |
http://purl.org/eprint/type/ConferencePaper |
|