Robust Sampling-based Motion Planning with Asymptotic Optimality Guarantees
Name
Karaman_Robust Sampling-based.pdf
Size
8.6 MB
Format
Adobe PDF
Checksum (MD5)
d437d7e3a2690dad284b098f872ba895
Author(s)
Karaman, Sertac
How, Jonathan P.
Luders, Brandon Douglas
Date Issued
August 2013
Journal
Proceedings of the AIAA Guidance, Navigation, and Control (GNC) Conference
Publisher
American Institute of Aeronautics and Astronautics
Citation
Luders, Brandon D., Sertac Karaman, and Jonathan P. How. “Robust Sampling-based Motion Planning with Asymptotic Optimality Guarantees.” In AIAA Guidance, Navigation, and Control (GNC) Conference. American Institute of Aeronautics and Astronautics, 2013.
Version
Author's final manuscript
Abstract
This paper presents a novel sampling-based planner, CC-RRT*, which generates robust, asymptotically optimal trajectories in real-time for linear Gaussian systems subject to process noise, localization error, and uncertain environmental constraints. CC-RRT* provides guaranteed probabilistic feasibility, both at each time step and along the entire trajectory, by using chance constraints to efficiently approximate the risk of constraint violation. This algorithm expands on existing results by utilizing the framework of RRT* to provide guarantees on asymptotic optimality of the lowest-cost probabilistically feasible path found. A novel risk-based objective function, shown to be admissible within RRT*, allows the user to trade-off between minimizing path duration and risk-averse behavior. This enables the modeling of soft risk constraints simultaneously with hard probabilistic feasibility bounds. Simulation results demonstrate that CC-RRT* can e fficiently identify smooth, robust trajectories for a variety of uncertainty scenarios and dynamics.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.2514/6.2013-5097
