Optimal sampling-based Feedback Motion Trees among obstacles for controllable linear systems with linear constraints
Jeong hwan Jeon, Sertac Karaman, Emilio Frazzoli
Abstract
The RRT* algorithm has efficiently extended Rapidly-exploring Random Trees (RRTs) to endow it with asymptotic optimality. We propose Goal-Rooted Feedback Motion Trees (GR-FMTs) that honor state/input constraints and generate collision-free feedback policies. Given analytic solutions for optimal local steering, GR-FMTs obtain and realize safe, dynamically feasible, and asymptotically optimal trajectories toward goals. Second, for controllable linear systems with linear state/input constraints, we propose a fast method for local steering, based on polynomial basis functions and segmentation. GR-FMTs with the method obtain and realize trajectories that are collision-free, dynamically feasible under constraints, and asymptotically optimal within a set we define. The formulation includes linear or quadratic programming of small sizes, where constraints are identified by root-finding in low or medium order of polynomials and added progressively.
BibTeX
@inproceedings{icra2015_optimalsamplingb,
title = {Optimal sampling-based Feedback Motion Trees among obstacles for controllable linear systems with linear constraints},
author = {Jeong hwan Jeon and Sertac Karaman and Emilio Frazzoli},
booktitle = {ICRA 2015},
year = {2015}
}