Feedback-motion-planning with simulation-based LQR-trees
Name
Reist15.pdf
Description
Submitted version
Size
1 MB
Format
Adobe PDF
Checksum (MD5)
3a67a1fc8bd2505120a2f01dd610d392
Author(s)
Reist, Philipp
Preiswerk, Pascal
Tedrake, Russell L
Date Issued
July 11, 2016
Journal
International Journal of Robotics Research
Publisher
SAGE Publications
Citation
Reist, Philipp et al. "Feedback-motion-planning with simulation-based LQR-trees." International Journal of Robotics Research 35, 11 (July 2016): 1393-1416. 2016 The Author(s).
Version
Original manuscript
Abstract
The paper presents the simulation-based variant of the LQR-tree feedback-motion-planning approach. The algorithm generates a control policy that stabilizes a nonlinear dynamic system from a bounded set of initial conditions to a goal. This policy is represented by a tree of feedback-stabilized trajectories. The algorithm explores the bounded set with random state samples and, where needed, adds new trajectories to the tree using motion planning. Simultaneously, the algorithm approximates the funnel of a trajectory, which is the set of states that can be stabilized to the goal by the trajectory's feedback policy. Generating a control policy that stabilizes the bounded set to the goal is equivalent to adding trajectories to the tree until their funnels cover the set. In previous work, funnels are approximated with sums-of-squares verification. Here, funnels are approximated by sampling and falsification by simulation, which allows the application to a broader range of systems and a straightforward enforcement of input and state constraints. A theoretical analysis shows that, in the long run, the algorithm tends to improve the coverage of the bounded set as well as the funnel approximations. Focusing on the practical application of the method, a detailed example implementation is given that is used to generate policies for two example systems. Simulation results support the theoretical findings, while experiments demonstrate the algorithm's state-constraints capability, and applicability to highly-dynamic systems. Keywords: Feedback motion-planning; random sampling; feedback policy; nonlinear dynamic system; trajectory library
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1177/0278364916647192
