Simulation-based LQR-trees with input and state constraints
Name
Tedrake_Simulation-based LQR.pdf
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709.71 KB
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Adobe PDF
Checksum (MD5)
38f010bb4f56badef44e9f14bbf79dbf
Author(s)
Tedrake, Russell Louis
Date Issued
July 2010
Journal
Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2010
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Reist, Philipp, and Russ Tedrake. “Simulation-based LQR-trees with Input and State Constraints.” IEEE International Conference on Robotics and Automation (ICRA), 2010. 5504–5510.
Version
Final published version
Abstract
We present an algorithm that probabilistically covers a bounded region of the state space of a nonlinear system with a sparse tree of feedback stabilized trajectories leading to a goal state. The generated tree serves as a lookup table control policy to get any reachable initial condition within that region to the goal. The approach combines motion planning with reasoning about the set of states around a trajectory for which the feedback policy of the trajectory is able to stabilize the system. The key idea is to use a random sample from the bounded region for both motion planning and approximation of the stabilizable sets by falsification; this keeps the number of samples and simulations needed to generate covering policies reasonably low. We simulate the nonlinear system to falsify the stabilizable sets, which allows enforcing input and state constraints. Compared to the algebraic verification using sums of squares optimization in our previous work, the simulation-based approximation of the stabilizable set is less exact, but considerably easier to implement and can be applied to a broader range of nonlinear systems. We show simulation results obtained with model systems and study the performance and robustness of the generated policies.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ROBOT.2010.5509893
