Simulation-based LQR-trees with input and state constraints

@article{Reist2010SimulationbasedLW,
  title={Simulation-based LQR-trees with input and state constraints},
  author={Philipp Reist and Russ Tedrake},
  journal={2010 IEEE International Conference on Robotics and Automation},
  year={2010},
  pages={5504-5510}
}
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… CONTINUE READING
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