Feedback Motion Planning Approach for Nonlinear Control using Gain Scheduled RRTs

Feedback Motion Planning Approach for Nonlinear Control using Gain Scheduled RRTs. Maeda, G. J., Singh, S. P. N., & Durrant-Whyte, H. 2010.
doi abstract bibtex

A new control strategy based on feedback motion planning is presented for solving nonlinear control problems in constrained environments. The algorithm explores the state-space using a bi-directional rapidly exploring random tree (biRRT) in order to find a feasible trajectory between an initial and goal state. By incrementally scheduling LQR controllers, it attempts to connect states so as to link the two trees. These attempts are evaluated by verifying that the connected state is inside the controllable area of an infinite time horizon controller at the goal. This allows for a rapid delineation of equivalent neighborhoods in the state-space. As a result, random exploration is terminated as soon as a feasible solution is made possible by feedback means, avoiding oversampling and partially introducing optimal actions at the neighborhood of the connection. The algorithm is demonstrated and compared against a biRRT using single-link pendulum and cart-pole swing-up tasks amongst obstacles, the latter showing a nearly order of magnitude more efficient search.

@CONFERENCE{Maeda2010,
  author = {Guilherme J. Maeda and Surya P. N. Singh and Hugh Durrant-Whyte},
  title = {Feedback Motion Planning Approach for Nonlinear Control using Gain
	Scheduled RRTs},
  booktitle = {Proceedings of the International Conference on Intelligent Robots
	and Systems ({IROS})},
  year = {2010},
  pages = {119--126},
  abstract = {A new control strategy based on feedback motion planning is presented
	for solving nonlinear control problems in constrained environments.
	The algorithm explores the state-space using a bi-directional rapidly
	exploring random tree (biRRT) in order to find a feasible trajectory
	between an initial and goal state. By incrementally scheduling LQR
	controllers, it attempts to connect states so as to link the two
	trees. These attempts are evaluated by verifying that the connected
	state is inside the controllable area of an infinite time horizon
	controller at the goal. This allows for a rapid delineation of equivalent
	neighborhoods in the state-space. As a result, random exploration
	is terminated as soon as a feasible solution is made possible by
	feedback means, avoiding oversampling and partially introducing optimal
	actions at the neighborhood of the connection. The algorithm is demonstrated
	and compared against a biRRT using single-link pendulum and cart-pole
	swing-up tasks amongst obstacles, the latter showing a nearly order
	of magnitude more efficient search.},
  doi = {10.1109/IROS.2010.5650634},
  pdf = {iros2010.feedbackmp.pdf}
}

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