ICRA 2012 Paper Abstract


Paper WeB07.2

Perez, Alejandro (MIT), Platt, Robert (MIT), Konidaris, George Dimitri (MIT), Kaelbling, Leslie (MIT), Lozano-Perez, Tomas (MIT)

LQR-RRT*: Optimal Sampling-Based Motion Planning with Automatically Derived Extension Heuristics

Scheduled for presentation during the Regular Session "Sampling-Based Motion Planning" (WeB07), Wednesday, May 16, 2012, 10:45−11:00, Meeting Room 7 (Remnicha)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on October 24, 2017

Keywords Motion and Path Planning


The RRT* algorithm has recently been proposed as an optimal extension to the standard RRT algorithm [1]. However, like RRT, RRT* is difficult to apply in problems with complicated or underactuated dynamics because it requires the design of a two domain-specific extension heuristics: a distance metric and node extension method. We propose automatically deriving these two heuristics for RRT* by locally linearizing the domain dynamics and applying linear quadratic regulation (LQR). The resulting algorithm, LQR-RRT*, finds optimal plans in domains with complex or underactuated dynamics without requiring domain-specific design choices. We demonstrate its application in domains that are successively torquelimited, underactuated, and in belief space.



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