LQR-RRT*: Optimal sampling-based motion planning with automatically derived extension heuristics
The RRT* algorithm has recently been proposed as an optimal extension to the standard RRT algorithm . 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 torque-limited, underactuated, and in belief space. © 2012 IEEE.
Perez, A; Platt, R; Konidaris, G; Kaelbling, L; Lozano-Perez, T
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