LQR-RRT*: Optimal sampling-based motion planning with automatically derived extension heuristics

Abstract

Abstract — 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. I.