Work

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

Year: 2012

Type: conference-paper

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 torque-limited, underactuated, and in belief space.

Source: Proceedings - IEEE International Conference on Robotics and Automation/Proceedings

DOI: 10.1109/icra.2012.6225177

Authors: Alejandro Perez, Robert Platt, George Konidaris, Leslie Kaelbling, Tomas Lozano-Perez

Institutions: MIT Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology

Language: English

Topic: Robotic Path Planning Algorithms

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green