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

Publication ,  Journal Article
Perez, A; Platt, R; Konidaris, G; Kaelbling, L; Lozano-Perez, T
Published in: Proceedings - IEEE International Conference on Robotics and Automation
January 1, 2012

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. © 2012 IEEE.

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Published In

Proceedings - IEEE International Conference on Robotics and Automation

DOI

ISSN

1050-4729

Publication Date

January 1, 2012

Start / End Page

2537 / 2542
 

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Perez, A., Platt, R., Konidaris, G., Kaelbling, L., & Lozano-Perez, T. (2012). LQR-RRT*: Optimal sampling-based motion planning with automatically derived extension heuristics. Proceedings - IEEE International Conference on Robotics and Automation, 2537–2542. https://doi.org/10.1109/ICRA.2012.6225177
Perez, A., R. Platt, G. Konidaris, L. Kaelbling, and T. Lozano-Perez. “LQR-RRT*: Optimal sampling-based motion planning with automatically derived extension heuristics.” Proceedings - IEEE International Conference on Robotics and Automation, January 1, 2012, 2537–42. https://doi.org/10.1109/ICRA.2012.6225177.
Perez A, Platt R, Konidaris G, Kaelbling L, Lozano-Perez T. LQR-RRT*: Optimal sampling-based motion planning with automatically derived extension heuristics. Proceedings - IEEE International Conference on Robotics and Automation. 2012 Jan 1;2537–42.
Perez, A., et al. “LQR-RRT*: Optimal sampling-based motion planning with automatically derived extension heuristics.” Proceedings - IEEE International Conference on Robotics and Automation, Jan. 2012, pp. 2537–42. Scopus, doi:10.1109/ICRA.2012.6225177.
Perez A, Platt R, Konidaris G, Kaelbling L, Lozano-Perez T. LQR-RRT*: Optimal sampling-based motion planning with automatically derived extension heuristics. Proceedings - IEEE International Conference on Robotics and Automation. 2012 Jan 1;2537–2542.

Published In

Proceedings - IEEE International Conference on Robotics and Automation

DOI

ISSN

1050-4729

Publication Date

January 1, 2012

Start / End Page

2537 / 2542