The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
LQR-RRT*: Optimal sampling-based motion planning with automatically derived extension heuristics
Open accessAbstract
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.
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Citations
Cites
Probabilistic roadmaps for path planning in high-dimensional configuration spaces
Kavraki, L. E., Svestka, P., Latombe, J.-C., & Overmars, M. H. (1996) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.508439 · Cited by 6,343 · cited 1×
“In an effort to solve problems of practical interest, much recent research has been devoted to developing samplingbased planners such as the Rapidly-exploring Randomized Tree (RRT) [4] and the Probabilistic Road Map (PRM) [5].”
Background
Metadata
- Authors
- Perez, A., Platt, R., Konidaris, G., Kaelbling, L., & Lozano-Perez, T.
- Venue
- 2012 IEEE International Conference on Robotics and Automation
- Publisher
- IEEE
- Published
- 2012-05
- Pages
- 2537-2542
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2166077797
- Cited by
- 225
- References
- 1
- FWCI
- 10.71
- Type
- conference paper
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: LQR-RRT*: Optimal sampling-based motion planning with automatically derived extension heuristics. https://app.cochat.ai/papers/10.1109/icra.2012.6225177
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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. 2012 IEEE International Conference on Robotics and Automation. https://doi.org/10.1109/icra.2012.6225177
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