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Multipartite RRTs for Rapid Replanning in Dynamic Environments
Abstract
The rapidly-exploring random tree (RRT) algorithm has found widespread use in the field of robot motion planning because it provides a single-shot, probabilistically complete planning method which generalizes well to a variety of problem domains. We present the multipartite RRT (MP-RRT), an RRT variant which supports planning in unknown or dynamic environments. By purposefully biasing the sampling distribution and re-using branches from previous planning iterations, MP-RRT combines the strengths of existing adaptations of RRT for dynamic motion planning. Experimental results show MP-RRT to be very effective for planning in dynamic environments with unknown moving obstacles, replanning in high-dimensional configuration spaces, and replanning for systems with space time constraints.
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Cited by
Perception, Planning, Control, and Coordination for Autonomous Vehicles
Pendleton, S., Andersen, H., Du, X., Shen, X., Meghjani, M., Eng, Y., Rus, D., & Ang, M. (2017) · Machines · doi:10.3390/machines5010006 · Cited by 657
Metadata
- Authors
- Zucker, M., Kuffner, J., & Branicky, M.
- Venue
- Proceedings 2007 IEEE International Conference on Robotics and Automation
- Publisher
- IEEE
- Published
- 2007-04
- Pages
- 1603-1609
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2099593662
- Cited by
- 263
- References
- 0
- FWCI
- 13.41
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Multipartite RRTs for Rapid Replanning in Dynamic Environments. https://app.cochat.ai/papers/10.1109/robot.2007.363553
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- Zucker, M., Kuffner, J., & Branicky, M. (2007). Multipartite RRTs for Rapid Replanning in Dynamic Environments. Proceedings 2007 IEEE International Conference on Robotics and Automation. https://doi.org/10.1109/robot.2007.363553
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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