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Rapidly-Exploring Random Tree/Evidence
Method evidence record

Rapidly-Exploring Random Tree

The Rapidly-Exploring Random Tree (RRT) is a motion planning algorithm that builds a tree of feasible paths by iteratively sampling random configurations in the workspace and connecting them to the nearest existing node in the tree. Introduced by LaValle in 1998, RRT is a breakthrough for high-dimensional motion planning, enabling robots to find collision-free paths in complex environments with obstacles, joint limits, and kinematic constraints.

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Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Rapidly-Exploring Random Tree
Taxonomic method record · ml-model / control-theory
  • LaValle, S. M. (1998). Rapidly-exploring random trees: A new tool for path planning. Technical Report TR 98-11, Iowa State University. · URL
  • Karaman, S., & Frazzoli, E. (2011). Sampling-based algorithms for optimal motion planning. International Journal of Robotics Research, 30(7), 846-894. · DOI 10.1177/0278364911406761
  • LaValle, S. M. (2006). Planning Algorithms. Cambridge University Press. · URL
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Related methods

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Same method familyFeedback Linearizationmachine-suggested · Relational suggestion, not evidence.Same method familyModel Predictive Controlmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketProbabilistic Roadmapmachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

3 recorded citations, copied from the method source record.

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