Navigation & SLAM
Rapidly-Exploring Random Tree (RRT)
A rapidly-exploring random tree (RRT) is a sampling-based motion planner, introduced by LaValle in 1998, that incrementally grows a tree from the start configuration by sampling random configurations and extending the nearest tree node toward each sample. Its Voronoi-biased growth explores high-dimensional spaces quickly; RRT-Connect grows bidirectional trees for speed, and RRT* (Karaman and Frazzoli, 2011) adds rewiring for asymptotic optimality.
Why it matters for physical AI
RRT variants remain the default single-query planners for manipulator motion generation, and they routinely serve as the classical baseline and fallback layer around learned motion policies.
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