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	急成長ランダムツリー	フィードバック線形化
分野	制御理論	制御理論
系統	Machine learning	Machine learning
提唱年	1998	1983
提唱者	Steven M. LaValle	Alberto Isidori
種類	algorithm	algorithm
原典	LaValle, S. M. (1998). Rapidly-exploring random trees: A new tool for path planning. Technical Report TR 98-11, Iowa State University.	Isidori, A. (1995). Nonlinear Control Systems (3rd ed.). Springer-Verlag.
別名	RRT, Incremental Sampling-based Algorithm	Exact Linearization, Nonlinear Feedback Control, Input-Output Linearization
関連	3	4
概要	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.	Feedback Linearization is a nonlinear control technique that uses a nonlinear state-feedback transformation to convert a nonlinear system into a linear one, enabling the use of standard linear control methods. Developed by Isidori, Sontag, and others in the 1980s, feedback linearization is conceptually elegant and powerful: if the system satisfies certain structural conditions (relative degree, decoupling matrix rank), the nonlinearities can be exactly cancelled through feedback, reducing the problem to linear design.