เปรียบเทียบวิธี

	Rapidly-Exploring Random Tree	การทำให้เป็นเชิงเส้นด้วยผลป้อนกลับ
สาขาวิชา	ทฤษฎีการควบคุม	ทฤษฎีการควบคุม
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	1998	1983
ผู้ริเริ่ม	Steven M. LaValle	Alberto Isidori
ประเภท	algorithm	algorithm
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	RRT, Incremental Sampling-based Algorithm	Exact Linearization, Nonlinear Feedback Control, Input-Output Linearization
ที่เกี่ยวข้อง	3	4
สรุป	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.