Σύγκριση μεθόδων

	Δέντρο Τυχαίας Ταχείας Εξερεύνησης	Πιθανοτικός Χάρτης Πορείας
Πεδίο	Θεωρία Ελέγχου	Θεωρία Ελέγχου
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	1998	1996
Δημιουργός	Steven M. LaValle	Lydia Kavraki
Τύπος	algorithm	algorithm
Θεμελιώδης πηγή	LaValle, S. M. (1998). Rapidly-exploring random trees: A new tool for path planning. Technical Report TR 98-11, Iowa State University.	Kavraki, L. E., Svestka, P., Latombe, J. C., & Overmars, M. H. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation, 12(4), 566-580.
Εναλλακτικές ονομασίες	RRT, Incremental Sampling-based Algorithm	PRM, Roadmap Method
Συναφείς	3	2
Σύνοψη	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.	The Probabilistic Roadmap (PRM) method is a motion planning algorithm that builds a pre-computed graph (roadmap) of feasible paths through the configuration space by sampling random configurations and connecting them if collision-free. Introduced by Kavraki et al. in 1996, PRM is powerful for multi-query planning scenarios where many path queries are answered, amortizing roadmap construction cost across many queries.