השוואת שיטות

	מפת דרכים הסתברותית	עץ אקראי מתרחב במהירות
תחום	תורת הבקרה	תורת הבקרה
משפחה	Machine learning	Machine learning
שנת המקור	1996	1998
הוגה השיטה	Lydia Kavraki	Steven M. LaValle
סוג	algorithm	algorithm
מקור מכונן	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.	LaValle, S. M. (1998). Rapidly-exploring random trees: A new tool for path planning. Technical Report TR 98-11, Iowa State University.
כינויים	PRM, Roadmap Method	RRT, Incremental Sampling-based Algorithm
קשורות	2	3
תקציר	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.	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.