Salīdzināt metodes

	Probabilistiskais ceļa plāns	Strauji izpētošs nejaušs koks
Nozare	Vadības teorija	Vadības teorija
Saime	Machine learning	Machine learning
Izcelsmes gads	1996	1998
Autors	Lydia Kavraki	Steven M. LaValle
Tips	algorithm	algorithm
Pirmavots	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.
Citi nosaukumi	PRM, Roadmap Method	RRT, Incremental Sampling-based Algorithm
Saistītās	2	3
Kopsavilkums	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.