Bandingkan kaedah

	Peta Laluan Berkemungkinan	Pokok Rawak yang Meneroka dengan Pantas
Bidang	Teori Kawalan	Teori Kawalan
Keluarga	Machine learning	Machine learning
Tahun asal	1996	1998
Pengasas	Lydia Kavraki	Steven M. LaValle
Jenis	algorithm	algorithm
Sumber perintis	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.
Alias	PRM, Roadmap Method	RRT, Incremental Sampling-based Algorithm
Berkaitan	2	3
Ringkasan	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.