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| Probabilistyczna Mapa Drogowa× | Drzewo Szybko Eksplorujące Losowe× | |
|---|---|---|
| Dziedzina | Teoria sterowania | Teoria sterowania |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1996 | 1998 |
| Twórca≠ | Lydia Kavraki | Steven M. LaValle |
| Typ | algorithm | algorithm |
| Źródło pierwotne≠ | 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. DOI ↗ | LaValle, S. M. (1998). Rapidly-exploring random trees: A new tool for path planning. Technical Report TR 98-11, Iowa State University. link ↗ |
| Inne nazwy | PRM, Roadmap Method | RRT, Incremental Sampling-based Algorithm |
| Pokrewne≠ | 2 | 3 |
| Podsumowanie≠ | 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. |
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