Сравнение методов

	Вероятностная дорожная карта	Модельно-прогнозирующее управление
Область	Теория управления	Теория управления
Семейство	Machine learning	Machine learning
Год появления	1996	1978
Автор метода	Lydia Kavraki	Jacques Richalet
Тип	algorithm	algorithm
Основополагающий источник	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.	Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control. Automatica, 14(5), 413-428.
Другие названия	PRM, Roadmap Method	MPC, Receding Horizon Control
Связанные	2	5
Сводка	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.	Model Predictive Control (MPC) is an advanced control strategy that uses an explicit process model to predict future system behavior over a finite horizon and solves an optimization problem at each control step. First formalized by Richalet et al. in 1978, MPC has become the dominant approach in process control industries, from chemical plants to autonomous vehicles, because it naturally handles constraints and can optimize multiple objectives simultaneously.