Comparar métodos

	Árvore Aleatória de Exploração Rápida	Controle Preditivo por Modelo
Área	Teoria de controle	Teoria de controle
Família	Machine learning	Machine learning
Ano de origem	1998	1978
Autor original	Steven M. LaValle	Jacques Richalet
Tipo	algorithm	algorithm
Fonte seminal	LaValle, S. M. (1998). Rapidly-exploring random trees: A new tool for path planning. Technical Report TR 98-11, Iowa State University.	Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control. Automatica, 14(5), 413-428.
Outros nomes	RRT, Incremental Sampling-based Algorithm	MPC, Receding Horizon Control
Relacionados	3	5
Resumo	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.	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.