This article examines robot motion planning, comprising path and trajectory planning, which represents one of the most challenging aspects of robot software architecture. These functionalities enable robots to determine optimal movement paths according to specified criteria while avoiding obstacles. This introductory survey presents a comprehensive general review of path and trajectory planning approaches and a state-of-the-art analysis of recent developments in this area, with particular emphasis on the optimality of planned paths and trajectories. The article provides a general literature review that categorizes motion planning algorithms into four primary families: random sampling-based methods (such as rapidly exploring random trees), optimal control methods (such as model predictive control), artificial potential field methods, and graph search methods (such as Dijkstra and A*). Additional approaches, including simulated annealing, genetic algorithms, and particle swarm optimization, are also addressed. Finally, features of the reviewed algorithms are discussed, and a comparative analysis of selected features for specific algorithms is presented in the conclusion.
Vošahlík et al. (Fri,) studied this question.