Introduction to Recent Advancements in Optimal Path and Trajectory Planning for Robots and Autonomous Machines
David Vošahlík, Marek Obitko · 2025
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 (optimizing given criterion) while avoiding obstacles. This introductory study provides a survey of path and trajectory planning approaches and a state-of-the-art review of recent works in this area, focusing on the optimality of planned paths and trajectories. This article categorizes motion planning algorithms into four primary families: Random Sampling-based methods (like RRT, PRM); Optimal Control methods (like MPC); Artificial Potential Field methods; and Graph Search methods (like Dijkstra, 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 comparison table of selected features for specific algorithms is presented in the conclusion.