Accurate trajectory tracking is essential for the autonomous navigation of mobile robots in different known and unknown environments. This paper proposes an approach based on the particle swarm optimization (PSO) algorithm to improve trajectory tracking capability in wheeled mobile robots. The functional tests were carried out on a mobile robot with differential configuration. The algorithm was initially implemented in Matlab and then translated into Python for execution in the mBlock programming environment, compatible with the mbot Neo robot. Tests were performed on five different trajectories (elliptical, circular, Lissajous curve, Cardioid curve, and Senoid curve) to measure the performance of the algorithm. The metric used to evaluate performance was the average position error, which calculates the distance between the robot’s theoretical and actual position at each point in the trajectory. The results showed that the algorithm maintains a reasonable consistency in its performance, with moderate variability in average position errors and a low standard deviation. These findings suggest that PSO is effective and robust for the generation of accurate trajectories in mobile robotics, although further adjustments to the algorithm parameters are recommended.