Marcelo Saavedra AlcobaEdgar Salazar FlorezBrayan G. Duran ToconasKevin Marlon Soza Mamani2026-03-222026-03-22202510.1109/stsiva66383.2025.11156386https://doi.org/10.1109/stsiva66383.2025.11156386https://andeanlibrary.org/handle/123456789/77894This work presents an alternative post-processing approach for optimizing mobile robot trajectories by combining vector quantization techniques with information theory. We developed an algorithm based on Vector Quantization (VQ) and Kullback-Leibler Divergence (VQKL) that maintains the original RRT*'s obstacle avoidance capabilities. When comparing VQKL and VQ with the Ramer-Douglas-Peucker (RDP) algorithm, our methods demonstrate significant superiority: VQ achieves a 13% reduction in path length (versus RDP's 10%) while VQKL achieves 14%, along with an 83% (VQ) and 84% (VQKL) reduction in node count compared to the original RRT* output. These results are obtained through an adaptive optimization process that iteratively adjusts centroids using a progressive annealing scheme. To ensure trajectory feasibility, we implemented a validation system that verifies both geometric deviation from the original path and collision-free operation with obstacles. Extensive simulations across 20 different environments with 100 trials each confirm that our method generates significantly shorter, more efficient, and safer trajectories, establishing a viable alternative for robotic path optimization.enArtificial intelligenceVector quantizationComputer scienceComputer visionQuantization (signal processing)TrajectoryPath (computing)CentroidRobotMotion planningarticle