- Conference Article
- 10.1109/stsiva66383.2025.11156386
Postprocessing Optimization of RRT* Using Machine Learning and Information Theory for Robotic Navigation
- Aug 27, 2025
- Marcelo Saavedra Alcoba + 3 more +3
This 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.
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