AI RESEARCH
Performance Comparison of Classical and Neural Sampling Algorithms for Robotic Navigation
arXiv CS.AI
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ArXi:2605.25010v1 Announce Type: cross Integrating artificial intelligence (AI) into sampling-based motion planning provides new possibilities for improving autonomous navigation efficiency. In this paper, three algorithms, namely RRT*, Neural RRT*, and Neural Informed RRT*, are implemented and evaluated on environments containing convex and concave obstacles with different obstacle densities. The obtained results indicate that neural-guided planners improve path quality, producing up to 14\% shorter paths and 55--75\% smoother trajectories compared with the conventional RRT* algorithm.