2010/01/14 by Barak Raveh, Raveh, Barak, Angela Enosh +3 · 1 citation
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Autonomous Vehicle Technology and Safety #Computational Geometry and Mesh Generation #FOS: Computer and information sciences #I.2.9 #Robotic Path Planning Algorithms #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.1001.2391
openalex publication_date 2010/01/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Sampling-based motion planners are an effective means for generating collision-free motion paths. However, the quality of these motion paths (with respect to quality measures such as path length, clearance, smoothness or energy) is often notoriously low, especially in high-dimensional configuration spaces. We introduce a simple algorithm for merging an arbitrary number of input motion paths into a hybrid output path of superior quality, for a broad and general formulation of path quality. Our approach is based on the observation that the quality of certain sub-paths within each solution may be higher than the quality of the entire path. A dynamic-programming algorithm, which we recently developed for comparing and clustering multiple motion paths, reduces the running time of the merging algorithm significantly. We tested our algorithm in motion-planning problems with up to 12 degrees of freedom. We show that our algorithm is able to merge a handful of input paths produced by several different motion planners to produce output paths of much higher quality.