2025/03/19 by Ryota Takamido, Jun Ota, Takamido, Ryota +1 · 1 citation
Computer Science · #AI-based Problem Solving and Planning #FOS: Computer and information sciences #Robotic Path Planning Algorithms #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2503.15715
openalex publication_date 2025/03/19 · openalex created_date 2025/10/17 · openalex updated_date 2026/07/28
This study aims to address the key challenge of obtaining a high-quality solution path within a short calculation time by generalizing a limited dataset. In the informed experience-driven random trees connect star (IERTC*) process, the algorithm flexibly explores the search trees by morphing the micro paths generated from a single experience while reducing the path cost by introducing a re-wiring process and an informed sampling process. The core idea of this algorithm is to apply different strategies depending on the complexity of the local environment; for example, it adopts a more complex curved trajectory if obstacles are densely arranged near the search tree, and it adopts a simpler straight line if the local environment is sparse. The results of experiments using a general motion benchmark test revealed that IERTC* significantly improved the planning success rate in difficult problems in the cluttered environment (an average improvement of 49.3% compared to the state-of-the-art algorithm) while also significantly reducing the solution cost (a reduction of 56.3%) when using one hundred experiences. Furthermore, the results demonstrated outstanding planning performance even when only one experience was available (a 43.8% improvement in success rate and a 57.8% reduction in solution cost).