2009/12/01 by Nicolas A. Barriga, Mauricio Araya, Barriga, Nicolas A. +4 · 1 citation
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Guidance and Control Systems #Robotic Path Planning Algorithms #Robotics (cs.RO) #Robotics and Sensor-Based Localization #cs.AI #cs.RO
paper · pdf · doi:10.48550/arxiv.0912.0266
8 pages, 7 figures. Presented at the XXVIII International Conference of the Chilean Computer Society 2009
arxiv created 2009/12/01 · openalex publication_date 2009/12/01 · arxiv updated 2009/12/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Probabilistic sampling methods have become very popular to solve single-shot path planning problems. Rapidly-exploring Random Trees (RRTs) in particular have been shown to be very efficient in solving high dimensional problems. Even though several RRT variants have been proposed to tackle the dynamic replanning problem, these methods only perform well in environments with infrequent changes. This paper addresses the dynamic path planning problem by combining simple techniques in a multi-stage probabilistic algorithm. This algorithm uses RRTs as an initial solution, informed local search to fix unfeasible paths and a simple greedy optimizer. The algorithm is capable of recognizing when the local search is stuck, and subsequently restart the RRT. We show that this combination of simple techniques provides better responses to a highly dynamic environment than the dynamic RRT variants.