Batch Informed Vines (BIV*): Heuristically Guided Exploration of Narrow Passages by Batch Vine Expansion

Ziheng Jiang, Chang Huang, Hongtu Zhou, Linlin Zhou, Chen Ye, Junqiao Zhao

Abstract: Commonly used asymptotically convergent sampling algorithms (SBP) often utilize the Informed Set (IS) to enhance sampling efficiency. However, IS typically requires obtaining a low-cost solution first, which is challenging when narrow passages are present in the environment. To address the narrow passage problem, RRT with vine expansion (RRV) has been proposed. However, it explores narrow passages via local sampling, which can result in a suboptimal exploration order. Moreover, the local sampling strategies of RRV cannot differentiate between explored and unexplored areas, potentially leading to redundant local sampling. Therefore, we propose an enhanced heuristic-based vine expansion method, termed Batch Informed Vines (BIV*). BIV* utilizes path information from the current search tree as heuristics to prioritize the exploration of narrow passages leading to lower solution cost. Additionally, we propose a batch vine expansion strategy, which includes exploration of “Closer to Unexplored Obstacle” (CTUO) nodes and batch expansion. Experiments demonstrate that BIV* achieves comparable efficiency to BIT* in exploring easy-to-find topologies, while surpassing existing vine expansion methods when exploring complex environments with narrow passages.

External IDs:dblp:journals/ral/JiangHZZYZ25