@article{Jiang2026, 
author = {Zhenpeng Jiang and Qingquan Liu and Ende Wang},
title = {HS-APF-RRT*: An Off-Road Path-Planning Algorithm for Unmanned Ground Vehicles Based on Hierarchical Sampling and an Enhanced Artificial Potential Field},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {86},
number = {1},
pages = {1-18},
keywords = {RRT*, APF, path planning;off-road, Unmanned Ground Vehicle (UGV)},
url = {https://www.sciopen.com/article/10.32604/cmc.2025.068780},
doi = {10.32604/cmc.2025.068780},
abstract = {Rapidly-exploring Random Tree (RRT) and its variants have become foundational in path-planning research, yet in complex three-dimensional off-road environments their uniform blind sampling and limited safety guarantees lead to slow convergence and force an unfavorable trade-off between path quality and traversal safety. To address these challenges, we introduce HS-APF-RRT*, a novel algorithm that fuses layered sampling, an enhanced Artificial Potential Field (APF), and a dynamic neighborhood-expansion mechanism. First, the workspace is hierarchically partitioned into macro, meso, and micro sampling layers, progressively biasing random samples toward safer, lower-energy regions. Second, we augment the traditional APF by incorporating a slope-dependent repulsive term, enabling stronger avoidance of steep obstacles. Third, a dynamic expansion strategy adaptively switches between 8 and 16 connected neighborhoods based on local obstacle density, striking an effective balance between search efficiency and collision-avoidance precision. In simulated off-road scenarios, HS-APF-RRT* is benchmarked against RRT*, Goal-Biased RRT*, and APF-RRT*, and demonstrates significantly faster convergence, lower path-energy consumption, and enhanced safety margins.}
}