@Article{cmc.2025.068780,
AUTHOR = {Zhenpeng Jiang, Qingquan Liu, 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},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {86},
YEAR = {2026},
NUMBER = {1},
PAGES = {1--18},
URL = {http://www.techscience.com/cmc/v86n1/64434},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2025.068780}
}