TY - JOUR
T1 - HIA-APF
T2 - A hierarchical based path planning method for lunar rovers on a partially observable surface
AU - Lu, Siyao
AU - Xu, Rui
AU - Li, Zhaoyu
AU - Wang, Bang
AU - Zhu, Shengying
AU - Gao, Ai
AU - Pan, Bo
AU - Zhao, Zhijun
N1 - Publisher Copyright:
© 2026
PY - 2026/5
Y1 - 2026/5
N2 - Autonomous lunar rovers are crucial for lunar exploration and construction. However, the precision of exploration on the moon is limited, as the rover's high-resolution vision is confined to its immediate surroundings, making long-term planning challenging. High precision is essential throughout the entire path-planning process, but only low-resolution Digital Elevation Model (DEM) maps are available prior to the mission. Paths generated by global planners such as A-Star may collide with obstacles due to insufficient detail in the global vision, while traditional incremental algorithms like D-Star plan slowly. Thus, we propose the Hierarchical Iterative A-star-Artificial Potential Field (HIA-APF) method, an innovative approach to path planning for lunar rovers operating on partially observable surfaces, and is applicable to both flat lunar base areas and rugged mining passway areas. First, we set intermediate coordinates determined by high-level A-Star and APF on low-resolution maps of the lunar base area for high-level planning. The iterative nature of the planning process allows effective navigation using explored low-resolution maps while adapting to limited high-resolution local observations. Then we present a low-level planning method composed of different artificial potential fields for modeling both small and large obstacles. Using artificial potential fields as heuristics within the A-Star algorithm enhances obstacle avoidance and target selection capabilities. This hierarchical approach combines the efficiency of the A-Star algorithm with the adaptability of the D-Star algorithm, enabling long-term path planning that is responsive to the dynamic lunar environment. Our experiments illustrate that the hierarchical method generates better paths than the A-Star, D-Star and Online RRT-Star, even on a selected base and passway area from the real DEM map, providing superior path quality and resource management for lunar exploration missions.
AB - Autonomous lunar rovers are crucial for lunar exploration and construction. However, the precision of exploration on the moon is limited, as the rover's high-resolution vision is confined to its immediate surroundings, making long-term planning challenging. High precision is essential throughout the entire path-planning process, but only low-resolution Digital Elevation Model (DEM) maps are available prior to the mission. Paths generated by global planners such as A-Star may collide with obstacles due to insufficient detail in the global vision, while traditional incremental algorithms like D-Star plan slowly. Thus, we propose the Hierarchical Iterative A-star-Artificial Potential Field (HIA-APF) method, an innovative approach to path planning for lunar rovers operating on partially observable surfaces, and is applicable to both flat lunar base areas and rugged mining passway areas. First, we set intermediate coordinates determined by high-level A-Star and APF on low-resolution maps of the lunar base area for high-level planning. The iterative nature of the planning process allows effective navigation using explored low-resolution maps while adapting to limited high-resolution local observations. Then we present a low-level planning method composed of different artificial potential fields for modeling both small and large obstacles. Using artificial potential fields as heuristics within the A-Star algorithm enhances obstacle avoidance and target selection capabilities. This hierarchical approach combines the efficiency of the A-Star algorithm with the adaptability of the D-Star algorithm, enabling long-term path planning that is responsive to the dynamic lunar environment. Our experiments illustrate that the hierarchical method generates better paths than the A-Star, D-Star and Online RRT-Star, even on a selected base and passway area from the real DEM map, providing superior path quality and resource management for lunar exploration missions.
KW - A-star
KW - Artificial potential field
KW - Autonomous lunar rover
KW - Path planning
UR - https://www.scopus.com/pages/publications/105029911536
U2 - 10.1016/j.robot.2026.105388
DO - 10.1016/j.robot.2026.105388
M3 - Article
AN - SCOPUS:105029911536
SN - 0921-8890
VL - 199
JO - Robotics and Autonomous Systems
JF - Robotics and Autonomous Systems
M1 - 105388
ER -