Terrain-Aware Local Path Planning with Global DEM Data Integration for Autonomous UGV Navigation

• URL: http://arxiv.org/abs/2608.17038v1
• Date: Mon, 17 Aug 2026 18:38:19 GMT
• Title: Terrain-Aware Local Path Planning with Global DEM Data Integration for Autonomous UGV Navigation
• Abstract summary: This work proposes a hybrid framework that integrates low-resolution Digital Elevation Model (DEM) data with real-time LiDAR-based obstacle detection and terrain analysis.<n>A global path is initially computed using a preprocessed DEM-based A* algorithm.<n>Local sensor data drives adaptive path correction, enabling the UGV to negotiate sudden environmental changes while maintaining safety and efficiency.
• Score: 0.9236074230806578
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Autonomous navigation in complex outdoor terrains presents critical challenges for unmanned ground vehicles (UGVs) due to the inherent disconnect between global mapping and real-time sensor feedback. This work proposes a hybrid framework that integrates low-resolution Digital Elevation Model (DEM) data with real-time LiDAR-based obstacle detection and terrain analysis for efficient path planning. A global path is initially computed using a preprocessed DEM-based A* algorithm. Subsequently, local sensor data drives adaptive path correction, enabling the UGV to negotiate sudden environmental changes while maintaining safety and efficiency. Simulation results in Gazebo demonstrate significant improvements over a baseline approach, achieving a 95\% obstacle avoidance rate and reducing the average encountered slope from $8^\circ$ to $2.7^\circ$ in custom terrain. This integration enhances path efficiency and terrain traversability and supports robust real-time adaptation, paving the way for more reliable autonomous navigation in dynamic outdoor environments.

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