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This paper introduces a hybrid framework for autonomous unmanned ground vehicle (UGV) navigation that combines low-resolution Digital Elevation Model (DEM) data with real-time LiDAR sensor feedback for improved path planning. By initially computing a global path using a DEM-based A* algorithm and then adapting it with local sensor data, the approach allows UGVs to effectively navigate sudden changes in terrain while ensuring safety and efficiency. Simulation results show a 95% obstacle avoidance rate and a significant reduction in the average encountered slope, highlighting the framework's potential for enhancing autonomous navigation in complex outdoor environments.
Achieving a 95% obstacle avoidance rate while reducing slope challenges from 8掳 to 2.7掳 reveals a breakthrough in UGV navigation under dynamic conditions.
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.
