DEMデータ統合によるUGV自律航法のための地形認識型ローカル経路計画
Terrain-Aware Local Path Planning with Global DEM Data Integration for Autonomous UGV Navigation
低解像度のDEMデータとリアルタイムLiDARセンサを統合し、グローバル経路とローカル適応を組み合わせたUGVの経路計画フレームワークを提案。シミュレーションで障害物回避率95%と平均斜面角度の低減を達成した。
著者: Devender Singh, Issah Nazif Suleiman, Paul Mitten, Glenn Cutler, Vinicius Prado da Fonseca, Matthew Hamilton
分類: cs.RO
原文アブストラクト
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.