磁気ポテンシャル場を用いた自律UAVの安全な3次元経路計画
Safe Aerial 3D Path Planning for Autonomous UAVs using Magnetic Potential Fields
Maxwell方程式に基づく磁場ポテンシャル場を3次元に拡張し、LiDARボクセルから障害物を考慮したポテンシャル場を予測する畳み込みオートエンコーダを用いて、都市環境でのUAV経路計画を高速化する手法を提案した。
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著者: Haechan Mark Bong, Giovanni Beltrame
分類: cs.RO
原文アブストラクト
Safe autonomous Uncrewed Aerial Vehicle (UAV) navigation in urban environments requires real-time path planning that avoids obstacles. MaxConvNet is a potential-field planner that leverages properties of Maxwell's equations to generate a path to the goal without local minima. We extend the 2D MaxConvNet magnetic field planner to 3D, using a convolutional autoencoder to predict obstacle-aware potential fields from LiDAR-derived 101^3 voxel grids. Evaluation across 100 randomized closed-loop trials in two distinct Cosys-AirSim urban environments, a dense night-time cityscape and a suburban district shows a 100% path planning success rate on both maps without retraining. In offline path planning, 3DMaxConvNet produces path lengths comparable to A* on unseen maps while reducing runtime from 0.155--0.17s to 0.087--0.089s, or about 1.7--1.95 times faster than A*. Against RRT*(3k), 3DMaxConvNet achieves similar path quality while reducing planning runtime from 17.2--17.5s to about 0.09s, which is roughly 193--201 times faster than RRT*(3k).
