用磁势场实现无人机3D避障，实时高效且100%成功。
Safe Aerial 3D Path Planning for Autonomous UAVs using Magnetic Potential Fields
- 基于麦克斯韦方程设计3D磁势场，避免局部极小点
- 在两个城市环境测试中路径规划成功率100%，无需重训练
- 比A*快1.7~1.95倍，比RRT*(3k)快193~201倍
安全的自主无人飞行器（UAV）在城市环境中导航需要实时避障路径规划。MaxConvNet是一种利用麦克斯韦方程性质的势场规划器，可在不产生局部极小值的情况下生成至目标点的路径。本文将2D MaxConvNet扩展至3D，使用卷积自编码器从LiDAR生成的101^3体素网格中预测障碍物感知的势场。在两个不同的Cosys-AirSim城市环境（密集夜间城区与郊区）中进行100次随机闭环试验，均达到100%路径规划成功率，且无需重新训练。离线规划中，3DMaxConvNet在未见地图上的路径长度与A*相当，但运行时间从0.155–0.17秒降至0.087–0.089秒，提速约1.7–1.95倍；相较RRT*(3k)（原需17.2–17.5秒），路径质量相近而规划时间缩短至约0.09秒，提速约193–201倍。
原文摘要 · Abstract (English)
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).
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。