Safe Aerial 3D Path Planning for Autonomous UAVs using Magnetic Potential Fields
Haechan Mark Bong, Giovanni Beltrame
TLDR
This paper introduces 3DMaxConvNet, a magnetic potential field planner for safe, real-time 3D UAV navigation in urban environments.
Key contributions
- Extends the 2D MaxConvNet magnetic field planner to 3D for UAV navigation.
- Uses a convolutional autoencoder to predict obstacle-aware potential fields from LiDAR voxel grids.
- Achieves 100% path planning success in complex urban simulations without retraining.
- Outperforms A* (1.7-1.95x faster) and RRT* (193-201x faster) in planning runtime.
Why it matters
This paper significantly advances autonomous UAV navigation by providing a robust and extremely fast 3D path planning solution. Its ability to achieve 100% success in complex urban environments with superior speed compared to traditional methods makes it highly practical for real-world deployment.
Original Abstract
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).
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