ArXiv

Safe Aerial 3D Path Planning for Autonomous UAVs using Magnetic Potential Fields

2026-05-11 · 17:28 UTC ·Haechan Mark Bong, Giovanni Beltrame ·1 min read
Authors
Haechan Mark Bong, Giovanni Beltrame
Categories
cs.RO
arXiv
https://arxiv.org/abs/2605.10880v1
PDF
https://arxiv.org/pdf/2605.10880v1

Brief

3DMaxConvNet extends the MaxConvNet magnetic potential-field planner into 3D by training a convolutional autoencoder to generate obstacle-aware potential fields from LiDAR-derived 101^3 voxel grids. In Cosys-AirSim experiments (100 randomized closed-loop trials on two urban maps) it reached 100% success without retraining, matched A* path quality with ~2× lower runtime, and ran ~200× faster than RRT*(3k).

Why it matters

3DMaxConvNet extends the 2D MaxConvNet magnetic potential-field planner to 3D, using a convolutional autoencoder to predict obstacle-aware potential fields from LiDAR-derived 101^3 voxel grids and achieved 100% path-planning success across 100 randomized closed-loop trials on two Cosys-AirSim urban maps (dense night-time cityscape and suburban district) without retraining.

Key details

  • Offline, 3DMaxConvNet produces path lengths comparable to A* on unseen maps while reducing runtime from 0.155–0.17s (A*) to 0.087–0.089s (≈1.7–1.95× faster); compared with RRT*(3k) it achieves similar path quality while cutting runtime from 17.2–17.5s to ≈0.09s (≈193–201× faster).
Source evidence

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).