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

TL;DR

This paper introduces 3DMaxConvNet, a real-time 3D path planning method for UAVs in urban environments that leverages magnetic potential fields and deep learning to ensure safety and efficiency.

Contribution

The extension of MaxConvNet to 3D using a convolutional autoencoder for obstacle-aware potential field prediction, enabling faster and reliable UAV navigation.

Findings

Achieved 100% success rate in urban environment trials without retraining.

Reduced path planning runtime by approximately 2 times compared to A*.

Significantly faster than RRT* with similar path quality, reducing runtime by over 190 times.

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).