# Urban Drone Navigation: Autoencoder Learning Fusion for Aerodynamics

arXiv: https://arxiv.org/abs/2310.08830 · HTML: https://mlsanity.com/paper/2310.08830/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 63 references that are in the map.
- First posted on arXiv 13 Oct 2023.

## Builds on (most cited first, 9 of 9)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [3D Bounding Box Estimation Using Deep Learning and Geometry](https://mlsanity.com/paper/1612.00496/) (2016)
- [Recent Advances in Autoencoder-Based Representation Learning](https://mlsanity.com/paper/1812.05069/) (2018)
- [DeepCFD: Efficient Steady-State Laminar Flow Approximation with Deep Convolutional Neural Networks](https://mlsanity.com/paper/2004.08826/) (2020)
- [Navion: A 2mW Fully Integrated Real-Time Visual-Inertial Odometry Accelerator for Autonomous Navigation of Nano Drones](https://mlsanity.com/paper/1809.05780/) (2018)
- [From Monocular SLAM to Autonomous Drone Exploration](https://mlsanity.com/paper/1609.07835/) (2016)
- [IMU Data Processing For Inertial Aided Navigation: A Recurrent Neural Network Based Approach](https://mlsanity.com/paper/2103.14286/) (2021)
- [Autonomous UAV Navigation Using Reinforcement Learning](https://mlsanity.com/paper/1801.05086/) (2018)
- [LiDAR-Based Navigation of Tethered Drone Formations in an Unknown Environment](https://mlsanity.com/paper/2003.12981/) (2020)

## Abstract (arXiv)

Drones are vital for urban emergency search and rescue (SAR) due to the challenges of navigating dynamic environments with obstacles like buildings and wind. This paper presents a method that combines multi-objective reinforcement learning (MORL) with a convolutional autoencoder to improve drone navigation in urban SAR. The approach uses MORL to achieve multiple goals and the autoencoder for cost-effective wind simulations. By utilizing imagery data of urban layouts, the drone can autonomously make navigation decisions, optimize paths, and counteract wind effects without traditional sensors. Tested on a New York City model, this method enhances drone SAR operations in complex urban settings.