# Airship Formations for Animal Motion Capture and Behavior Analysis

arXiv: https://arxiv.org/abs/2404.08986 · HTML: https://mlsanity.com/paper/2404.08986/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 13 Apr 2024.

## Builds on (most cited first, 5 of 5)

- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Range, Endurance, and Optimal Speed Estimates for Multicopters](https://mlsanity.com/paper/2109.04741/) (2021)
- [Deep Neural Network-based Cooperative Visual Tracking through Multiple Micro Aerial Vehicles](https://mlsanity.com/paper/1802.01346/) (2018)
- [Simulation and Control of Deformable Autonomous Airships in Turbulent Wind](https://mlsanity.com/paper/2012.15684/) (2020)
- [Viewpoint-driven Formation Control of Airships for Cooperative Target Tracking](https://mlsanity.com/paper/2209.13040/) (2022)

## Abstract (arXiv)

Using UAVs for wildlife observation and motion capture offers manifold advantages for studying animals in the wild, especially grazing herds in open terrain. The aerial perspective allows observation at a scale and depth that is not possible on the ground, offering new insights into group behavior. However, the very nature of wildlife field-studies puts traditional fixed wing and multi-copter systems to their limits: limited flight time, noise and safety aspects affect their efficacy, where lighter than air systems can remain on station for many hours. Nevertheless, airships are challenging from a ground handling perspective as well as from a control point of view, being voluminous and highly affected by wind. In this work, we showcase a system designed to use airship formations to track, follow, and visually record wild horses from multiple angles, including airship design, simulation, control, on board computer vision, autonomous operation and practical aspects of field experiments.