Swarical: An Integrated Hierarchical Approach to Localizing Flying Light Specks

2026-05-22

AI Summary

This work addresses the challenge of achieving efficient and accurate self-localization and cooperative illumination for fleets of flying light spots (FLS) in complex 2D/3D environments. The authors propose a hierarchical swarm intelligence localization method that integrates heterogeneous sensor orientations with line-of-sight constraints. By leveraging Raspberry Pi cameras and ArUco markers to detect neighboring nodes, the approach converts mesh models into point clouds and employs a hierarchical anchor selection strategy combined with a decentralized algorithm to guide the FLS swarm toward high-precision self-localization. Notably, this is the first method to incorporate both sensor orientation and line-of-sight constraints into a hierarchical localization framework, achieving over twice the speed of existing decentralized approaches while maintaining localization accuracy, thereby significantly enhancing system efficiency and applicability.

Abstract

Swarical, a Swarm-based hierarchical localization technique, enables miniature drones, known as Flying Light Specks (FLSs), to accurately and efficiently localize and illuminate complex 2D and 3D shapes. Its accuracy depends on the physical hardware (sensors) of FLSs, which are used to track neighboring FLSs in order to localize themselves. It uses the hardware specification to convert mesh files into point clouds that enable a swarm of FLSs to localize at the highest accuracy afforded by their hardware. Swarical considers a heterogeneous mix of FLSs with different orientations for their tracking sensors, ensuring a line of sight between a localizing FLS and its anchor FLS. We present an implementation using Raspberry cameras and ArUco markers. A comparison of Swarical with a state of the art decentralized localization technique shows that it is as accurate and more than 2x faster.

Problem

• localization
• Flying Light Specks
• swarm
• 3D shapes
• sensor constraints

Innovation

• Swarm-based localization
• Flying Light Specks
• heterogeneous swarm
• point cloud generation
• decentralized positioning