A Flying Light Speck (FLS) is a small drone configured with light sources to illuminate different colors and textures. A swarm of FLSs illuminates complex 3D multimedia shapes in a fixed volume, a 3D display. An FLS is a mechanical device. Its failure is the norm rather than an exception, causing a point of an illumination to go dark. In this article, we use reliability groups with dark standby FLSs to minimize the duration of time a point remains dark. We introduce three techniques to prevent a dark standby FLS from obstructing the user’s Field of View (FoV). All three move the FLS out of the user’s FoV. One technique, Suspend:Closest, maximizes the utility of a standby FLS while preventing it from obstructing the user’s FoV.

This companion paper provides artifacts and instructions on replicating the experiments in the ACM Multimedia 2024 paper entitled ''Swarical: An Integrated Hierarchical Approach to Localizing Flying Light Specks.'' Swarm-based hierarchical, Swarical, is a localization technique that enables miniature drones, Flying Light Specks (FLSs), to accurately and efficiently localize and illuminate complex 2D and 3D shapes. It consists of two components, an offline planner and an online localization technique that executes on an FLS. The offline planner uses the FLS sensor specification for positioning to convert mesh files into swarms of FLSs. Some FLSs are dark and used only for localization. We reported the online localization technique to be fast and highly accurate. We describe how to reproduce this finding using our artifacts.

Swarical, a Swar m-based hierarchical localization technique, enables miniature drones, 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 used to track neighboring FLSs to localize themselves. It uses the specification of the sensors to convert mesh files into point clouds that enable a swarm of FLSs to localize at the highest accuracy afforded by their sensors. 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.

A Flying Light Speck, FLS, is a miniature sized drone configured with light sources to illuminate different colors and textures. A swarm of FLSs illuminates complex 3D multimedia shapes in a fixed volume, a 3D display. An FLS is a mechanical device. Its failure is the norm rather than an exception, causing a point of an illumination to go dark. In this paper, we use reliability groups with dark standby FLSs to minimize the duration of time a point remains dark. This study makes two novel contributions. First, it compares a centralized and a decentralized algorithm to form groups, demonstrating the superiority of the centralized technique. Second, it detects when the dark standby FLSs may obstruct the user's field of view and relocates them with minimal impact on their provided benefit.

Group formation is fundamental for 3D displays that use Flying Light Specks, FLSs, to illuminate shapes and provide haptic interactions. An FLS is a drone with light sources that illuminates a shape. Groups of G FLSs may implement reliability techniques to tolerate FLS failures, provide kinesthetic haptic feedback in response to a user’s touch, and facilitate a divide and conquer approach to challenges such as localizing FLSs to render a shape. This paper evaluates four decentralized techniques to form groups. An FLS implements a technique autonomously using asynchronous communication and without a global clock. We evaluate these techniques using synthetic point clouds with known optimal solutions and real point clouds. Obtained results show a technique named Random Subset (RS) is superior when constructing small groups (G ≤ 5) while a different technique named Closest Available Neighbor First (CANF) is superior when constructing large groups (G ≥ 10).

A Flying Light Speck, FLS, is a miniature sized drone configured with light sources. Swarms of FLSs will illuminate an object in a 3D volume, an FLS display. These illuminations and their data models are the novel contributions of this paper. We introduce a conceptual model of drone flight paths to render static, slide, and motion illuminations. We describe a physical implementation of the conceptual model using bag files. We evaluate this implementation using different lossless compression techniques. A key finding is that our bag file implementation is very compact when compared with the original point clouds. While compression reduces the size of a bag file, a combination that includes the use of both internal bag file compression (lz4 with chunks) and Gzip is not necessarily the most compact representation. We open source our software and its point cloud sequence data for use by the scientific community, see https://github.com/flyinglightspeck/FLSbagfile.