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This study presents the hardware and software architecture of a transformative system for illuminating line drawings and letterforms. These mid-air illuminations are indoors and might be animated. The hardware contribution is a drone equipped with servo-actuated rod joints and a dense, addressable LED strip that enables arbitrary orientation, a LightBender. The software contributions are threefold. First, the system implements algorithms and heuristics to estimate the minimum number of LightBenders required to render a line drawing or letterform, stagger swarm formations to mitigate LightBender downwash, generate Swarm Flight and Lighting (SFL) files, and execute these files using a swarm of LightBenders to illuminate line drawings and letterforms. Second, a Blender add-on enables users to register LightBenders, author graphics and animations represented by swarms of LightBenders, and deploy the swarm for illumination through one-click functions. Third, users may import SVG files into either the Blender add-on or a standalone LB-Author tool to illuminate line drawings directly from vector graphics. We present results from an IRB-approved human subject study (n=21) to evaluate the impact of LightBender misalignment on the perceived illuminations. Obtained results demonstrate that the system's 10.1 mm maximum misalignment is perceptually acceptable across tested illuminations, with a median quality rating of 8 on a 0-10 scale. 

This paper presents the design and implementation of a Flying Light Speck (FLS) to illuminate English letters. The FLS uses its onboard camera and computing to localize and follow a trajectory to illuminate a letter. We evaluate the illuminations quantitatively and qualitatively. The latter is based on an IRB approved human subject study with 20 participants. The obtained results show a 42 to 56 millimeter error that impacts the detection of letters. A key finding is that the order in which the illumination of letters is presented to subjects has a significant effect on detection duration. 

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. 

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 paper, 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. 

One may construct a 3D multimedia display using miniature drones configured with light sources, Flying Light Specks (FLSs). Swarms of FLSs localize to illuminate complex 3D shapes and animated sequences. This requires FLSs to measure their relative pose (distance and angle) accurately. A challenge is how to do this when the sensors used by FLSs have a blind range that prevents them from quantifying their relative pose. Our technique, Swazure, requires FLSs to cooperate to compensate for their sensor's blind range. It implements {\em physical data independence} by abstracting the physical characteristics of the sensors, making point cloud data independent of the sensor hardware. The size of an FLS relative to the minimum distance between points of a point cloud is an important parameter. It may result in potential obstructions that prevent Swazure from quantifying relative pose. We present two techniques, move obstructing and move source, to address this limitation. Our experimental results show the superiority of the Move Obstructing technique. 

Swarical, a Swarm-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). 

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$$\leq$$5) while a different technique named Closest Available Neighbor First (CANF) is superior when constructing large groups (G$$\geq$$10).