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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. 

This paper presents the design and implementation of a circular flight pattern for use by a 3D multimedia display, a Dronevision (DV). A DV uses drones configured with light sources, Flying Light Specks (FLSs), that are battery powered. The flight pattern enables a swarm of FLSs to enter an opening, granting them access to the charging coils to charge their batteries. We present two algorithms for an FLS to travel from its current coordinate to rendezvous with its assigned slot on the flight pattern, Shortest Distance (SD) and Fastest Rendezvous Time (FRT). In addition to quantifying the tradeoff associated with these algorithms, we present an implementation using a swarm of Crazyflie drones with Vicon localization. 

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). 

This study evaluates the accuracy of three different types of time-of-flight sensors to measure distance. We envision the possible use of these sensors to localize swarms of flying light specks (FLSs) to illuminate objects and avatars of a metaverse. An FLS is a miniature-sized drone configured with RGB light sources. It is unable to illuminate a point cloud by itself. However, the inter-FLS relationship effect of an organizational framework will compensate for the simplicity of each individual FLS, enabling a swarm of cooperating FLSs to illuminate complex shapes and render haptic interactions. Distance between FLSs is an important criterion of the inter-FLS relationship. We consider sensors that use radio frequency (UWB), infrared light (IR), and sound (ultrasonic) to quantify this metric. Obtained results show only one sensor is able to measure distances as small as 1 cm with a high accuracy. A sensor may require a calibration process that impacts its accuracy in measuring distance.