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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. 

We present flight patterns for a collision-free passage of swarms of drones through one or more openings. The narrow openings provide drones with access to an infrastructure component such as charging stations to charge their depleted batteries and hangars for storage. The flight patterns are a staging area (queues) that match the rate at which an infrastructure component and its openings consume drones. They prevent collisions and may implement different policies that control the order in which drones pass through an opening. We illustrate the flight patterns with a 3D display that uses drones configured with light sources to illuminate shapes. 

Today's robotic laboratories for drones are housed in a large room. At times, they are the size of a warehouse. These spaces are typically equipped with permanent devices to localize the drones, e.g., Vicon Infrared cameras. Significant time is invested to fine-tune the localization apparatus to compute and control the position of the drones. One may use these laboratories to develop a 3D multimedia system with miniature sized drones configured with light sources. As an alternative, this brave new idea paper envisions shrinking these room-sized laboratories to the size of a cube or cuboid that sits on a desk and costs less than 10K dollars. The resulting Dronevision (DV) will be the size of a 1990s Television. In addition to light sources, its Flying Light Specks (FLSs) will be network-enabled drones with storage and processing capability to implement decentralized algorithms. The DV will include a localization technique to expedite development of 3D displays. It will act as a haptic interface for a user to interact with and manipulate the 3D virtual illuminations. It will empower an experimenter to design, implement, test, debug, and maintain software and hardware that realize novel algorithms in the comfort of their office without having to reserve a laboratory. In addition to enhancing productivity, it will improve safety of the experimenter by minimizing the likelihood of accidents. This paper introduces the concept of a DV, the research agenda one may pursue using this device, and our plans to realize one. 

Swarm-Merging, SwarMer, is a decentralized framework to localize Flying Light Specks (FLSs) to render 2D and 3D shapes. An FLS is a miniature sized drone equipped with one or more light sources to generate different colors and textures with adjustable brightness. It is battery powered, network enabled with storage and processing capability to implement a decentralized algorithm such as SwarMer. An FLS is unable to render a shape by itself. SwarMer uses the inter-FLS relationship effect of its organizational framework to compensate for the simplicity of each individual FLS, enabling a swarm of cooperating FLSs to render complex shapes. SwarMer is resilient to network packet loss, FLSs failing, and FLSs leaving to charge their battery. It is fast, highly accurate, and scales to remain effective when a shape consists of a large number of FLSs. 

A Flying Light Speck, FLS, is a miniature sized drone configured with light sources to illuminate 3D multimedia objects in a fixed volume, an FLS display. A swarm of FLSs may provide haptic interactions by exerting force back at a user's touch. This paper presents a conceptual model for the multimedia data to enable content-based queries. The model empowers users of an FLS display to annotate the illuminations by adding semantics to the data, extending a multimedia repository with information and knowledge. We present a core conceptual model and demonstrate its extensions for two diverse applications, authoring tools with entertainment and MRI scans with healthcare. 

This paper provides an overview of different forms of reality, comparing and contrasting them with one another. It argues the definition of the term "reality" is ambiguous. This motivates an internalization of elements from a technology standpoint, e.g., biological, 3D printed, Flying Light Speck illuminations, etc. 

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).