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For more than a century, segmented displays, such as the seven‐segment display, have been a popular and cost‐effective option. They can display various commonly used characters by controlling a few custom‐shaped binary light emitters. Herein, the acoustic equivalent of segmented displays, which uses heterogeneous sound modulators to generate a limited set of acoustic holographic images, is introduced. Designing segmented acoustic displays is more challenging than optical ones due to the complex relationship between emitted sound and generated holographic images. To address this challenge, a design methodology based on unsupervised learning techniques is proposed. The approach balances the cost of acoustic displays and the quality of the images they generate, resulting in segmented displays that outperform existing general‐purpose ones when generating a finite set of acoustic images. Using simulations and physical fabrication of metamaterial‐based acoustic displays, it is proven that the approach can create segmented acoustic displays that produce high‐quality images at a lower cost. Additionally, the methodology is applied to multifrequency acoustic displays and its scalability is assessed as the number of images increases. The cost reduction through segmentation presented herein is expected to democratize sound manipulators for acoustic displays and other applications like acoustic levitation and noise cancellation. [ABSTRACT FROM AUTHOR]

High‐quality acousto‐holographic patterns and images, integral to applications like 3D displays, acoustophoresis, and midair haptics, require precise distribution of ultrasound waves to achieve. Essential tools for this task are spatial sound modulators (SSMs), which control constituent elements to enable dynamic distribution of sound pressure. However, current ultrasonic SSMs face limitations due to high costs and the intricate actuation of numerous small, closely spaced units. This study introduces "segmented SSMs," novel devices that combine traditional acoustic metasurface pixel units into custom‐shaped segmented elements. These segmented SSMs reduce actuation costs and complexity while retaining pressure distribution quality. This approach includes a custom phase agglomeration algorithm (PAA), that offers a hierarchy of potential segmentation solutions for user selection. An SSM fabrication method is detailed using off‐the‐shelf 3D printers and bespoke control electronics, completing an end‐to‐end methodology from conception to realization. This approach is validated with two prototype SSM devices that focus sound waves and levitate polystyrene beads using dynamic segmented elements. Further enhancements to the technique are explored through hybrid SSM devices with both static and dynamic elements. The pipeline facilitates efficient SSM construction across diverse applications and invites the inception of future devices with varying sizes, uses, and actuation mechanisms. [ABSTRACT FROM AUTHOR]

To effectively perform collective monitoring of dynamic environments, a robot swarm needs to adapt to changes by processing the latest information and discarding outdated beliefs. We show that in a swarm composed of robots relying on local sensing, adaptation is better achieved if the robots have a shorter rather than longer communication range. This result is in contrast with the widespread belief that more communication links always improve the information exchange on a network. We tasked robots with reaching agreement on the best option currently available in their operating environment. We propose a variety of behaviors composed of reactive rules to process environmental and social information. Our study focuses on simple behaviors based on the voter model—a well-known minimal protocol to regulate social interactions—that can be implemented in minimalistic machines. Although different from each other, all behaviors confirm the general result: The ability of the swarm to adapt improves when robots have fewer communication links. The average number of links per robot reduces when the individual communication range or the robot density decreases. The analysis of the swarm dynamics via mean-field models suggests that our results generalize to other systems based on the voter model. Model predictions are confirmed by results of multiagent simulations and experiments with 50 Kilobot robots. Limiting the communication to a local neighborhood is a cheap decentralized solution to allow robot swarms to adapt to previously unknown information that is locally observed by a minority of the robots. [ABSTRACT FROM AUTHOR]