Using Hexbugs™ to model gas pressure and electrical conduction: A pandemic-inspired distance lab

DiBari, Genevieve; Valle, Liliana; Bua, Refilwe Tanah; Cunningham, Lucas J.; Hort, Eleanor; Venenciano, Taylor; Hudgings, Janice

doi:10.1119/5.0087142

Cited by 5 publications

(1 citation statement)

References 15 publications

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“…Microrobots made from low-cost HEXBUG devices mounted with ESP32-CAM boards can broadcast images back to a Raspberry Pi acting as a base station. This paper uses two models: (1) the HEXBUG nano, which moves by vibratory locomotion to access hardto-reach areas [40], and (2) the HEXBUG Spider, which uses integrated servo motors to rhythmically propel six legs in a coordinated insect gait. An infrared remote link wirelessly directs the movement and rotation of the spider-like exoskeleton.…”

Section: Augmented Reality Monitoring and Object Detectionmentioning

confidence: 99%

Indoor Infrastructure Maintenance Framework Using Networked Sensors, Robots, and Augmented Reality Human Interface

Fath,

Hanna,

Liu

et al. 2024

Future Internet

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Sensing and cognition by homeowners and technicians for home maintenance are prime examples of human–building interaction. Damage, decay, and pest infestation present signals that humans interpret and then act upon to remedy and mitigate. The maintenance cognition process has direct effects on sustainability and economic vitality, as well as the health and well-being of building occupants. While home maintenance practices date back to antiquity, they readily submit to augmentation and improvement with modern technologies. This paper describes the use of networked smart technologies embedded with machine learning (ML) and presented in electronic formats to better inform homeowners and occupants about safety and maintenance issues, as well as recommend courses of remedial action. The demonstrated technologies include robotic sensing in confined areas, LiDAR scans of structural shape and deformation, moisture and gas sensing, water leak detection, network embedded ML, and augmented reality interfaces with multi-user teaming capabilities. The sensor information passes through a private local dynamic network to processors with neural network pattern recognition capabilities to abstract the information, which then feeds to humans through augmented reality and conventional smart device interfaces. This networked sensor system serves as a testbed and demonstrator for home maintenance technologies, for what can be termed Home Maintenance 4.0.

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Section: Augmented Reality Monitoring and Object Detectionmentioning

confidence: 99%

Indoor Infrastructure Maintenance Framework Using Networked Sensors, Robots, and Augmented Reality Human Interface

Fath,

Hanna,

Liu

et al. 2024

Future Internet

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Emergent behaviors of buckling-driven elasto-active structures

Xi,

Marzin,

Huang

et al. 2024

Proc. Natl. Acad. Sci. U.S.A.

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Active systems of self-propelled agents, e.g., birds, fish, and bacteria, can organize their collective motion into myriad autonomous behaviors. Ubiquitous in nature and across length scales, such phenomena are also amenable to artificial settings, e.g., where brainless self-propelled robots orchestrate their movements into spatial-temporal patterns via the application of external cues or when confined within flexible boundaries. Like their natural counterparts, these approaches typically require many units to initiate collective motion, so controlling the ensuing dynamics is challenging. Here, we demonstrate a simple mechanism that leverages nonlinear elasticity to tame near-diffusive motile particles in forming structures capable of directed motion and other emergent behaviors. Our elasto-active system comprises two centimeter-sized self-propelled microbots connected with elastic beams. These microbots exert forces that suffice to buckle the beam and set the structure in motion. We first rationalize the physics of the interaction between the beam and the microbots. Then we use reduced-order models to predict the interactions of our elasto-active structures with boundaries, e.g., walls and constrictions, and demonstrate how they can exhibit remarkable emergent behaviors such as maze navigation. These findings demonstrate that allowing and understanding changes in body morphology can enhance the capabilities of active matter systems and enable the design of robotic materials capable of space exploration, adaptation, and complex interactions with their surrounding environment.

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