Perception in granular media remains challenging due to unpredictable particle dynamics. To address this challenge, we present SandWorm, a biomimetic screw-actuated robot augmented by peristaltic motion to enhance locomotion, and SWTac, a novel event-based visuotactile sensor with an actively vibrated elastomer. The event camera is mechanically decoupled from vibrations by a spring isolation mechanism, enabling high quality tactile imaging of both dynamic and stationary objects. For algorithm design, we propose an IMU-guided temporal filter to enhance imaging consistency, improving MSNR by 24%. Moreover, we systematically optimize SWTac with vibration parameters, event camera settings and elastomer properties. Motivated by asymmetric edge features, we also implement contact surface estimation by U-Net. Experimental validation demonstrates SWTac's 0.2 mm texture resolution, 98% stone classification accuracy, and 0.15 N force estimation error, while SandWorm demonstrates versatile locomotion (up to 12.5 mm/s) in challenging terrains, successfully executes pipeline dredging and subsurface exploration in complex granular media (observed 90%success rate). Field experiments further confirm the system's practical performance.

Visuotactile sensors provide high-resolution tactile information but are incapable of perceiving the material features of objects. We present UltraTac, an integrated sensor that combines visuotactile imaging with ultrasound sensing through a coaxial optoacoustic architecture. The design shares structural components and achieves consistent sensing regions for both modalities. Additionally, we incorporate acoustic matching into the traditional visuotactile sensor structure, enabling the integration of the ultrasound sensing modality without compromising visuotactile performance. Through tactile feedback, we can dynamically adjust the operating state of the ultrasound module to achieve more flexible functional coordination. Systematic experiments demonstrate three key capabilities: proximity sensing in the 3–8 cm range (R2= 0.99), material classification (average accuracy: 99.20%), and texture-material dual-mode object recognition achieves 92.11% accuracy on a 15-class task. Finally, we integrate the sensor into a robotic manipulation system to concurrently detect container surface patterns and internal content, which verifies its promising potential for advanced human-machine interaction and precise robotic manipulation.

The demand for accurate and real-time indoor localization and human sensing is rising with the development of smart environments, with applications in security and smart homes. Effective systems enhance safety, energy efficiency, and user experience by leveraging existing infrastructure, reducing deployment costs, and integrating seamlessly. Traditional methods rely on dedicated hardware, while communication or lighting infrastructure can provide dual-purpose solutions. This research focuses on Visible Light Communication (VLC) technology, which uses visible light for data transmission. Our VLC system utilizes existing lighting infrastructure to transmit data, providing localization and human sensing functionalities. The system design strategically incorporates specific VLC transmitters and receivers to enhance sensing performance. The collected data is processed using advanced algorithms and machine learning models, ensuring robust, real-time, and cost-effective localization with an accuracy of 96.6% and human activity recognition with an accuracy of 98.3%. This multi-functionality system demonstrates the potential for VLC in healthcare monitoring and home automation applications.