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.

Multi-robot clusters are increasingly deployed in indoor environments, where effective communication and 3D perception are critical for coordinated operations. Monocular cameras, known for their lightweight design, cost-effectiveness, and versatility, present a promising solution for these tasks. However, relying solely on monocular cameras for comprehensive perception and communication presents significant challenges. To address this, we introduce MonoLDP, a novel system that leverages monocular cameras for depth estimation, mutual pose estimation, and visible light communication in indoor environments, providing an integrated framework to overcome these limitations. MonoLDP features a two-stage network: (1) a depth estimation module that infers depth from monocular images, and (2) a depth-guided 3D object recognition network for agent-relative localization and pose estimation. We created a custom dataset to validate the accuracy of MonoLDP. On our indoor dataset, MonoLDP outperforms the baseline by 43.39% in 3D detection and 42.39% in bird's-eye view detection, with an average localization error of 0.104 m and an orientation error of 1.66 degrees. Moreover, the depth estimation network demonstrates excellent performance on the NYU v2 dataset. Additionally, the system achieves a communication rate of 1.2 Kbps with a bit error rate below 10-2at a distance of up to 4 m using LED arrays. Our code will be released at https://github.com/RavenLiang1005/MonoLDP.git.

Accurately localizing high-speed dynamic objects in 3D space with low latency is crucial for various robotic applications. Current methods face challenges due to extended exposure times and limited sensor resolution, hindering precise object detection and localization. Event cameras, known for their high temporal resolution and asynchronous nature, offer a promising solution. To leverage the potential of the event camera, we propose EventTracker, a novel framework that integrates event and depth measurements for precise and low-latency 3D localization and tracking of the high-speed dynamic object. EventTracker incorporates a collaborative object detection and tracking algorithm optimized for both event and depth data, overcoming detection and registration challenges. Additionally, a graph-instructed optimization algorithm enhances accuracy by fusing heterogeneous sensor data effectively. Experimental evaluation in dynamic environments demonstrates significant improvements in localization performance compared to baseline methods.

Event cameras excel in high-speed and high-dynamic-range scenarios but are highly sensitive to rain, which introduces significant noise while also revealing detailed rain features. This paper introduces a novel Event-based Rain-Background Decomposition Network that integrates Spiking Neural Networks (SNNs) and Convolutional Neural Networks (CNNs). By "Distilling Rain," we reconstruct a rain-free background for downstream tasks, and by "Collecting Rain," we extract the physical characteristics of rain. Experimental evaluations demonstrate the network's effectiveness in both background reconstruction and rain modeling. This work extends the capabilities of event cameras by mitigating the adverse effects of rain while also leveraging rain-induced noise to extract valuable environmental data, enhancing their utility in both challenging weather conditions and detailed environmental analysis.

In response to the challenges presented by conventional material and texture recognition methods, our research introduces a system using visible light communication (VLC) technology. This approach provides a non-contact, non-destructive, dual-functional solution, overcoming the limitations of cost, safety, and environmental adaptability associated with traditional methods. Through a comprehensive design integrating hardware and software, our system utilizes VLC for precise and efficient recognition. Extensive testing confirms its effectiveness, achieving 97.7% accuracy in material identification and 93.8% in texture detection. This study highlights VLC's potential in enhancing automated recognition systems across various applications.