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.

翻译：颗粒介质中的感知仍面临挑战，这源于其不可预测的颗粒动力学。为应对这一挑战，我们提出了沙虫——一种通过蠕动运动增强移动能力的仿生螺旋驱动机器人，以及SWTac——一种配备主动振动弹性体的新型事件视觉触觉传感器。事件相机通过弹簧隔离机制与振动机械解耦，从而能够对动态和静态物体进行高质量的触觉成像。在算法设计方面，我们提出了一种IMU引导的时间滤波器以增强成像一致性，将MSNR提高了24%。此外，我们系统性地优化了SWTac的振动参数、事件相机设置和弹性体特性。受非对称边缘特征的启发，我们还通过U-Net实现了接触表面估计。实验验证表明，SWTac具有0.2毫米的纹理分辨率、98%的石块分类准确率和0.15 N的力估计误差；而沙虫在挑战性地形中展现出多功能的移动能力（最高达12.5毫米/秒），并在复杂的颗粒介质中成功执行了管道疏浚和地下勘探任务（观测到90%的成功率）。现场实验进一步证实了该系统的实际性能。