用振动触觉传感器和螺旋机器人实现沙地高精度感知与探索
SandWorm: Event-based Visuotactile Perception with Active Vibration for Screw-Actuated Robot in Granular Media
- 通过主动振动弹性体与事件相机解耦设计，实现高质触觉成像
- 触觉传感器可分辨0.2mm纹理，石头分类准确率达98%，力估计误差仅0.15N
- 螺旋机器人在复杂沙地中达12.5mm/s速度，90%成功率完成管道清淤与探查
由于颗粒介质中粒子行为不可预测，其感知仍具挑战。本文提出SandWorm——一种仿生螺旋驱动机器人，结合蠕动运动以增强移动性能；以及SWTac——一种新型事件驱动的视觉触觉传感器，采用主动振动弹性体。事件相机通过弹簧隔离机制与振动解耦，实现对动态与静态物体的高质量触觉成像。算法层面，提出基于IMU的时序滤波器，使MSNR提升24%。系统性优化了振动参数、事件相机设置及弹性体特性。基于非对称边缘特征，采用U-Net实现接触面估计。实验验证：SWTac达到0.2 mm纹理分辨率，98%石块分类准确率，0.15 N力估计误差；SandWorm在复杂颗粒介质中实现最高12.5 mm/s的多样化运动，成功完成管道清淤与地下探测，观测成功率90%。野外实验进一步证实系统实用性。
原文摘要 · Abstract (English)
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.
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