Guanlan Zhang

Xia Chen, Guanlan Zhang, Michael Yu Wang et al.

Vision-based tactile sensors have been widely studied in the robotics field for high spatial resolution and compatibility with machine learning algorithms. However, the currently employed sensor's imaging system is bulky limiting its further application. Here we present a micro lens array (MLA) based vison system to achieve a low thickness format of the sensor package with high tactile sensing performance. Multiple micromachined micro lens units cover the whole elastic touching layer and provide a stitched clear tactile image, enabling high spatial resolution with a thin thickness of 5 mm. The thermal reflow and soft lithography method ensure the uniform spherical profile and smooth surface of micro lens. Both optical and mechanical characterization demonstrated the sensor's stable imaging and excellent tactile sensing, enabling precise 3D tactile information, such as displacement mapping and force distribution with an ultra compact-thin structure.

Guanlan Zhang, Yipai Du, Hongyu Yu et al.

Tactile sensing is an essential perception for robots to complete dexterous tasks. As a promising tactile sensing technique, vision-based tactile sensors have been developed to improve robot performance in manipulation and grasping. Here we propose a new design of a vision-based tactile sensor, DelTact. The sensor uses a modular hardware architecture for compactness whilst maintaining a contact measurement of full resolution (798*586) and large area (675mm2). Moreover, it adopts an improved dense random color pattern based on the previous version to achieve high accuracy of contact deformation tracking. In particular, we optimize the color pattern generation process and select the appropriate pattern for coordinating with a dense optical flow algorithm under a real-world experimental sensory setting. The optical flow obtained from the raw image is processed to determine shape and force distribution on the contact surface. We also demonstrate the method to extract contact shape and force distribution from the raw images. Experimental results demonstrate that the sensor is capable of providing tactile measurements with low error and high frequency (40Hz).

Guanlan Zhang, Yipai Du, Yazhan Zhan et al.

Tactile sensing on human feet is crucial for motion control, however, has not been explored in robotic counterparts. This work is dedicated to endowing tactile sensing to legged robot's feet and showing that a single-legged robot can be stabilized with only tactile sensing signals from its foot. We propose a robot leg with a novel vision-based tactile sensing foot system and implement a processing algorithm to extract contact information for feedback control in stabilizing tasks. A pipeline to convert images of the foot skin into high-level contact information using a deep learning framework is presented. The leg was quantitatively evaluated in a stabilization task on a tilting surface to show that the tactile foot was able to estimate both the surface tilting angle and the foot poses. Feasibility and effectiveness of the tactile system were investigated qualitatively in comparison with conventional single-legged robotic systems using inertia measurement units (IMU). Experiments demonstrate the capability of vision-based tactile sensors in assisting legged robots to maintain stability on unknown terrains and the potential for regulating more complex motions for humanoid robots.