Papers
9
Total Citations
154
H-Index
6
About
Guanlan Zhang is a leading researcher in the field of robotic tactile sensing, with a focus on vision-based tactile sensors and their integration into robotic systems. Their most impactful work, "DelTact: A Vision-Based Tactile Sensor Using a Dense Color Pattern" (69 citations), introduces a novel sensor design that leverages dense color patterns to achieve high-resolution tactile feedback, significantly advancing dexterous manipulation in robotics. Zhang has also pioneered the use of optical flow for 3D contact point cloud reconstruction (24 citations) and developed a thin-format sensor using microlens arrays (MLA) to overcome the bulkiness of traditional imaging systems (17 citations). Their innovative application of tactile sensing extends to legged robotics, where they demonstrated that a single-legged robot can be stabilized using only tactile feedback from its foot (15 citations). Additionally, Zhang contributed to soft robotics for rehabilitation, designing a pneumatic muscle-based robot for elbow rehabilitation (13 citations). With over 150 total citations and a recent breakthrough in combining near-field 3D visual and tactile sensing via a compound-eye imaging system (CompdVision, 2024), Zhang’s work is shaping the future of compact, multi-modal sensing for adaptive human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1DelTact: A Vision-Based Tactile Sensor Using a Dense Color Pattern69 citations · 2022
- 23D Contact Point Cloud Reconstruction From Vision-Based Tactile Flow24 citations · 2022
- 3A Thin Format Vision-Based Tactile Sensor With a Microlens Array (MLA)17 citations · 2022
- 4A Tactile Sensing Foot for Single Robot Leg Stabilization15 citations · 2021
- 5Design of a Soft Robot Using Pneumatic Muscles for Elbow Rehabilitation13 citations · 2018
- 6
- 7
- 8A Tactile Sensing Foot for Single Robot Leg Stabilization2 citations · 2021
- 9A Thin Format Vision-Based Tactile Sensor with A Micro Lens Array (MLA)2 citations · 2022