Research Portfolio of Bongsub Song, Ph.D. Researcher at ETRI specializing in Robotics, Reinforcement Learning and Field Robotics.
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Shape-Adaptive Electro-Adhesive Gripper for Grasping Arbitrary Shaped Objects
Funded by: MOTIE
Period: 2018 – 2019
Keywords: Electro-adhesion, Variable friction, Adaptive grasping
Overview
This project developed a shape-adaptive gripper based on Electro-Adhesion (EA) capable of grasping objects of arbitrary shape. By applying voltage to the EA film, electrostatic attraction generates variable friction force — enabling stable grasping regardless of object surface geometry.
Key Contributions
- Characterized EA-based grasping principle and modeled friction variation as a function of voltage, surface material, and curvature
- Designed a systematic experiment matrix covering surface material / curvature / load combinations
- Iteratively tuned design and control parameters to improve grasp stability and repeatability
Mechanism
Electro-Adhesion Principle
Gripper Structure
Experiments & Results
Grasping Force Measurement
Repeatability Evaluation
- Bongsub Song, et al., “Sensorless Control for Atypical Object Gripper,” AsiaHaptics, 2018.
- Jeongseok Kim, Bongsub Song, et al., “Design of an Electric Adhesive Gripper,” ETME, 2018.
- Jeongseok Kim, Bongsub Song, et al., “Development of a Prototype of Two-Fingered Gripper,” ICMT, 2018.
- Bongsub Song, Younghun Su, Jingon Yoon, Baek-Jin Kim*, Dongwon Yun*, “A Hybrid Gripper with Electro-adhesive Film for Variable Friction,” Sensors and Actuators A: Physical, 2024.
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Last updated: April 2026