Abdul Ghafar

About

Abdul Ghafar is a robotics researcher focused on advancing industrial automation through the integration of computer vision, intelligent grasping, and soft robotics. His major contributions lie in enhancing the autonomy and dexterity of robotic manipulators, particularly for pick-and-place and assembly tasks. He pioneered the use of deep learning-based object detection and localization for real-time robotic grasping with Selective Compliant Assembly Robot Arms (SCARA), demonstrating how vision-guided robots can significantly improve adaptivity on production lines. His work on slippage detection using force sensing resistors and nonlinear adaptive backstepping control for pneumatic artificial muscles addresses the critical challenge of grasping weight-varying objects with precision. Ghafar has also innovated in gripper design, developing a spline surface vacuum gripper for industrial arms and an electroadhesion pad for soft robotic gloves aimed at assisting patients with neuromuscular impairments. With over 60 citations across his most-cited papers, his research bridges the gap between robust industrial manipulation and assistive robotics, showcasing a commitment to both manufacturing efficiency and human-centered technology.

Research Focus

Key Achievements

Top Papers

1. 1
   Grasping and Positioning Tasks for Selective Compliant Articulated Robotic Arm Using Object Detection and Localization: Preliminary Results

   22 citations · 2019
2. 2
   Real-Time Robotic Grasping and Localization Using Deep Learning-Based Object Detection Technique

   20 citations · 2019
3. 3
   Slippage Detection for Grasping Force Control of Robotic Hand Using Force Sensing Resistors

   12 citations · 2019
4. 4
   Design of spline surface vacuum gripper for pick and place robotic arms

   12 citations · 2020
5. 5
   Design of electroadhesion pad for improving grasping friction of soft robotic glove

   3 citations · 2024

Key Collaborators

Contact & Links