Hamed Ghasemi
Papers
1
Total Citations
8
H-Index
1
About
Hamed Ghasemi is a robotics researcher whose work focuses on human-robot interaction, autonomous systems, and computer vision, with a particular emphasis on making drone control more intuitive and accessible. His most cited paper, "Control a Drone Using Hand Movement in ROS Based on Single Shot Detector Approach" (2020, 8 citations), addresses a critical challenge in unmanned aerial vehicle (UAV) operation: the complexity of traditional control interfaces, which contribute to aviation accidents. Ghasemi proposes a novel system that leverages the Single Shot Detector (SSD) deep learning algorithm within the Robot Operating System (ROS) framework to enable real-time hand gesture recognition, allowing operators to control drones through natural hand movements. This work bridges the gap between advanced computer vision techniques and practical robotics applications, offering a safer, more user-friendly alternative to conventional joystick-based control. By integrating state-of-the-art object detection with ROS, Ghasemi’s research contributes to reducing accident rates in drone operations across sectors like agriculture, delivery, and construction. His approach exemplifies how deep learning can democratize drone technology, making it more accessible to non-expert users while enhancing operational safety.
Research Focus
Key Achievements
Top Papers
- 1