Shayan Ghiasvand
Papers
1
Total Citations
4
H-Index
1
About
Shayan Ghiasvand is a robotics and computer vision researcher whose work centers on autonomous systems for space applications. His most notable contribution is a deep neural network (DNN)-based robotic visual servoing framework for satellite target tracking, developed to replace costly and error-prone manual tracking operations on the International Space Station (ISS). By enabling a robot to autonomously lock onto and follow a moving satellite using visual feedback, this work directly tackles a critical bottleneck in on-orbit servicing and debris management. The approach, detailed in his 2024 paper, has already garnered 4 citations, signaling early impact in the field. Ghiasvand’s research bridges the gap between deep learning and real-time robotic control, offering a scalable solution for autonomous space operations. His work is particularly relevant for students and researchers interested in the intersection of computer vision, robotics, and space technology, demonstrating how neural networks can be deployed in high-stakes, resource-constrained environments.
Research Focus
Key Achievements
Top Papers
- 1