Toto Indriyanto
Papers
1
Total Citations
4
H-Index
1
About
Toto Indriyanto is a researcher specializing in robotics and control systems, with a particular focus on visual servoing and camera platform stabilization. His work centers on developing advanced control strategies for robotic vision systems, notably through the application of Linear Quadratic Gaussian (LQG) methods to yaw-pitch camera platforms. Indriyanto’s key contribution lies in enhancing the precision and robustness of visual servo control—the process of using visual feedback to guide robotic movement. His research addresses the critical challenge of maintaining a moving object within the camera’s field of view, specifically centered on the image plane, which is essential for applications in autonomous navigation, surveillance, and industrial automation. While his most-cited paper, "Visual servo strategies using Linear Quadratic Gaussian (LQG) for Yaw-Pitch camera platform" (2018), has garnered 4 citations, it represents foundational work in optimizing 2DOF platform performance under dynamic conditions. Indriyanto’s achievements demonstrate a commitment to improving real-time visual tracking and control, offering practical solutions for high-performance robotic systems. His research continues to influence the development of more reliable and efficient visual servo architectures in robotics.
Research Focus
Key Achievements
Top Papers
- 1