Pranoto Hidaya Rusmin
Papers
5
Total Citations
44
H-Index
4
About
Pranoto Hidaya Rusmin is a leading researcher in robotics, computer vision, and autonomous systems, with a particular focus on real-time object tracking, mobile robot navigation, and industrial automation. His most impactful work includes the development of a deep features fusion method for Kernelized Correlation Filter (KCF)-based moving object tracking, which addresses the critical challenge of occlusion handling in real-time applications—a paper that has garnered 14 citations. Rusmin has also made significant contributions to warehouse automation, designing a pallet detection and distance estimation system that integrates YOLO with fiducial markers for autonomous forklift robots, earning 11 citations. His expertise extends to motion planning and control, where he has implemented Model Predictive Control (MPC) for trajectory tracking in skid-steering mobile robots and visual servo applications on pan-tilt camera platforms, each cited 8-9 times. More recently, his work on optimization path planning using the RRT* algorithm with advanced sampling methods demonstrates his ongoing commitment to improving autonomous navigation. With a portfolio of highly cited papers, Rusmin’s research bridges the gap between theoretical control systems and practical, real-world robotic applications, making him a notable figure in the field of intelligent robotics and automation.
Research Focus
Key Achievements
Top Papers
- 1Deep features fusion for KCF-based moving object tracking14 citations · 2023
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