Jony Arif Ricardo Silitonga
Papers
1
Total Citations
3
H-Index
1
About
Jony Arif Ricardo Silitonga is a robotics researcher whose work centers on computer vision, humanoid robot coordination, and embedded AI systems. His most-cited paper, "Tiny-YOLO distance measurement and object detection coordination system for the BarelangFC robot" (2023, 3 citations), presents a lightweight deep learning approach for real-time object detection and distance estimation, enabling a humanoid robot to recognize opponents and coordinate ball passing during the Kontes Robot Indonesia (KRI) competition. This contribution addresses a critical challenge in multi-robot systems: achieving effective team coordination under computational constraints. Silitonga’s research demonstrates how optimized neural networks like Tiny-YOLO can be deployed on resource-limited robotic platforms, bridging the gap between advanced AI and practical competition robotics. His work not only advances the field of autonomous robot coordination but also provides a scalable framework for real-time perception in humanoid robots. For students and researchers interested in robotics, computer vision, or embedded AI, Silitonga’s contributions offer a compelling example of how efficient algorithms can enable complex behaviors in competitive, real-world settings.
Research Focus
Key Achievements
Top Papers
- 1