Papers
3
Total Citations
9
H-Index
2
About
Furizal Furizal is a rising researcher at the intersection of robotics, artificial intelligence, and control systems, whose work focuses on enabling intelligent, autonomous movement and perception in mobile robots. His most impactful contributions center on three key areas: object detection for autonomous robots, collision classification using neural networks, and precision motion control. In his highly cited work on ball detection for wheeled soccer robots, Furizal applied the MobileNetV2 SSD method to achieve real-time object recognition, demonstrating how lightweight AI models can be deployed on resource-constrained robotic platforms. His research on pattern recognition neural networks (PRNN) for collision classification using force sensor signals provides a robust method for robots to detect and respond to physical interactions, enhancing safety in dynamic environments. Additionally, his development of a PID controller for four-wheeled omnidirectional robots, integrating MPU and rotary encoder sensors, enables holonomic movement with high precision. With over 9 citations across his top papers, Furizal’s work is gaining traction for its practical applications in autonomous robotics, particularly in competitive and industrial settings. His achievements highlight a commitment to bridging AI and control theory for real-world robotic systems.
Research Focus
Key Achievements
Top Papers
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