Riyadh Zulkifli
Papers
2
Total Citations
7
H-Index
2
About
Riyadh Zulkifli is a robotics researcher whose work centers on wall-climbing robots, structural health monitoring, and deep learning applications for infrastructure inspection. His most significant contributions lie in developing autonomous robotic systems capable of real-time defect detection on vertical surfaces. In his highly cited 2023 paper, "Real-Time Crack Classification with Wall-Climbing Robot Using MobileNetV2," Zulkifli demonstrated how lightweight convolutional neural networks can be integrated with climbing robots to classify structural cracks on-the-fly, achieving practical deployment speeds for field inspection. This work, alongside his 2022 study on "Analysis of the Hybrid Adhesion Mechanism of the Wall Climbing Robot," has advanced the understanding of how suction and magnetic adhesion can be combined for reliable locomotion on varied building materials. Though early in his career, Zulkifli’s papers have already garnered attention for bridging computer vision with mechanical design, offering a scalable solution for aging infrastructure maintenance. His research promises to reduce human risk in hazardous inspection environments while enabling data-driven maintenance schedules, positioning him as an emerging voice in the intersection of robotics and civil engineering.
Research Focus
Key Achievements
Top Papers
- 1
- 2Analysis of the Hybrid Adhesion Mechanism of the Wall Climbing Robot3 citations · 2022