Nut Kaewnark
Papers
1
Total Citations
20
H-Index
1
About
Nut Kaewnark’s research centers on autonomous underwater vehicles (AUVs) and embedded robotics, with a particular emphasis on low-cost, accessible implementations. Their most cited work, “Implementation of visual odometry estimation for underwater robot on ROS by using RaspberryPi 2” (2016, 20 citations), demonstrates a key contribution: integrating visual odometry—a critical capability for navigation in hazardous underwater environments—onto a compact Raspberry Pi 2 platform using the Robot Operating System (ROS). This approach significantly reduces hardware cost and complexity, making advanced AUV navigation more attainable for research and education. By proving that sophisticated estimation algorithms can run on affordable single-board computers, Kaewnark’s work has influenced the development of lightweight, scalable robotic systems. The paper’s citation count reflects its practical value for researchers exploring ROS-based underwater robotics and embedded vision. Kaewnark’s focus on bridging algorithm performance with hardware constraints marks an important step toward democratizing underwater exploration technology.
Research Focus
Key Achievements
Top Papers
- 1