Alongkorn Pirayawaraporn
Papers
2
Total Citations
5
H-Index
2
About
Alongkorn Pirayawaraporn is a robotics researcher whose work centers on human-robot interaction and mobile robot perception. His key contributions lie in developing computational models that allow robots to better understand and respond to human behavior, as well as improving the precision of robot navigation systems. In his 2019 paper "Vision-Based Attentiveness Determination Using Scalable HMM Based on Relevance Theory," Pirayawaraporn proposed a novel approach for robots to identify human attentiveness during interaction, leveraging hidden Markov models grounded in relevance theory—a framework that has earned 3 citations for its innovative application of cognitive principles to robotics. His second major work, "Simultaneous Calibration of Odometry and Head-Eye Parameters for Mobile Robots with a Pan-Tilt Camera" (2019, 2 citations), addresses a critical challenge in robot navigation: the simultaneous estimation of wheel parameters and camera pose for enhanced accuracy. This dual-calibration method is essential for precise operation in real-world environments. Though his citation counts are modest, Pirayawaraporn's research demonstrates a thoughtful integration of cognitive science and engineering, laying groundwork for more intuitive and reliable robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2