Goktug Hambarci
Papers
1
Total Citations
16
H-Index
1
About
Goktug Hambarci is a researcher at the forefront of intelligent robotics and precision localization systems. His work primarily focuses on enhancing the autonomy and reliability of mobile robots in complex indoor environments, with a particular emphasis on Mecanum-wheeled platforms. Hambarci’s most cited contribution introduces a novel hybrid approach that integrates Monte Carlo methods with Latin hypercube sampling and machine learning to dramatically improve the measurement accuracy of indoor positioning systems. This work, published in 2022 and garnering 16 citations, addresses a critical bottleneck in mobile robotics: the trade-off between computational efficiency and localization precision. By developing a more robust algorithm for sensor fusion and probabilistic estimation, Hambarci has provided a practical solution for applications ranging from warehouse automation to assistive robotics. His research is notable for bridging theoretical sampling techniques with real-world robotic deployment, offering a scalable framework that reduces error margins without sacrificing real-time performance. For students and researchers in robotics, Hambarci’s work exemplifies how careful statistical design can unlock new levels of accuracy in autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1