Y. Mohammed

About

Y. Mohammed is a researcher specializing in mobile robotics, autonomous navigation, and machine learning, with a particular focus on localization and learning from demonstration. Their most notable contribution is the development of a robust outdoor localization system for mobile robots using 3D LiDAR data, integrating Principal Component Analysis (PCA) and K-Nearest Neighbors (KNN) algorithms. This work, published in 2021 and garnering 6 citations, addresses critical challenges in GPS-denied environments by mitigating sensor inaccuracies caused by weather and lighting conditions. Mohammed’s approach enhances the reliability of robot positioning in real-world outdoor settings, advancing the field of autonomous navigation. Additionally, their 2015 study on learning from demonstration using variational Bayesian inference (4 citations) explores probabilistic methods for robots to acquire skills by observing human actions. This work contributes to the broader domain of robot learning, enabling more intuitive human-robot interaction. Together, Mohammed’s research bridges practical localization challenges with theoretical machine learning frameworks, offering solutions that improve the autonomy and adaptability of mobile robots in complex environments.

Research Focus

Key Achievements

Top Papers

1. 1
   Outdoor Localization in Mobile Robot with 3D LiDAR Based on Principal Component Analysis and K-Nearest Neighbors Algorithm

   6 citations · 2021
2. 2
   Learning from Demonstration Using Variational Bayesian Inference

   4 citations · 2015

Key Collaborators

Contact & Links