Hamza Aydemir

About

Hamza Aydemir is an emerging researcher specializing in autonomous mobile robotics, with a particular focus on navigation, mapping, and path planning systems. His work sits at the intersection of robotics software frameworks and intelligent control algorithms, leveraging tools such as ROS (Robot Operating System) and Gazebo simulation environments to advance the capabilities of self-navigating robots. Aydemir's most notable contribution examines how geometric environmental features influence Simultaneous Localization and Mapping (SLAM) performance, a foundational challenge in enabling robots to operate in unknown spaces. Building on this, he has explored complete coverage planning using clustering methods — tackling the critical problem of ensuring autonomous robots can systematically navigate entire mapped areas efficiently. His work on reinforcement learning-based local path planning further demonstrates his commitment to developing adaptive, intelligent navigation strategies capable of responding to dynamic, real-world scenarios. Beyond technical robotics research, Aydemir has contributed to STEM education, investigating the integration of robotics into mathematics and science curricula — reflecting a broader commitment to translating technological advancements into educational practice. With a growing body of work accumulating citations across multiple publications since 2021, Aydemir represents a promising voice in the autonomous systems research community.

Research Focus

Key Achievements

Top Papers

1. 1
   Examining of the Effect of Geometric Objects on SLAM Performance Using ROS and Gazebo

   5 citations · 2021
2. 2
   Complete coverage planning with clustering method for autonomous mobile robots

   4 citations · 2023
3. 3
   Robotik ile Matematik ve Fen Entegrasyonu

   2 citations · 2023
4. 4
   Reinforcement learning based local path planning for mobile robot

   2 citations · 2023

Key Collaborators

Contact & Links