Mehmet Tekerek
Papers
5
Total Citations
25
H-Index
3
About
Mehmet Tekerek is a robotics researcher whose work spans human-robot interaction, autonomous mobile robotics, and educational applications of robotic systems. His contributions have advanced both the theoretical and practical dimensions of robotics, with a particular focus on making robots more capable of operating intelligently in real-world environments and interacting naturally with humans. Tekerek's early work on robotic education, his most-cited paper with 12 citations, demonstrated a commitment to bridging theoretical robotics knowledge with hands-on learning experiences for students. His research has since evolved toward complex autonomous systems, including pioneering work on Simultaneous Localization and Mapping (SLAM) using ROS and Gazebo, where he systematically examined how geometric objects influence navigation performance. His investigations into complete coverage planning using clustering methods and reinforcement learning-based local path planning reflect a sophisticated understanding of mobile robot autonomy challenges. More recently, Tekerek has explored social humanoid robots, addressing the critical challenge of seamless human-robot integration in everyday environments. Across his body of work, accumulating over 25 citations, he consistently bridges fundamental robotics research with practical applications, making his scholarship valuable to both students entering the field and experienced researchers seeking innovative approaches to autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
- 1A human robot interaction application for robotic education12 citations · 2009
- 2
- 3
- 4Human Robot Interaction with Social Humanoid Robots2 citations · 2024
- 5Reinforcement learning based local path planning for mobile robot2 citations · 2023
Key Collaborators
Related papers
- Reinforcement learning based local path planning for mobile robot
- Reinforcement learning based local path planning for mobile robot
- Mapping and Localization of Autonomous Mobile Robots in Simulated Indoor Environments
- Map Making in Social Indoor Environment Through Robot Navigation Using Active SLAM
- Mobile robot path planning for complex dynamic environments
Researchers in this area
Labs working in this area
If you're exploring the applied side
Commercial systems in adjacent areas you may find useful as a starting point. This is not a claim that the research here is used in them — academia and industry often move on separate tracks.
- Related commercial systems
Alphasense AutonomySevensense · Agv Amr
MagicBotMagicLab · Humanoid
Go1Unitree
MenteeBotHumanoidHub · Humanoid
CARLYIPLUSMOBOT
EZ Map ProUbiquity Robotics
- Companies in this space
- Peer RoboticsUSA
Suggested by topic similarity — not advertising or endorsement.