Cagatay Sariman
Papers
2
Total Citations
19
H-Index
2
About
Cagatay Sariman is a robotics researcher whose work focuses on the intersection of evolutionary algorithms, reinforcement learning, and autonomous systems for constrained environments. His primary research areas include underwater robotics, pipe inspection systems, and the application of the Robot Operating System (ROS) for morphological adaptation. In his most-cited work, "Morphological evolution for pipe inspection using Robot Operating System (ROS)" (2020, 16 citations), Sariman addresses the challenge of deploying miniaturized sensor agents in fluid-filled, confined spaces for industrial monitoring. He further advances the field with "UR-EARL: A framework for designing underwater robots using evolutionary algorithm-driven reinforcement learning" (2025, 3 citations), which introduces a novel framework that simultaneously optimizes an AUV’s physical body shape and its control system—a dual challenge long considered a bottleneck in autonomous underwater vehicle design. By integrating evolutionary algorithms with reinforcement learning, Sariman’s work paves the way for more adaptive and efficient robots capable of navigating complex, real-world environments. His contributions are particularly valuable for students and researchers interested in bio-inspired robotics, autonomous navigation, and the practical deployment of intelligent agents in industrial and marine settings.
Research Focus
Key Achievements
Top Papers
- 1
- 2