Adem Tuncer
Papers
9
Total Citations
384
H-Index
6
About
Adem Tuncer is a robotics and autonomous systems researcher whose work centers on mobile robot navigation, path planning, and the application of computational intelligence to real-world robotic challenges. His most celebrated contribution, "Dynamic Path Planning of Mobile Robots with Improved Genetic Algorithm" (2012), has amassed over 326 citations, establishing him as a significant voice in evolutionary computation-based robotics. This foundational work, complemented by related studies on chromosome coding methods and hybrid FPGA implementations, demonstrated his deep expertise in applying genetic algorithms to optimize robot navigation in complex, obstacle-laden environments. Tuncer's research has progressively embraced hardware acceleration, as evidenced by his design of a genetic algorithm IP core on FPGA, bridging the gap between software intelligence and embedded systems. More recently, he has expanded into deep reinforcement learning, exploring Dueling Double Deep Q Networks for autonomous navigation in dynamic unknown environments, and integrated classical approaches like Voronoi diagrams with ant colony optimization for robust path planning. His work on low-cost single-board computer platforms further reflects a practical, accessibility-driven philosophy. Across more than a decade of research, Tuncer has consistently advanced the frontier of intelligent, adaptive mobile robotics.
Research Focus
Key Achievements
Top Papers
- 1Dynamic path planning of mobile robots with improved genetic algorithm326 citations · 2012
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- 3A Motion Planning System for Mobile Robots10 citations · 2012
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- 9Real-Time Obstacle Avoidance Based on Floor Detection for Mobile Robots2 citations · 2020