Vahide Bulut
Papers
8
Total Citations
91
H-Index
4
About
Vahide Bulut is a leading researcher in autonomous robotics, specializing in path planning and geometric modeling for mobile robots and robotic arms. Her work focuses on developing novel algorithms that combine Bézier curves with machine learning and optimization techniques to generate smooth, collision-free trajectories. Bulut’s most influential contribution is the "Optimal path planning method based on epsilon-greedy Q-learning algorithm" (2022, 30 citations), which integrates reinforcement learning with Bézier curves to dynamically adapt to environments. She also pioneered the use of quintic trigonometric Bézier curves for path planning (2021, 24 citations) and introduced analytic geometry-based methods for dynamic environments (2023, 21 citations). Her innovative SP-search algorithm (2021) recursively optimizes shape parameters for mobile robot navigation, while her hierarchical clustering approach (2022) enhances robotic arm efficiency in industrial settings. Bulut’s recent work extends to geodesic-based Q-learning for surface paths (2024) and differential geometry of wheel-legged robots (2019), demonstrating her versatility. With over 90 total citations, her research bridges theoretical geometry and practical robotics, offering scalable solutions for autonomous navigation in complex, obstacle-rich environments.
Research Focus
Key Achievements
Top Papers
- 1Optimal path planning method based on epsilon-greedy Q-learning algorithm30 citations · 2022
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- 7Differential geometry of autonomous wheel-legged robots3 citations · 2019
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