Kaylash Chaudhary
Papers
8
Total Citations
49
H-Index
5
About
Kaylash Chaudhary is a researcher at the forefront of computational robotics and optimization, specializing in robot path planning, motion control, and swarm intelligence algorithms. His work bridges classical and heuristic methods, with a particular focus on enhancing the Firefly Algorithm (FA) for complex combinatorial problems. Chaudhary introduced the novel "Preference-Based Stepping Ahead Firefly Algorithm," which he has successfully applied to both the Single Depot Multiple Travelling Salesman Problem and the Uncapacitated Examination Timetabling Problem, demonstrating the versatility of swarm-based optimization beyond robotics. His highly cited 2020 comparative study on classical versus heuristic path planning approaches (12 citations) established a foundational benchmark in the field. Chaudhary's hybrid ACO-Kinematic model (2022, 7 citations) and his systematic review of robot path planning and motion control (2024) further underscore his impact, offering practical frameworks for smoother, safer autonomous navigation. With recent work integrating neural networks for obstacle avoidance and kinematic equations for motion control, Chaudhary continues to shape intelligent, real-world robotic systems, making his research essential reading for students and engineers advancing autonomous technologies.
Research Focus
Key Achievements
Top Papers
- 1A Face-off - Classical and Heuristic-based Path Planning Approaches12 citations · 2020
- 2
- 3ACO-Kinematic: a hybrid first off the starting block7 citations · 2022
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- 6Robot Path Planning and Motion Control: A Systematic Review4 citations · 2024
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