Papers
36
Total Citations
423
H-Index
11
About
Velappa Ganapathy is a prominent robotics researcher whose career has been dedicated to advancing autonomous mobile robot navigation, path planning, and intelligent control systems. With over two decades of contributions to the field, his work spans a rich spectrum of methodologies — from fuzzy logic and neural networks to genetic algorithms and artificial potential fields — all aimed at enabling robots to navigate complex, dynamic, and unknown environments with greater efficiency and safety. Among his most influential contributions is his development of the Enhanced Artificial Potential Field (E-APF) method for obstacle avoidance and trajectory planning, which has garnered 67 citations since its 2018 publication. His 2016 optimization of the A-Star algorithm (48 citations) and earlier work on genetic algorithm-based dynamic path planning (44 citations) further demonstrate his sustained leadership in intelligent navigation research. Early foundational papers on fuzzy-neural controllers and Q-learning approaches, published in 2009, helped establish robust frameworks for autonomous decision-making in robots. Beyond navigation, Ganapathy has extended his expertise into medical robotics, contributing to stroke rehabilitation systems for upper limb recovery. His cumulative body of work, exceeding 280 citations, makes him a valuable reference for students and researchers exploring the intersection of artificial intelligence and autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
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- 2Trajectory Planning of a Mobile Robot using Enhanced A-Star Algorithm48 citations · 2016
- 3Dynamic path planning algorithm in mobile robot navigation44 citations · 2011
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- 5Neural Q-Learning controller for mobile robot23 citations · 2009
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