Shri Harish Manoharan
Papers
3
Total Citations
34
H-Index
3
About
Shri Harish Manoharan is a robotics researcher whose work sits at the intersection of bio-inspired design, multi-objective optimization, and intelligent control systems. His primary research areas include modular robotics, snake-like robot locomotion, formation control, and the application of machine learning to robotic systems. Manoharan’s most impactful contribution is his 2022 study on energy-efficient gait optimization for snake-like modular robots, which has garnered 24 citations. In this work, he pioneered the use of multiobjective reinforcement learning combined with a fuzzy inference system to balance competing objectives like forward velocity and power consumption—a critical challenge for robots navigating challenging terrain. He further advanced the field by developing a Mixed Compositional Pattern-Producing Network-NeuroEvolution of Augmenting Topologies (CPPN-NEAT) method for locomotion control, demonstrating innovative approaches to managing the high degrees of freedom inherent in snake robots. In formation control, his 2019 paper introduced a dynamic destination approach for Automated Guided Vehicles (AGVs), combining consensus-based control with adaptive destination updating to handle nonlinear dynamics in real-world environments. Manoharan’s work is notable for its practical focus on energy efficiency and adaptability, making significant strides toward more autonomous and versatile robotic systems capable of operating in complex, unstructured environments.
Research Focus
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Top Papers
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