Saravana Perumaal Subramanian

National Institute of Technical Teachers’ Training and Research, National Institute of Technical Teachers Training and Research

Papers

Total Citations

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About

Saravana Perumaal Subramanian is a leading researcher at the intersection of robotics, automation, and intelligent manufacturing. His primary contributions lie in developing advanced algorithms for robotic mobile fulfillment systems (RMFS) and vision-assisted robotic assembly. Subramanian’s most impactful work, “Simultaneous allocation and sequencing of orders for robotic mobile fulfillment system using reinforcement learning algorithm” (2023, 22 citations), pioneers the use of reinforcement learning to optimize order allocation and sequencing in warehouse automation, significantly improving efficiency in dynamic environments. Earlier, his “GA‐based camera calibration for vision‐assisted robotic assembly system” (2016, 14 citations) advanced visual servoing by employing genetic algorithms for precise camera-robot calibration, enabling accurate manipulator positioning despite sensor noise. This work laid the foundation for robust vision-guided assembly. Subramanian also addressed uncertainty in pose estimation with “Image Uncertainty-Based Absolute Camera Pose Estimation with Fibonacci Outlier Elimination” (2019), introducing a novel method for eliminating outliers. His recent “Congestion-Aware Path Planning for Multiple Shelf-Carrying Mobile Robots in Robotic Mobile Fulfillment System” (2025) tackles real-time traffic management for multi-robot systems. With a growing citation record, Subramanian’s research directly impacts logistics, smart manufacturing, and autonomous robotics, making him a key figure in the evolution of intelligent automation systems.