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About

Ngangbam Herojit Singh is a robotics researcher whose work is primarily focused on mobile robot navigation, autonomous path planning, and intelligent control systems. His research explores how computational intelligence techniques — including fuzzy logic, neural networks, genetic algorithms, and nature-inspired optimization methods — can enable robots to navigate safely and efficiently through both static and dynamic environments. Singh's most impactful contributions came in 2018, when he published multiple influential studies on mobile robot navigation. His development of a Mamdani Fuzzy Inference System for static environments and his neural network-based approaches for avoiding moving obstacles each garnered 64 citations, establishing him as a notable voice in intelligent robotics. His work with MLP-BP neural networks and Fuzzy-GA hybrid approaches further demonstrated his commitment to multi-method problem solving in complex navigation scenarios. More recently, Singh has advanced into swarm intelligence and nature-inspired algorithms, applying techniques such as the Spider Monkey Optimization Algorithm and Firefly Algorithm with Three Path Methodology to cluttered, real-world-like environments. His body of work reflects a consistent drive to push the boundaries of autonomous robot decision-making, making meaningful contributions to a field increasingly central to industrial automation, assistive robotics, and autonomous vehicles.