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About

Arindam Singha is a researcher specializing in autonomous mobile robotics, with a particular focus on intelligent path planning, navigation algorithms, and complete coverage strategies in grid-based environments. His work addresses one of robotics' most fundamental challenges: enabling mobile robots to navigate efficiently and safely from starting positions to target destinations while avoiding obstacles in complex, real-world environments. Singha's most notable contributions center on applying neural network-based approaches to robot navigation. His 2017 paper on local and target weighted neural networks for mobile robot navigation, which has garnered 6 citations, laid important groundwork for intelligent collision-free path planning with applications spanning smart city traffic management, military operations, and warehouse automation. Building on this foundation, he extended his research into complete coverage algorithms, exploring how single and multiple robots can systematically cover entire grid environments — work with significant implications for warehouse automation and area-sweeping tasks. More recently, Singha has explored bio-inspired optimization techniques, contributing a 2022 study on Adaptive Particle Swarm Optimization for robot path planning. Though his research career appears to be in its earlier stages, his consistent output across navigation strategies and multi-robot coordination positions him as an emerging voice in the autonomous robotics and intelligent systems community.