A. Suyampulingam

Papers

Total Citations

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About

A. Suyampulingam is a researcher focused on advancing autonomous navigation and robotic path planning, particularly in complex and hazardous environments. His work centers on developing and comparing algorithms that enable unmanned aerial vehicles (UAVs) and ground robots to navigate efficiently, avoid obstacles, and save lives during disasters. His most cited paper, "Comparative evaluation of path planning algorithms in a simulated disaster environment" (2022, 6 citations), critically assesses Q-learning and SARSA reinforcement learning methods for human detection and rescue navigation. He further extends this line of inquiry by comparing search-based algorithms like Bidirectional A*, D*, and D* Lite (2024, 2 citations) for optimal path planning in partially known factory settings, and by analyzing image processing techniques for obstacle avoidance (2021, 2 citations). Through these comparative studies, Suyampulingam provides practical insights for deploying autonomous systems in real-world scenarios, from disaster response to industrial automation. His work is particularly valuable for students and engineers seeking to understand the trade-offs between different path planning strategies, offering a clear roadmap for selecting the right algorithm for specific operational constraints.