Didik Purnomo

Universitas Negeri Surabaya, Politeknik Elektronika Negeri Surabaya

Papers

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About

Didik Purnomo is a leading researcher in intelligent robotic systems and autonomous navigation, with a focus on integrating deep learning into real-world defense and mobile robotics applications. His pioneering work on the "Deep multilayer network for automatic targeting system of gun turret" (2017, 19 citations) introduced a novel approach to automating military vehicle operations, replacing traditional manual control with advanced neural network-based targeting—a contribution that bridges artificial intelligence with critical defense technology. In the domain of autonomous mapping, Purnomo’s influential study on "Occupancy Grid map Mapping Method on Hector SLAM Technique" (2019, 17 citations) addresses the fundamental challenge of unknown environments for differential drive mobile robots. By refining grid map occupancy methods, he enabled robots to effectively represent and navigate indoor spaces, advancing the practical deployment of SLAM (Simultaneous Localization and Mapping) systems. His work is characterized by a clear focus on solving tangible engineering problems—from battlefield automation to indoor robot exploration—making him a notable figure in applied robotics. With these highly cited contributions, Purnomo continues to shape the intersection of deep learning, autonomous systems, and real-world robotic intelligence.