Hugo Aprilianto

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About

Hugo Aprilianto is a robotics researcher whose work centers on intelligent navigation, sensor integration, and autonomous systems. His research spans fuzzy logic control, vision-based mapping, and real-time robot navigation, with a particular focus on enabling robots to perceive and move through complex environments. Aprilianto’s most cited work, “Penerapan Metode Fuzzy Pada Robot Beroda Menggunakan Omni-Directional Wheels” (2017), applies fuzzy logic to enhance the maneuverability of wheeled robots, a contribution that has garnered 6 citations and highlights his early impact in Indonesian national robotics competitions. In “Research of smart real-time robot navigation system” (2016), he trained a humanoid robot’s camera to detect and track colored objects, advancing distance estimation techniques for dynamic environments. His later work, “Early Model of Vision-Based Obstacle Mapping Utilizing Grid-Edge-Depth Map” (2019), introduces a novel method for indoor obstacle mapping that combines depth and positional data, marking a step forward in autonomous navigation. Aprilianto’s publications reflect a sustained commitment to practical, vision-driven robotics solutions, making his research valuable for students and engineers developing intelligent, real-world robotic systems.