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About

Budi Sugandi is a researcher specializing in computer vision, robotics, and deep learning, with a particular focus on autonomous robotic systems and real-time image processing. His work sits at the intersection of artificial intelligence and practical robotics applications, most notably in the domain of robot soccer and automated control systems. Sugandi's most impactful contribution to date is his 2019 study applying YOLO-based deep neural networks for real-time football field landmark detection, which has garnered 5 citations and demonstrates his commitment to deploying state-of-the-art deep learning architectures in competitive robotics environments. Complementing this, his research on robot localization using particle filter algorithms further advances the intelligence of wheeled soccer robots by enabling accurate self-positioning during gameplay. Beyond autonomous navigation, Sugandi has made notable contributions in human-robot interaction, designing a stereo camera-based hand tracking system for robotic arm control and developing HSV color filtering techniques for position-following robots. His goal detection and opponent avoidance algorithms highlight his ability to create robust, computationally efficient solutions for real-world robotic challenges. Collectively, his body of work reflects a productive research agenda bridging theoretical machine learning with tangible engineering applications.