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About

Aris Nasuha is a robotics researcher whose work spans intelligent control systems, autonomous navigation, and human-robot interaction. His most cited paper, "Face Tracking for Flying Robot Quadcopter based on Haar Cascade Classifier and PID Controller" (2021, 22 citations), demonstrates a practical integration of computer vision and control theory, enabling a quadcopter to detect and follow a human face in real time. This work highlights his ability to bridge perception and actuation for aerial robotics. Nasuha also addresses fundamental challenges in mobile robot navigation with his "Vortex Artificial Potential Field for Mobile Robot Path Planning" (2022, 7 citations), proposing a novel scheme to escape local minima—a persistent problem in potential field methods. In the domain of precision motion control, his study on "Synchronization of Dual Servo Motor Using CMAC Neural Network-based Lugre Friction Model" (2021, 4 citations) applies neural network compensation to improve synchronization accuracy, relevant to applications from electric vehicles to industrial robotics. Additionally, his simulation work on welding manipulator kinematics (2019) showcases his foundational expertise in robotic arm modeling. With a growing citation footprint, Nasuha’s research is characterized by its applied focus on real-time control, path planning, and vision-based autonomy, making meaningful contributions to the practical deployment of intelligent robotic systems.