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About

Mayuko Takano is a robotics researcher whose work centers on acoustic sensing, robot navigation, and intelligent system design. Her most influential contribution is a method for classifying object surfaces by calculating the acoustic transfer function between echoes from a reference plane and a test object, demonstrating that phase characteristics enable shape classification—a foundational approach in non-contact surface recognition (20 citations). She also developed a system for measuring the 3-D position and orientation of a robot hand using ultrasonic triangulation with spark-discharge transmitters, advancing precise spatial tracking in manufacturing environments (8 citations). In mobile robotics, Takano proposed a navigation control method that fuses internal and external sensor data—including ultrasonic sensors—to overcome low sampling rates and achieve stable autonomous land vehicle guidance (8 citations). Additionally, she applied fuzzy reasoning to robot manipulator type selection, enabling task-specific optimization of workspace, accuracy, speed, and payload capacity (5 citations). Her work bridges acoustic physics and practical robotics, offering elegant solutions for sensing and decision-making in automated systems.