Davide Spina

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About

Davide Spina is a researcher specializing in robotics and sensor-based estimation, with a focus on improving the accuracy and reliability of industrial and mobile robotic systems. His key research areas include inertial sensing, kinematic calibration, and fault-detection for manipulators and mobile robots. Spina’s major contributions center on developing low-cost, easy-to-install sensor solutions that enhance robotic performance without relying solely on expensive primary encoders. His most-cited work, “A Joint-Angle Estimation Method for Industrial Manipulators Using Inertial Sensors” (2015, 40 citations), introduces a method that uses inertial sensors to estimate joint angles independently, enabling robust fault-detection systems. This approach is particularly valuable for industrial settings where downtime and sensor failure are critical concerns. In another notable study, “Auto-Calibration Methods of Kinematic Parameters and Magnetometer Offset for the Localization of a Tracked Mobile Robot” (2016, 13 citations), Spina presents an automatic calibration procedure using an extended Kalman filter to estimate wheel radii, wheelbase, and magnetometer offsets. This work significantly improves outdoor mobile robot localization, demonstrating Spina’s impact on practical, real-world robotics. His research is widely cited for its practical applications in automation and sensor fusion, making him a key contributor to advancing autonomous systems.