Objetivo
Sensor drift is a major problem for inertial sensors and limits their usage in autonomous navigation applications. Inertial sensor data is integrated to find the position and drift leads to error accumulation. A common drift suppression approach is temperature calibration, but ovenized state of the art sensors still exhibit drift. Instead of using temperature as a drift indicator, I have pursued a non-conventional approach and measured on-chip stress that directly correlates with drift. The device interacts with its surroundings through the anchors and on-chip stress accurately estimates drift. I am the leading researcher in the stress compensation field, and I have recently demonstrated that MEMS gyroscope drift could be eliminated with stress compensation. My long-term stability results at 2 days of averaging are unrivaled, but the calibration algorithm is not practical. Different from temperature calibration, stress calibrating a device is difficult. I propose a sensor system that would convert my proof of concept work into a practical 0-drift sensor with self-calibration. The proposed system consists of a circular MEMS sensor with multiple (~100) distributed stress sensors and piezoelectric stress transducers, a machine learning supported analytical calibration model, a custom ASIC for superior noise, and an FPGA for system control and self-calibration. If successful, the proposed approach would improve the MEMS gyroscope stability by >100X to the levels of 10-4 10-5/h, enabling error-free, only gravity-referenced inertial navigation. Unlike GPS or camera, inertial navigation works under all weather, light, and location conditions providing a stable reference to navigation algorithms. With further miniaturization, 0-drift sensors could fit into smartphones, and reliable indoor navigation would become a reality. The compact, low-cost sensor could also disrupt the precision inertial market dominated by bulky and expensive fiber-optic and laser sensors.
Ámbito científico (EuroSciVoc)
CORDIS clasifica los proyectos con EuroSciVoc, una taxonomía plurilingüe de ámbitos científicos, mediante un proceso semiautomático basado en técnicas de procesamiento del lenguaje natural.
CORDIS clasifica los proyectos con EuroSciVoc, una taxonomía plurilingüe de ámbitos científicos, mediante un proceso semiautomático basado en técnicas de procesamiento del lenguaje natural.
- ingeniería y tecnologíaingeniería eléctrica, ingeniería electrónica, ingeniería de la informacióningeniería electrónicasensoressensores ópticos
- ciencias naturalesciencias físicasópticafísica del láser
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Palabras clave
Programa(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Régimen de financiación
HORIZON-ERC - HORIZON ERC GrantsInstitución de acogida
06800 Bilkent Ankara
Turquía