Highly accurate measured values even with movement
Inclination sensors are among the standard components in mobile machines. Until now, sensor manufacturers, as well as users, have often been confronted with a challenge: The inclination measurement of these sensors based on conventional accelerometers, which cannot distinguish between the acceleration due to gravity and other external accelerations. GEMAC from Chemnitz is now responding to the increasing demand for acceleration-compensated sensors with its new IMU+ inertial measurement system.
Three years of development work on the algorithm
The new system enables 6‑axis motion detection on the mobile machine, based on raw data acquisition for acceleration (3‑axis) and rotation rate (3‑axis). High-precision data processing is performed in the sensor using a sophisticated sensor fusion algorithm. Integrated sensor fusion filters support the user in orientation calculation by suppressing externally acting accelerations.
Rico Gräßler, an engineer at GEMAC and team leader sensor development at IMU+:
“From the very beginning, it was the requirement in development to achieve a new dimension of accuracy by combining an absolute measurement with a specific evaluation algorithm. Three years of development time invested in the self-designed algorithm alone. The new IMU+ system offers the decisive advantage over the previous IMU systems that, in addition to the raw data for acceleration and rotation rate, it can also output internally calculated values such as tilt or rotation angle in different axes. So in the future, it will be able to detect deviations in the defined machine system even more quickly, enabling the user to take targeted action”.
By combining and calculating these six measured values, one measuring system can now be integrated instead of several. The user saves space and considerably reduces the integration effort. Since time-critical calculations now performed in the sensor system, the user saves additional time. Also, the new IMU+ offers significantly improved static and dynamic accuracy compared to the existing sensors.