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This paper discusses an innovative adaptive heterogeneous fusion algorithm based on estimation of the mean square error of all variables used in real time processing. The algorithm is designed for a fusion between derivative and absolute sensors and is explained by the fusion of the 3-axial gyroscope, 3-axial accelerometer and 3-axial magnetometer into attitude and heading estimation. Our algorithm has similar error performance in the steady state but much faster dynamic response compared to the fixed-gain fusion algorithm. In comparison with the extended Kalman filter the proposed algorithm converges faster and takes less computational time. On the other hand, Kalman filter has smaller mean square output error in a steady state but becomes unstable if the estimated state changes too rapidly. Additionally, the noisy fusion deviation can be used in the process of calibration. The paper proposes and explains a real-time calibration method based on machine learning working in the online mode during run-time. This allows compensation of sensor thermal drift right in the sensor's working environment without need of re-calibration in the laboratory.

A haptic interface is a kinaesthetic link between a human and some real or virtual environment. In this article, we discuss whether the haptic technology (virtually touching objects and feeling forces) could be effectively implemented in the industrial applications. As an example, we will examine the virtual wall which is a fundamental component of almost all virtual objects. Typically, it is based on a simple spring and damper model with constraints that allow the user to make contact with an object. Various factors lead to an unstable behaviour in a controlled system such as the virtual wall. Main causes of disturbances are the sensor (e.g. the signal resolution) and the actuator (e.g. the dynamics of the system which are not covered by the controller design). Some of these disturbance mechanisms can be excluded by mechanical design, and others are more difficult to eliminate – following two are discussed in the article: First one is the zero-order hold effect caused by sampling and the second one is the shifted synchronization of the wall threshold crossings with the sampling times. Both have unwanted effects on the sampled data within the virtual wall system.

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