In the past years, algorithms for 3D shape tracking using radial functions in spherical coordinates represented with different methods have been proposed. However, we have seen that mainly measurements from the lateral surface of the target can be expected in a lot of dynamic scenarios and only few measurements from the top and bottom parts leading to an error-prone shape estimate in the top and bottom regions when using a representation in spherical coordinates. We, therefore, propose to represent the shape of the target using a radial function in cylindrical coordinates, as these only represent regions of the lateral surface, and no information from the top or bottom parts is needed. In this paper, we use a Fourier-Chebyshev double series for 3D shape representation since a mixture of Fourier and Chebyshev series is a suitable basis for expanding a radial function in cylindrical coordinates. We investigate the method in a simulated and real-world maritime scenario with a CAD model of the target boat as a reference. We have found that shape representation in cylindrical coordinates has decisive advantages compared to a shape representation in spherical coordinates and should preferably be used if no prior knowledge of the measurement distribution on the surface of the target is available.

Knowledge about human intention can be beneficial in many disciplines of robotics, such as collaborative manufacturing, prosthetics, or encountered-type haptics. Existing intention estimation approaches are either traditional and rely on handcrafted features and heuristics, or learning-based and tailored to very specific conditions. This paper attempts to combine the best of both worlds by making recurrent neural networks adaptable to different scenarios. To achieve this, the intention estimation problem is formulated as a probabilistic classification problem and two new data sets with real-world motion and eye-tracking data are presented. Based on this data, three real-time capable classifiers with different features regarding situational awareness and additional outputs are designed and evaluated against two competing approaches. The results show that two out of three classifiers lead to improved or equivalent performance compared to traditional approaches, while good generalization is maintained.

The control of brushless DC motors requires high-resolution angular position and accurate speed information. However, available sensor-based solutions only measure either the position or the speed directly, and then approximate the other numerically. In this work, a novel technique is presented to estimate both of these values simultaneously by sensing the stray magnetic field of the internal permanent magnets of the motor. However, achieving this requires the following two challenges to be addressed. First, the relationship between the magnetic field and the motor position is distorted by the rotational speed in a non-intuitive way, requiring careful modeling of these dependencies. Second, the derived model needs to consider that the angular position data is periodic by nature, but the magnetic field data and the angular speed data are linear (i.e., non-periodic). To achieve this, we introduce two different multidimensional regression models based on the Fourier series. Both models are first trained offline using reference data, and then used as a measurement function in a nonlinear estimator such as the EKF for online estimation. Evaluations show that both models outperform state-of-the-art techniques.