Search

In this contribution, a new aspect in robotic basic applications is approached. The problem regards the human-machine modeling for remote ultrasound scan equipments. Even if robotic systems for ultrasound scan applications with remote operations have been widely studied, in this research the remote force feedback control has been investigated. This means that the human operator does receive as input the correct force perception that is transmitted by the remote ultrasound scan equipment in analyzing the body of the patient. Two principal aspects have been investigated. The first one is to consider an artificial body model that will receive the control signals from the remote equipment. The second one is to study the suitable feedback control law that try to compensate both the uncertainty between the artificial body and the patient body also taking into account the transmission delay. Therefore the task is to give to the operator the real perception also taking into account of the force effect, thus improving the experience of the remote operation in performing ultrasound scan procedures.

Plasma physics is an example of research field where many measurements carried out at very specific working conditions need to be collected and processed. By looking at the properties of these data, it can be possible to explore their hidden features in order to solve challenging problems that usually require high computational efforts, such as the tomographic reconstruction. In this paper, preliminary but nontrivial analyses of flux measurements produced in a Tokamak machine are shown and discussed, with the aim of introducing an application of some algorithms for feature selection to detect hidden, relevant relationships within given sets of channels. All the statistical details, and therefore the feature selection procedure itself, are introduced in view of further deepenings, such as the aforementioned problem of tomographically reconstructing plasma profiles from flux measurements or modelling the system in terms of its input-output relationship.

This manuscript aims at showing the effects of feature selection and manifold reduction methods in dealing with the wall-following problem in mobile robotics, a well-known nonlinearly separable classification problem in which sensor recordings are associated to controlled motor responses. The capabilities of state manifold reduction in Echo State Networks (ESNs) through Laplacian Eigenmaps (LEs) are described in terms of noise rejection over the trained weights. Furthermore, various machine learning-based and data mining-based methodologies are applied to show the advantages of using the most informative contents drawn from the original sensor readings.