Modular closed-loop control has been demonstrated to be a very promising way to treat diabetes disease: PC-based systems running this type of algorithm have been successfully tested in clinical trials [1]. In order to allow the introduction of this kind of approach for the control of diabetes in the so called “Artificial Pancreas” wearable devices, an embedded implementation of the algorithm must respect specific and strict constraints in terms of computational power and memory footprint. In this paper we introduce the architecture of an embedded solution for closed-loop control of diabetes. In addition, we provide details about the implementation, memory requirements and needed computational power. We also validate the embedded algorithm via in silico pre-clinical trials demonstrating the feasibility of using modular closed-loop control of diabetes in a wearable system.

Concept drift detection methods are crucial components of many online learning approaches. Accurate drift detections allow prompt reaction to drifts and help to maintain high performance of online models over time. Although many methods have been proposed, no attention has been given to data streams with imbalanced class distributions, which commonly exist in real-world applications, such as fault diagnosis of control systems and intrusion detection in computer networks. This paper studies the concept drift problem for online class imbalance learning. We look into the impact of concept drift on single-class performance of online models based on three types of classifiers, under seven different scenarios with the presence of class imbalance. The analysis reveals that detecting drift in imbalanced data streams is a more difficult task than in balanced ones. Minority-class recall suffers from a significant drop after the drift involving the minority class. Overall accuracy is not suitable for drift detection. Based on the findings, we propose a new detection method DDM-OCI derived from the existing method DDM. DDM-OCI monitors minority-class recall online to capture the drift. The results show a quick response of the online model working with DDM-OCI to the new concept.

A communication system for robotics application should allow fast exchange of real-time data for low level robot control and large amount of data for high level algorithms like intelligent vision or map building. Ethernet seems to be the perfect candidate for the low level communication interface because of its large diffusion, high bandwidth and low cost. This paper describes a real-time communication protocol for interconnecting robotic smart devices. Some comparison with existing standard are also presented.

This paper describes the architecture of a UAV designed to study the composition of gas inside volcanic plumes. The main aim of the system is that of flying inside the plume (volcanic cloud) to directly analyze the concentration of the main components of the fumes. The system must be capable of flying autonomously at up to 4000m altitude with a payload of 5Kg using electric motors, to avoid contaminations with the gas sampling system, at a cruise speed of 40km/h. The complete architecture is presented together with the HMI: this can be used both for path planning and for navigation.

In this paper a traction control algorithm for mobile robots in outdoor environment is presented. The algorithm using a wheel slip estimator optimizes the traction in sandy terrains like those found near some volcanic vents. Some comparative tests with a PI control using a simulator show the adherence improvements. In order to analyze the algorithm efficiency in real conditions, several comparative tests have been executed on the P6W robot: a small robot with six independently actuated wheels.

This work describes an algorithm for the localization and self calibration of a mobile robot. Data from a DGPS and two optical encoders are fused by an extended Kalman filter (EKF) in order to calculate the absolute position of the robot and to estimate the values of the parameters used by the odometry (wheels radii and wheelbase). The presented self calibrating EKF (EKF/sub SC/) has been tested both on a simulator and on the mobile robot "Robovolc": a six independently actuated wheels robot for volcano exploration. The EKF/sub SC/ does not need a separate phase for the parameter identification. Moreover the odometry parameters of the Robovolc robot have been identified both with the UMBmark and with the EKF/sub SC/; some comparative tests show an improvement of the estimated trajectories.

A control strategy for a two wheels planar robot that distributes the motor torques to optimize the traction is presented. A comparison between this strategy and PI control is described using the tests results obtained through a dynamic simulator. This work also shows how the proposed control strategy improves the traction in most situations with respect to a simple PI control, but when the adherence is lost the PI control is more reliable. Finally, this work examines the problems of this strategy and suggests how to solve them.