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Applied Science requires testbeds to carry out experiments and validate in practice the results of the application of the methods. This article presents a low-cost (35–40 euros) educational mobile robot, based on Android and Arduino, integrated with Robot Operating System (ROS), together with its application for learning and teaching in the domain of intelligent automatic control, computer vision and Machine Learning. Specifically, the practical application to visual path tracking integrated with a Fuzzy Collision Risk system, that avoids collision with obstacles ahead, is shown. Likewise, a Wi-Fi positioning system is presented, which allows identifying in which room the robot is located, based on self-collected data and Machine Learning.

This article presents a method based on linear matrix inequalities (LMIs) for designing a modular feedback control law, whose synthesis guarantees the system stability, while switching to different network topologies. Such stability is achieved by means of a common Lyapunov function to all network admissible configurations. Several mechanisms to relieve the computational burden of this methodology in large-scale systems are also presented. To assess its applicability, the modular controller is tested on a real case study, namely the Barcelona drinking water network (DWN), and its performance is compared with that of other control strategies, showing the effectiveness of the proposed approach.

The main objective of the system presented in this paper is to provide surveyors and engineers with a new photogrammetry device that can be easily integrated with surveying total stations and a global navigation satellite system (GNSS) infrastructure at a construction site, taking advantage of their accuracy and overcoming limitations of aerial vehicles with respect to weight, autonomy and skilled operator requirements in aerial photogrammetry. The system moves between two mounting points, in a blondin ropeway configuration, at the construction site, taking pictures and recording the data of the position and the orientation along the cable path. A cascaded extended Kalman filter is used to integrate measurements from the on-board inertial measurement unit (IMU), a GPS and a GNSS. Experimental results taken in a construction site show the system performance, including the validation of the position estimation, with a robotic surveying total station, or the creation of a digital surface model (DSM), using the emergent structure from motion (SfM) techniques and open software. The georeferencing of the DSM is performed based on estimated camera position or using ground control points (GCPs).

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