Ernesto Fabregas

A Remote Laboratory as an Innovative Educational Tool for Practicing Control Engineering Concepts

This paper presents the development, structure, implementation, and some applications of a remote laboratory for teaching automatic control concepts to engineering students. There are two applications: formation control of mobile robots and a ball-plate system. In teaching control engineering, there are two main approaches to control design: model-based control and non-model-based control. Students are given insight into: 1) for model-based control: identification of real processes (i.e., dealing with noise, choosing the sampling time, observing nonlinear effects at startup, pairing input-output variables); and 2) for non-model-based control: the advantages and disadvantages of auto-tuning techniques. The paper concludes by presenting an evaluation of these remote labs and discussing the advantages of using them as complementary tools for teaching control engineering at the Bachelors and Masters level.

A Mobile Robots Experimental Environment with Event-Based Wireless Communication

An experimental platform to communicate between a set of mobile robots through a wireless network has been developed. The mobile robots get their position through a camera which performs as sensor. The video images are processed in a PC and a Waspmote card sends the corresponding position to each robot using the ZigBee standard. A distributed control algorithm based on event-triggered communications has been designed and implemented to bring the robots into the desired formation. Each robot communicates to its neighbors only at event times. Furthermore, a simulation tool has been developed to design and perform experiments with the system. An example of usage is presented.

A Remote Laboratory for Mobile Robot Applications

Abstract This paper presents the architecture and the implementation of a remote laboratory for mobile robot applications. The implementation is based on Matlab and Easy Java Simulations (EJS). The aim of the remote laboratory is that students perform – via the Internet – experiments on a group of non-holonomic mobile robots. The robot application presented in this paper is leader-follower formation control using image processing.

Event-Based Control Strategy for Mobile Robots in Wireless Environments.

In this paper, a new event-based control strategy for mobile robots is presented. It has been designed to work in wireless environments where a centralized controller has to interchange information with the robots over an RF (radio frequency) interface. The event-based architectures have been developed for differential wheeled robots, although they can be applied to other kinds of robots in a simple way. The solution has been checked over classical navigation algorithms, like wall following and obstacle avoidance, using scenarios with a unique or multiple robots. A comparison between the proposed architectures and the classical discrete-time strategy is also carried out. The experimental results shows that the proposed solution has a higher efficiency in communication resource usage than the classical discrete-time strategy with the same accuracy.

Platform for Teaching Mobile Robotics

This paper describes the development of a motivating and innovative multi-robot formation control platform for laboratory experiments with mobile robots. The platform is composed of two components: a simulator and an environment to experiment with low cost wheeled mobile robots. The environment constitutes a ready to use test tool that provides to engineering students the opportunity to simulate and test many different formation and cooperation control strategies with a real system. Currently the platform is used in the Systems and Control Engineering Master program offered by the National University of Distance Education (UNED) and the Complutense University of Madrid (UCM) in Spain. The use of the platform exposes students to hands-on laboratory sessions, contributing to their development as engineers.

Virtual and remote experimentation with the Ball and Hoop system

Using Intenet-based networking technologies traditional control laboratories in engineering education can be replaced with a remote or simulated experimental session. Thus, the way of studying becomes more flexible: the assistance to the laboratories is minimized. Accessing to the application students can make experiments and obtain results with a real plant from different localizations far from the university. This paper presents a complete virtual and remote control laboratory for experimentation of an oscillatory system: the Ball and Hoop. Using this application students can understand in a practical way important topics such as non-minimum phase behaviors, zeros transmission of the system, resonance, or to demonstrate control of oscillatory systems. The client-side of the virtual laboratory has been developed using the programming support provided by Easy Java Simulations (Ejs). The server-side has been developed using Labview and a data acquisition card.

An interactive simulator for networked mobile robots

This article presents an interactive simulator for formation control of wireless networked robots. The formation control is achieved thanks to the application of consensus algorithms of multi-agent systems which are executed in a distributed manner. Each vehicle sends to its neighbors the state, and they use this state to compute their control law. The agents communicate through a packet-based network. Different scheduling schemes are provided: time-based and event-based, with different event trigger functions. The user can configure the topology of the network, define delays in the communication links, packet-dropout rates or induce error in the transmission of the packets. The user can test the system in a large number of possible scenarios and interact with the simulation by just drag-and-drop actions. Four cases of study are provided to show some of the capabilities of the simulation tool.

A Neural Network Approach for Building An Obstacle Detection Model by Fusion of Proximity Sensors Data

Proximity sensors are broadly used in mobile robots for obstacle detection. The traditional calibration process of this kind of sensor could be a time-consuming task because it is usually done by identification in a manual and repetitive way. The resulting obstacles detection models are usually nonlinear functions that can be different for each proximity sensor attached to the robot. In addition, the model is highly dependent on the type of sensor (e.g., ultrasonic or infrared), on changes in light intensity, and on the properties of the obstacle such as shape, colour, and surface texture, among others. That is why in some situations it could be useful to gather all the measurements provided by different kinds of sensor in order to build a unique model that estimates the distances to the obstacles around the robot. This paper presents a novel approach to get an obstacles detection model based on the fusion of sensors data and automatic calibration by using artificial neural networks.

Improving the 3D Positioning for Low Cost Mobile Robots

A new algorithm to improve the 3D positioning for low cost mobile robots is presented. The core of the algorithm is based on a Finite State Machine (FSM) which estimates the 3D position and orientation of the robots, also a low pass filter and a threshold calculator are used in the system to filter and to estimate the noise in the sensors. The system sets dynamically the parameters of the algorithm and makes them independent of the noise. The algorithm has been tested with differential wheel drive robots, however it can be used with other different types of robots in a simple way. To improve the accuracy of the estimations, a new reference system based on the accelerometer of the robot is presented which reduces the accumulative error that the odometry produces.

3D Positioning Algorithm for Low Cost Mobile Robots

A new 3D positioning algorithm for low cost robots is proposed. The algorithm is based on a Finite State Machine to estimate the position and orientation of the robot. The system sets dynamically the parameters of the algorithm and makes it independent of the noise in the sensors. The algorithm has been tested for differential wheel drive robots, however it can be used with different types of robots in a simple way. To improve the accuracy of the system, a new reference system based on the accelerometer of the robot is presented which reduces the accumulative error that the odometry produces.