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EDUROV is an educational underwater robot proposal from the researchers of the Oceanic Platform of Canary Islands (PLOCAN) and the Computer Vision and Robotics research group of the University of Girona (VICOROB), launched in January 2012 with the support of the Spanish Foundation for Science and Technology (FECyT). This program has evolved in the last decade in order to make it more sustainable, allowing the teleoperation of underwater vehicles from anywhere in the world. EDUROVs have passed through several phases, beginning with a basic electronics robot, followed by the incorporation of open-source electronic prototyping platforms and finally reaching the current state of teleoperation. Results based on 1–5 Likert scale questions show that both students and teachers consider the program useful to introduce technical and scientific concepts. It is concluded that the use of low-cost materials and tools that are easy to obtain, following education on sustainability approaches, also makes them possible for use in high schools, and science teachers can easily carry out the activity. Moreover, the possibility of remote teleoperation of underwater vehicles, together with the collaboration among groups of students in different locations that are in contact through these online tools, allows one to motivate students to work on the project from a different perspective This research was funded by different grants from Patronat Escola Politècnica Superior de la Universitat de Girona (APAU), Consell Social de la Universitat de Girona, Ayudas ACDC Generalitat de Catalunya, Fundación Ciencia y Tecnologia FECyT (FCT-16-11269 ROVINO and FCT-17-11873 ROVSTEAM projects) and Caixabank

This paper addresses the maintenance of urbanized areas in automatic form, collecting data from the streets using mobile autonomous robots. The only supervision of the system by human operator is necessary to avoid the robots be abandoned in case of malfunction. Is a team of robots to do the job in efficient manner and minimizing the time in a pedestrians zone. A prior organization task is necessary to distribute the travel for each robot, to cover all desired area without interference between robots, the k-CPP problem is analyzed to solve this situation.

This article reports the experience gathered along 6 years in developing a team-based project activity to promote engineering programs among secondary school students. The aim of the activity is to increase the interest of students for science and technology in general, but also to promote engineering skills, capabilities and values, leading to attract more secondary school students to enrolment for engineering programs. Simple theoretical concepts related to physics and engineering are illustrated through hands-on experimentation. To achieve this goal, the students build a remotely operated underwater robot in a workshop. The robot is built using low-cost materials and the students customize their own design over the different phases of the workshop. Once the activity is completed, every team understands that with teamwork, effort and a good working strategy, every problem can be overcome This work has been partially funded by the Catalan Government (Generalitat de Catalunya) through ACDC grants 2009, 2010, 2011, 2012 and 2013, the EnginyCAT program, MICINN project CTM2010-15216, EU project FP7- ICT-2009-248497 and the UdG (Social Council, VICOROB Research Group, Patronatge of Technical School and Vicerectorate of Research).

The idea of incorporating and applying interdisciplinary concepts is an important objective in electrical and computer engineering education. In this sense, mobile robot platforms can be used as educational tools to promote and integrate different curriculum subjects. In this paper, the programme and experience of an optional course called Applied Mobile Robotics is outlined. The reported content is specially focused on the experiences of applying control theory to a mobile robot platform.

Laser range finder and omnidirectional cameras are becoming a promising combination of sensors to extract rich environmental information. This information includes textured plane extraction, vanishing points, catadioptric projection of vertical and horizontal lines, or invariant image features. However, many indoor scenes do not have enough texture information to describe the environment. In these situations, vertical edges could be used instead. This study presents a sensor model that is able to extract three-dimensional position of vertical edges from a range-augmented omnidirectional vision sensor. Using the unified spherical model for central catadioptric sensors and the proposed sensor model, the vertical edges are locally projected, improving the data association for mapping and localisation. The proposed sensor model was tested using the FastSLAM algorithm to solve the simultaneous localisation and mapping problem in indoor environments. Real-world qualitative and quantitative experiments are presented to validate the proposed approach using a Pioneer-3DX mobile robot equipped with a URG-04LX laser range finder and an omnidirectional camera with parabolic mirror This work has been partially supported by the project RAIMON - Autonomous Underwater Robot for Marine Fish Farms Inspection and Monitoring (Ref. CTM2011-29691-C02-02)funded by the Spanish Ministry of Science and Innovation, the LASPAU-COLCIENCIAS grant no. 136-2008, the University of Valle contract no. 644-19-04-95, and the consolidated research group's grant no. SGR2009-00380

The strength of appearance-based mapping models for mobile robots lies in their ability to represent the environment through high-level image features and to provide human-readable information. However, developing a mapping and a localization method using these kinds of models is very challenging, especially if robots must deal with long-term mapping, localization, navigation, occlusions, and dynamic environments. In other words, the mobile robot has to deal with environmental appearance change, which modifies its representation of the environment. This paper proposes an indoor appearance-based mapping and a localization method for mobile robots based on the human memory model, which was used to build a Feature Stability Histogram (FSH) at each node in the robot topological map. This FSH registers local feature stability over time through a voting scheme, and the most stable features were considered for mapping, for Bayesian localization and for incrementally updating the current appearance reference view in the topological map. The experimental results are presented using an omnidirectional images dataset acquired over the long-term and considering: illumination changes (time of day, different seasons), occlusions, random removal of features, and perceptual aliasing. The results include a comparison with the approach proposed by Dayoub and Duckett (2008) [19] and the popular Bag-of-Words (Bazeille and Filliat, 2010) [35] approach. The obtained results confirm the viability of our method and indicate that it can adapt the internal map representation over time to localize the robot both globally and locally.

This paper presents our teaching experience in control educational issues through a summer course ” Applied Mobile Robotics”. The main aim is to integrate different knowledge related to control and computer science through an adequate robotic framework that acts as an educational multidisciplinary tool in the course. As a practical approach, the majority of educational activities of the course are carried out at our university labs. The students are greatly motivated by working on such a robotic platform, which permits them to consolidate the acquired knowledge and to extend their complementary curricula.

This work proposes a tool based on texture analysis to characterise incorrect point correspondences in underwater image pairs. Interest point correspondences are first detected through region correlation, obtaining pairs of matched points in both images. For every pair of points, their textural characteristics are computed. These textural properties are stored in two corresponding characterisation vectors, which are then compared by means of similarity measures. This measure can be considered as a reliable threshold for outlier rejection. Experiments with real underwater images were carried out.

Image segmentation has been, and still is, an important research area in Computer Vision, and hundreds of segmentation algorithms have been proposed in the last 30 years. However, elementary segmentation techniques based on either boundary or region information often fail to produce accurate segmentation results on their own. In the last few years, there has therefore been a trend towards algorithms that take advantage of their complementary nature. This paper reviews various segmentation proposals that integrate edge and region information and highlights different strategies and methods for fusing such information. The key objective is to point out the advantages and disadvantages of the various approaches, as well as to comment upon new and interesting ideas that perhaps have not been properly exploited.

Two-dimensional forward-looking sonars are becoming standard sensors in both remotely operated and autonomous underwater vehicles, increasing the possibility of mapping under low visibility conditions. Due to the inherent nature of sonar image formation, the ideal mapping strategy relies on maintaining the same orientation, so as to minimize intensity alterations due to viewpoint changes. However, this is not always possible and therefore it is necessary to deal with the registration of sonar images under rotational movements. Previous investigations have discouraged the use of feature-based techniques and have suggested the use of global methods that are robust to noise, low-resolution and inhomogeneous insonification and can deal with the decoupled estimation of roto-translations. In this paper we review several candidate methods and assess them by using real data gathered under different conditions. By identifying the best approach for rotation estimation we aim to extend the applicability of sonar mosaicing to more diverse scenarios. Results indicate that applying phase correlation directly to polar frames leads to the highest accuracy under most cases.

The progress on robotics, computer technology, sensors and perception systems has produced great advances in the field of underwater robotics, in the research and development of both autonomous ROVs and AUVs. Unfortunately, this progress has driven to the use of very expensive devices and, consequently, the development of high-cost underwater vehicles. As an alternative approach, the GARBI underwater robot has been designed as a low-cost ROV developed for underwater applications in shallow water environments. This paper describes the underwater vehicle and the endowed systems, the robot's goals and its main applications.

This paper presents basically the sensorial system, the navigation systems and the data communication of the ROGER autonomous mobile robot. This robot is capable to operate in structured indoor environments and in certain outdoor environments. The robot can place itself independently in its workspace, and can perform some basic manipulation operations. These features make possible the use this mobile robot in a wide range of applications (industrial, social, etc.)

Two-dimensional forward-looking sonars such as Blueview or DIDSON are becoming a standard sensor in both remotely-operated and autonomous underwater vehicles. Registration of imagery obtained from this sensors is of great interest since it constitutes a key step in several applications like the generation of acoustic mosaics or the extraction of vehicle motion estimates from sonar imagery, specially on poor visibility conditions. However, the characteristics of these sonar images, such as low signal-to-noise ratio, low resolution and intensity alterations due to viewpoint changes pose a challenge to the traditional registration techniques applied on optical images. In this paper, the performance of popular registration methods commonly used in photomosaicing are evaluated on real sonar data, including feature-based methods and an areabased approach. Experiments are carried out on different environments, from man-made structured scenarios to more natural and featureless areas, and under challenging conditions such as viewpoint changes and the presence of different sonarspecific artifacts. After assessing the impact of all these factors on the different registration techniques, we show that Fourierbased registration method stands as the more robust option to register acoustic imagery This work has been supported by the FP7-ICT-2011-7 project PANDORA-Persistent Autonomy through Learning, Adaptation, Observation and Re-planning (Ref 288273) funded by the European Commission and the Spanish Project ANDREA/RAIMON (Ref CTM2011-29691-C02-02) funded by the Ministry of Science and Innovation

This work proposes a system for long-term mapping and localization based on the Feature Stability Histogram (FSH) model which is an innovative feature management approach able to cope with changing environments. FSH is built using a voting schema, where re-observed features are promoted; otherwise the feature progressively decreases its corresponding FSH value. FSH is inspired by the human memory model. This model introduces concepts of Short-Term Memory (STM), which retains information long enough to use it, and Long-Term Memory (LTM), which retains information for longer periods of time. If the entries in STM are continuously rehearsed, they become part of LTM. However, this work proposes a change in the pipeline of this model, allowing any feature to be part of STM or LTM depending on the feature strength. FSH stores the stability values of local features, stable features are only used for localization and mapping. Experimental validation of the FSH model was conducted using the FastSLAM framework and a long-term dataset collected during a period of one year at different environmental conditions. The experiments carried out include qualitative and quantitative results such as: filtering out dynamic objects, increasing map accuracy, scalability, and reducing the data association effort in long-term run This work has been partially supported by the project RAIMON—Autonomous Underwater Robot for Marine Fish Farms Inspection and Monitoring (Ref. CTM2011-29691-C02-02) funded by the Spanish Ministry of Science and Innovation, the LASPAUCOLCIENCIAS grant 136-2008, the University of Valle contract 644-19-04-95, and the consolidated research group’s grant SGR2009-00380

The present proposal is inspired by bionics; in this way, local path planning is developed by using the perception system under a deliberative strategy. Indoor global navigation is attained by solving local goals within an occupancy grid framework. Path-planning is computed at each perception step by using the on-robot monocular perception system and considering the detected obstacles. In this context, artificial potential fields are used for attracting the robot towards the desired coordinates. Trajectory tracking is based on reactive behaviors that are performed by using local model predictive control techniques. Global navigation is done by considering a set of local points that have to be reached using reactive behaviors. Dead reckoning problems are set to zero by selecting natural structured landmarks as local passage points that act as local attraction fields towards the goal. The research work is completed with a set of local experiments developed on a wheeled mobile robot.

In this chapter the use of an open mobile robot platform as an innovative educational tool in order to promote and integrate control science knowledge is presented. The idea of including applied interdisciplinary concepts is an important objective in engineering education. Future work in Electrical and Computer Engineering education is pointing towards gaining the ability to understand the technical details of a wide variety of disciplines (Antsaklis et al., 1999, Murray et al., 2003). Moreover, experimental developments have helped to breathe life into theoretical concepts found in text books and have thereby greatly changed the educational experience of students (Hristu-Varsakelis and Brockett, 2002). Students react positively to realism, since the models used for such experiments are in general accurately described by some relatively simple differential equations. Within this framework, the challenge of using mobile robots becomes evident. Thus, realistic platforms that incorporate engineering standards and realistic constraints increase student skills and experience through engineering practices. In this sense, mobile robot platforms can be used as educational tools to promote and integrate different curriculum subjects. Control system world and computer science share a mutual interest in robotics. Therefore, in the educational community, the Robotics Education Lab becomes a resource for supporting courses with an academic curriculum in a broad range of subjects. Some important institutions have developed various teaching activities by introducing the mobile robots as a necessary academic tool in a broad sense in order to extend the student’s previously acquired knowledge (Carnegie Mellon University, 2011, Qu and Wu, 2006). In this context, many universities take advantage of mobile robot competitions in engineering education. This allows real world problem projects to be tackled, and fundamental concepts by increasing motivation and retention purposes to be reinforced. Thus, for example, FIRST (For Inspiration and Recognition of Science and Technology) mobile robot contest attracts young people to careers in engineering, technology and science. Robotics competition encourages students to apply knowledge gained throughout their engineering degree, it also offers all students a chance to serve as members of interdisciplinary engineering teams, and introduces both freshmen and sophomores to engineering concepts. Moreover, university curriculum is reinforced by the knowledge gained throughout applied experiences that embrace a wide spectrum of subjects (Wilczynski and Flowers, 2006). The educational and research objectives can also be achieved through the use of configurable, small, low-cost such as LEGO mobile robot kits (Valera, 2007).

Model predictive control, MPC, has many interesting features for its application to mobile robot control. It is a more effective advanced control technique, as compared to the standard PID control, and has made a significant impact on industrial process control (Maciejowski, 2002). MPC usually contains the following three ideas: • The model of the process is used to predict the future outputs along a horizon time. • An index of performance is optimized by a control sequence computation. • It is used a receding horizon idea, so at each instant of time the horizon is moved towards the future. It involves the application of the first control signal of the sequence computed at each step. The majority of the research developed using MPC techniques and their application to WMR (wheeled mobile robots) is based on the fact that the reference trajectory is known beforehand (Klancar & Skrjanc, 2007). The use of mobile robot kinematics to predict future system outputs has been proposed in most of the different research developed (Kuhne et al., 2005; Gupta et al., 2005). The use of kinematics have to include velocity and acceleration constraints to prevent WMR of unfeasible trajectory-tracking objectives. MPC applicability to vehicle guidance has been mainly addressed at path-tracking using different on-field fixed trajectories and using kinematics models. However, when dynamic environments or obstacle avoidance policies are considered, the navigation path planning must be constrained to the robot neighborhood where reactive behaviors are expected (Fox et al., 1997; Ogren & Leonard, 2005). Due to the unknown environment uncertainties, short prediction horizons have been proposed (Pacheco et al., 2008). In this context, the use of dynamic input-output models is proposed as a way to include the dynamic constraints within the system model for controller design. In order to do this, a set of dynamic models obtained from experimental robot system identification are used for predicting the horizon of available coordinates. Knowledge of different models can provide information about the dynamics of the robot, and consequently about the reactive parameters, as well as the safe stop distances. This work extends the use of on-line MPC as a suitable local path-tracking methodology by using a set of linear time-varying descriptions of the system dynamics when short prediction horizons are used. In the approach presented, the trajectory is dynamically updated by giving a straight line to be tracked. In this way, the control law considers the local point to be achieved and the WMR coordinates. The cost function is formulated with parameters that involve the capacity of turning and going straight. In the