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When carrying out robotic manipulation tasks, objects occasionally fall as a result of the rotation caused by slippage. This can be prevented by obtaining tactile information that provides better knowledge on the physical properties of the grasping. In this paper, we estimate the rotation angle of a grasped object when slippage occurs. We implement a system made up of a neural network with which to segment the contact region and an algorithm with which to estimate the rotated angle of that region. This method is applied to DIGIT tactile sensors. Our system has additionally been trained and tested with our publicly available dataset which is, to the best of our knowledge, the first dataset related to tactile segmentation from non-synthetic images to appear in the literature, and with which we have attained results of 95% and 90% as regards Dice and IoU metrics in the worst scenario. Moreover, we have obtained a maximum error of ≈ 3° when testing with objects not previously seen by our system in 45 different lifts. This, therefore, proved that our approach is able to detect the slippage movement, thus providing a possible reaction that will prevent the object from falling. This work was supported by the Ministry of Science and Innovation of the Spanish Government through the research project PID2021-122685OB-I00 and by the University of Alicante through the grant UAFPU21-26.

The papers in this special section focus on robotic handling of deformable objects. Object handling and manipulation is a recurrent theme in robotics, moving progressively from simple rigid objects to articulated objects and then complex deformable objects. In recent years, there has been a growing interest in the development of robotic systems that are able to handle deformable objects, due to many potential applications in the industrial and domestic field but also in robotics in remote hostile environments. Despite the promising prospects, considering object deformation in grasping and manipulation poses significant challenges affecting almost all aspects of robotics: perception, modeling, planning, control and grasping.

Paper submitted to the 8th International Conference on Control, Automation and Robotics (ICCAR), Xiamen, China, April 8-10, 2022. This paper presents an approach of a detection and location system for waste recognition in outdoor environments that can be usable on an autonomous robot for garbage collection. It is composed of a camera and a LiDAR. For the detection task, some YOLO models were trained and tested for classification of waste by using a own dataset acquired from the camera. The image coordinates predicted by the best detector are used in order to compute the location relative to the camera. Then, we used the LiDAR to get a global waste location relative to the robot, transforming the coordinates of the center of each trash instance. Our detection approach was tested in outdoor environments obtaining a mAP@.5 around 0.99 and a mAP@.95 over 0.84, and an average time of detection less than 40 ms., being able to make it in real time. The location method was also tested in presence of objects at a maximum distance of 8 m., obtaining an average error smaller than 0.25 m. This research was funded by Spanish Government through the project RTI2018-094279-B-I00. Besides, computer facilities were provided by Valencian Government and FEDER through the IDIFEFER/2020/003.

The camera-Lidar sensor fusion plays an important role in autonomous navigation research. Nowadays, the automatic calibration of these sensors remains a significant challenge in mobile robotics. In this article, we present a novel calibration method that achieves an accurate six-degree-of-freedom (6-DOF) rigid-body transformation estimation (aka extrinsic parameters) between the camera and LiDAR sensors. This method consists of a novel co-registration approach that uses local edge features in arbitrary environments to get 3D-to-2D errors between the data of both, camera and LiDAR. Once we have 3D-to-2D errors, we estimate the relative transform, i.e., the extrinsic parameters, that minimizes these errors. In order to find the best transform solution, we use the perspective-three-point (P3P) algorithm. To refine the final calibration, we use a Kalman Filter, which gives the system high stability against noise disturbances. The presented method does not require, in any case, an artificial target, or a structured environment, and therefore, it is a target-less calibration. Furthermore, the method we present in this article does not require to achieve a dense point cloud, which holds the advantage of not needing a scan accumulation. To test our approach, we use the state-of-the-art Kitti dataset, taking the calibration provided by the dataset as the ground truth. In this way, we achieve accuracy results, and we demonstrate the robustness of the system against very noisy observations. This work was supported by the Regional Valencian Community Government and the European Regional Development Fund (ERDF) through the grants ACIF/2019/088 and AICO/2019/020.

The goal of this paper is to present a robotic workcell to automate several tasks of the cementing process in footwear manufacturing. Our cell's main applications are sole digitization of a wide variety of footwear, glue dispensing and sole grasping from conveyor belts. This cell is made up of a manipulator arm endowed with a gripper, a conveyor belt and a 3D scanner. We have integrated all the elements into a ROS simulation environment facilitating control and communication among them, also providing flexibility to support future extensions. We propose a novel method to grasp soles of different shape, size and material, exploiting the particular characteristics of these objects. Our method relies on object contour extraction using concave hulls. We evaluate it on point clouds of 16 digitized real soles in three different scenarios: concave hull, k-NNs extension and PCA correction. While we have tested this workcell in a simulated environment, the presented system's performance is scheduled to be tested on a real setup at INESCOP facilities in the upcoming months. Work funded by the European Commission and FEDER funds through the COMMANDIA project (SOE2/P1/F0638), supported by Interreg-V Sudoe.

This paper presents a method that can be used for the efficient detection of small maritime objects. The proposed method employs aerial images in the visible spectrum as inputs to train a categorical convolutional neural network for the classification of ships. A subset of those filters that make the greatest contribution to the classification of the target class is selected from the inner layers of the CNN. The gradients with respect to the input image are then calculated on these filters, which are subsequently normalized and combined. Thresholding and a morphological operation are then applied in order to eventually obtain the localization. One of the advantages of the proposed approach with regard to previous object detection methods is that it is only required to label a few images with bounding boxes of the targets to be trained for localization. The method was evaluated with an extended version of the MASATI (MAritime SATellite Imagery) dataset. This new dataset has more than 7 000 images, 4 157 of which contain ships. Using only 14 training images, the proposed approach achieves better results for small targets than other well-known object detection methods, which also require many more training images. This work was supported in part by the Spanish Government's Ministry of Economy, Industry, and Competitiveness under Project RTC-2014-1863-8, and in part by the Babcock MCS Spain under Project INAER4-14Y (IDI-20141234).

Tactile sensors provide useful contact data during the interaction with an object which can be used to accurately learn to determine the stability of a grasp. Most of the works in the literature represented tactile readings as plain feature vectors or matrix-like tactile images, using them to train machine learning models. In this work, we explore an alternative way of exploiting tactile information to predict grasp stability by leveraging graph-like representations of tactile data, which preserve the actual spatial arrangement of the sensor's taxels and their locality. In experimentation, we trained a Graph Neural Network to binary classify grasps as stable or slippery ones. To train such network and prove its predictive capabilities for the problem at hand, we captured a novel dataset of ~ 5000 three-fingered grasps across 41 objects for training and 1000 grasps with 10 unknown objects for testing. Our experiments prove that this novel approach can be effectively used to predict grasp stability. This work has been funded by the Spanish Government with Feder funds (TIN2016-76515-R and DPI2015-68087-R), by two grants for PhD studies (FPU15/04516 and BES-2016-07829), by regional projects (GV/2018/022 and GRE16-19) and by the European Commission (COMMANDIA SOE2/P1/F0638), action supported by Interreg-V Sudoe.

This paper presents a system for the detection of ships and oil spills using side-looking airborne radar (SLAR) images. The proposed method employs a two-stage architecture composed of three pairs of convolutional neural networks (CNNs). Each pair of networks is trained to recognize a single class (ship, oil spill, and coast) by following two steps: a first network performs a coarse detection, and then, a second specialized CNN obtains the precise localization of the pixels belonging to each class. After classification, a postprocessing stage is performed by applying a morphological opening filter in order to eliminate small look-alikes, and removing those oil spills and ships that are surrounded by a minimum amount of coast. Data augmentation is performed to increase the number of samples, owing to the difficulty involved in obtaining a sufficient number of correctly labeled SLAR images. The proposed method is evaluated and compared to a single multiclass CNN architecture and to previous state-of-the-art methods using accuracy, precision, recall, F-measure, and intersection over union. The results show that the proposed method is efficient and competitive, and outperforms the approaches previously used for this task. This work was supported in part by the Spanish Government’s Ministry of Economy, Industry, and Competitiveness under Project RTC-2014-1863-8 and in part by Babcock MCS Spain under Project INAER4-14Y (IDI-20141234).

Designing educational resources allow students to modify their learning process. In particular, on-line and downloadable educational resources have been successfully used in engineering education the last years [1]. Usually, these resources are free and accessible from web. In addition, they are designed and developed by lecturers and used by their students. But, they are rarely developed by students in order to be used by other students. In this work-in-progress, lecturers and students are working together to implement educational resources, which can be used by students to improve the learning process of computer networks subject in engineering studies. In particular, network topologies to model LAN (Local Area Network) and MAN (Metropolitan Area Network) are virtualized in order to simulate the behavior of the links and nodes when they are interconnected with different physical and logical design. This work is supported by the “Computer Science Research Institute” of the University of Alicante through the aid “Internationalization and quality of the doctoral program”.

Virtual and remote laboratories (VRLs) are e-learning resources that enhance the accessibility of experimental setups providing a distance teaching framework which meets the student's hands-on learning needs. In addition, online collaborative communication represents a practical and a constructivist method to transmit the knowledge and experience from the teacher to students, overcoming physical distance and isolation. This paper describes the extension of two open source tools: (1) the learning management system Moodle, and (2) the tool to create VRLs Easy Java Simulations (EJS). Our extension provides: (1) synchronous collaborative support to any VRL developed with EJS (i.e., any existing VRL written in EJS can be automatically converted into a collaborative lab with no cost), and (2) support to deploy synchronous collaborative VRLs into Moodle. Using our approach students and/or teachers can invite other users enrolled in a Moodle course to a real-time collaborative experimental session, sharing and/or supervising experiences at the same time they practice and explore experiments using VRLs. This work was supported by the Spanish Government under the CICYT Project DPI2007-61068 and the GITE grant of the Technology and Educational Innovation Vice-President Office of the University of Alicante.

New low cost sensors and the new open free libraries for 3D image processing are permitting to achieve important advances for robot vision applications such as tridimensional object recognition, semantic mapping, navigation and localization of robots, human detection and/or gesture recognition for human-machine interaction. In this paper, a method to recognize the human hand and to track the fingers is proposed. This new method is based on point clouds from range images, RGBD. It does not require visual marks, camera calibration, environment knowledge and complex expensive acquisition systems. Furthermore, this method has been implemented to create a human interface in order to move a robot hand. The human hand is recognized and the movement of the fingers is analyzed. Afterwards, it is imitated from a Barret hand, using communication events programmed from ROS. This work was supported in part by the Valencia Regional Government and the Research and Innovation Vice-president Office of the University of Alicante for their financial support through the projects GV2012/102 and GRE10-16, respectively.

This paper analyzes the learning experiences and opinions obtained from a group of undergraduate students in their interaction with several on-line multimedia resources included in a free on-line course about Computer Networks. These new educational resources employed are based on the Web2.0 approach such as blogs, videos and virtual labs which have been added in a web-site for distance self-learning. IEEE Education Society

This paper introduces a new set of compact interactive simulations developed for the constructive learning of computer networks concepts. These simulations, which compose a virtual laboratory implemented as portable Java applets, have been created by combining EJS (Easy Java Simulations) with the KivaNS API. Furthermore, in this work, the skills and motivation level acquired by the students are evaluated and measured when these simulations are combined with Moodle and SCORM (Sharable Content Object Reference Model) documents. This study has been developed to improve and stimulate the autonomous constructive learning in addition to provide timetable flexibility for a Computer Networks subject. IEEE Education Society

This paper describes the modelling, simulation and visual servo control of a robot-arm designed for supervision of complex task environments. This robot (mini-robot) has 2 degrees of freedom (hereafter d.o.f.) and it is attached at the end of another anthropomorphic robot with 7 d.o.f. (main robot). In order to achieve a suitable supervision, the mini-robot has to perform a given trajectory around the workspace where the task is developed. With the purpose of obtaining an accurate and fast supervision, this paper presents a novel tracking method carried out in the image space using direct visual servo control. This work is supported by the Spanish Ministry of Education and Science (MEC) through the research project DPI2008-02647.

This paper develops the kinematic, dynamic and contact models of a three-fingered robotic hand (BarrettHand) in order to obtain a complete description of the system which is required for manipulation tasks. These models do not only take into account the mechanical coupling and the breakaway mechanism of the under-actuated robotic hand but they also obtain the force transmission from the hand to objects, which are represented as triangle meshes. The developed models have been implemented on a software simulator based on the Easy Java Simulations platform. Several experiments have been performed in order to verify the accuracy of the proposed models with regard to the real physic system. This work is supported by the Spanish Ministries of Education, Science and Innovation through the research project DPI2008-02647 ('Intelligent Manipulation through Haptic Perception and Visual Servoing by Using an Articulated Structure situated over a Robotic Manipulator') and the grant AP2005-1458.

This paper presents a novel human-robot interaction system which guarantees the safety of human operators which cooperate with robotic manipulators. In particular, this system computes on realtime the minimum distance between the human operator and the robotic manipulator and modify the trajectory of the robot accordingly. The system is integrated by two elements: a human tracking system and a hierarchy of bounding volumes. The tracking system combines the measurements of a motion capture system and an UWB localization system by a modified Kalman filter and thus the movements of the human are registered with accuracy. The hierarchy of bounding volumes covers the bodies of the human and the robot so that the minimum distance between them can be computed efficiently. A new distance computation algorithm based on this hierarchy has been developed. Finally, the system has been applied on a real task. This work was supported in part by the Spanish Ministry of Education under grant AP2005-1458 and by the Spanish Ministry of Science and Innovation under research projects DPI2005-06222 (Design, Implementation and Experimentation of Intelligent Manipulation Scenarios for Automatic Assembly/Disassembly Applications) and DPI2008-02647 (Intelligent Manipulation through Haptic Perception and Visual Servoing by Using and Articulated Structure situated over a Robotic Manipulator).

When a human is introduced into a robotic cell, the robot controller must be aware of the human location in order to assure her/his physical integrity. This paper presents a pre-collision strategy which maintains a safety distance between a robot and a human who wears a tracking system composed of a motion capture suit and a UWB localization system. The system proposed is able to guide the robot using visual servoing through a previously defined path. The time-independent behaviour of this system enables the robot to completely track the desired trajectory, even in those cases when the robot goes away from the human during the tracking to guarantee her/his safety. This work was supported in part by the Spanish MEC under Project DPI2005-06222 and Grant AP2005-1458 and by the Valencian Community under Grant BPI06/015.

In this paper, a new method to track image trajectories by visual servoing is proposed. This method solves the problem of the previous proposed time-independent tracking systems based on visual servoing. With the proposed method, the robot can track a previously generated trajectory affording a correct tracking not only in the image but also in the 3D space. This new method presents several improvements over the previous ones such as the possibility of specifying the desired tracking velocity, a less oscillating behavior or a correct tracking in the 3D space when high velocities are used. In order to demonstrate the correct behavior of the visual servoing system, an eye-in-hand camera system is used. Funded by the Spanish MEC project "Diseño, Implementación y Experimentación de Escenarios de Manipulación Inteligentes para Aplicaciones de Ensamblado y Desensamblado Automático".

This paper proposes a new method for the detection and elimination of specular reflectance in color images of real scenes. We use a two-dimensional histogram that allows us to relate the signals of intensity and saturation of a color image, and to identify the specularities in an area of the histogram. This is known as the Intensity-Saturation (MS) diagram, and it is constructed from the Intensity-Saturation-Hue (MSH) generalized color space. An experimental and detailed study of the presence of specularities in the MS diagram for different types of materials in real scenes is carried out. To eliminate the specularities detected, we use a new connected vectorial filter based on the extension of mathematical morphology to color images, employing a lexicographical order. This new filter operates only in the bright areas previously detected, avoiding the high cost of processing the connected filters and the related oversimplification. The proposed method achieves results similar to current methods, but without the need for costly multiple-view systems or stereo images.

In this paper, the so-called 2-D movement flowbased visual servoing system is proposed, which allows the tracking of trajectories planned in the image space. This method is time-independent, which is useful when problems such as obstructions occur during the tracking. This method allows the tracking of image trajectories assuring the correct behaviour in the 3-D space and avoiding the limitations of the time-dependent systems used up to now for tracking trajectories. This aspect has allowed its application to manipulation tasks in which the trajectory can be obstructed during the tracking. This work is partially supported by the Spanish MCYT project “DESAURO: Desensamblado Automático Selectivo para Reciclado mediante Robots Cooperativos y Sistema Multisensorial” (DPI2002-02103).

This paper deals with the use of vector-connected filters for eliminating Gaussian noise in colour images. This class of morphological filters suppresses noise but preserves the contours of the objects. We impose a total order between pixels for morphological processing. Once the HSI space has been adapted, we employ it in the lexicographical order. As such, all of the morphological operations are vectorial. After having defined the vectorial geodesic operators, they are then employed to eliminate Gaussian noise.

En este artículo se describe una nueva técnica para el seguimiento de trayectorias ante oclusiones empleando control basado en imagen, denominada control visual basado en flujo de movimiento. Esta estrategia permite el seguimiento de trayectorias especificadas en el espacio de la imagen presentando asimismo un correcto comportamiento en el espacio cartesiano 3- D. Una de las principales aportaciones conseguidas es la posibilidad de realizar el seguimiento de forma no dependiente del tiempo, lo que permite asegurar un correcto seguimiento de la trayectoria al no verse el sistema sometido a restricciones temporales. Para posibilitar el seguimiento de trayectorias ante oclusiones se describe la integración de la información procedente del flujo de movimiento en un estimador basado en el filtro de Kalman, y para validar las estrategias propuestas se aplican a un robot con una cámara en su extremo. In this article, we describe a new approach for the tracking of trajectories with occlusions, using image-based control, referred to here as movement flow-based visual servoing. This approach allows the tracking of trajectories specified in the image space and shows a correct behaviour in the 3-D Cartesian space. One of the main contributions of this method is a tracking that is not dependent on time, which affords a better tracking as the system is not subject to time restrictions. To make the tracking of trajectories with occlusions possible, we describe the inclusion of information from the movement flow into an estimator based on the Kalman filter. To verify the correct behaviour of this approach, an eye-in-hand camera system is employed.

Automatic quality control examination of banknotes constitutes an important research challenge for the main printing organizations in the world. It is worth to note that presently there is no commercial product with an acceptable performance, in spite of the efforts spent during last ten years. The product complexity lies in the combination of several printings with the purpose of achieving hard to counterfeit banknotes. The printing system that is necessary for banknotes manufacturing, causes significant differences between several printed sheets in similar conditions. However, in spite of these differences, while respecting certain tolerances, the banknotes are accepted. The required quality of the product demands an exhaustive inspection of the bill sheets. Nowadays the quality control is carried out by manual turn over of the bill sheet, reaching a speed of 400 sheets/hour. It has been developed a system that works out an automatic inspection at a rate of 3600 sheets/hour.