Providing force feedback as a feature in current Robot-Assisted Minimally Invasive Surgery systems still remains a challenge. In recent years, Vision-Based Force Sensing (VBFS) has emerged as a promising approach to address this problem. Existing methods have been developed in a Supervised Learning (SL) setting. Nonetheless, most of the video sequences related to robotic surgery are not provided with ground-truth force data, which can be easily acquired in a controlled environment. A powerful approach to process unlabeled video sequences and find a compact representation for each video frame relies on using an Unsupervised Learning (UL) method. Afterward, a model trained in an SL setting can take advantage of the available ground-truth force data. In the present work, UL and SL techniques are used to investigate a model in a Semi-Supervised Learning (SSL) framework, consisting of an encoder network and a Long-Short Term Memory (LSTM) network. First, a Convolutional Auto-Encoder (CAE) is trained to learn a compact representation for each RGB frame in a video sequence. To facilitate the reconstruction of high and low frequencies found in images, this CAE is optimized using an adversarial framework and a L1-loss, respectively. Thereafter, the encoder network of the CAE is serially connected with an LSTM network and trained jointly to minimize the difference between ground-truth and estimated force data. Datasets addressing the force estimation task are scarce. Therefore, the experiments have been validated in a custom dataset. The results suggest that the proposed approach is promising.

Within the context of tele-laboratories for education the use of the Internet as communication media permits any researcher/student to perform remote experiments in a simple and reliable manner. Moreover, this situation introduces many interesting issues like network protocols for Internet robots, the effects of variable bandwidth and time-delays on telerobotics, etc. In this paper we present a comparative analysis of using several Internet transport protocols when performing a remote experiment within the UJI Industrial Telelaboratory. TCP, UDP, Trinomial and TEAR protocols are analyzed using the NS2 simulator. Conclusions show a set of characteristics the authors of this paper consider very important when designing an end-to-end congestion control transport protocol for Internet Telerobotics. These ideas are the basis for the definition of the SNRTP (simple network robot transport protocol).

Reliability, simplicity and low cost are three requirements difficult to combine in Robotics. Therefore, this combination has a great interest for education institutions. Our work is addressed to develop multirobot system using low cost platform. Simple LEGO © robots are enhanced by adding them an expansion module, in order to break its main limitations, and integrating it in an off-board programming system. A distributed architecture structured in four processing levels is implemented to allow users to manage, control and program a multirobot system through Internet.

Reliability, simplicity and low cost are three properties that are difficult to combine in Robotics. Therefore, this combination has a great interest for education institutions. Our work is addressed to develop multirobot system using low cost platform. Simple LEGO© robots are enhanced by adding them expansion modules, in order to break its main limitations, and integrating it in an off-board programming system. A distributed architecture structured in four processing levels is implemented in order to allow users to manage, control and program a multirobot system through Internet.

Laboratories are key components in the learning process of applied matters. The laboratory enables students to acquire methodologies, work habitude, knowledge on equipment operation and experience, in conditions as near as possible to their future professional activity. The evolution of communication and information technologies opens new possibilities in educational methods. The purpose of this paper is to present a Web based system for the implementation of a robotics laboratory with didactic finalities. The laboratory is to be accessible indifferently and simultaneously, in situ or via Internet. The system presented aims at providing access to the laboratory at any time, from everywhere, without space problems, paying special attention to safety requirements. For these reasons we call it an Open laboratory.

Like oral communication, gestures are a natural way to carry out human machine interface. In the early days of robotic systems, human gesture was used to control robot movements by means of a master-slave structure. In spite of the use if robot programming languages, manual control is the most reliable way to carry out complex tasks in unstructured environments. In these situations, a non-contact, passive and remote system can be helpful to control a teleoperated robot by means of human gestures. In this paper, a vision system able to detect, locate and track the head and hands of a human body is presented. The system uses several calibrated cameras placed around the operator scenario to locate the body parts of a person in 3D. The system combines different computer vision techniques to increase the reliability of the body parts detection: image movement detection, user skin colour segmentation and stereo. The data provided by these modules are focused looking for coherence according to the human body dimensions. With the scheme proposed it is possible to obtain a low-cost real-time system for human computer interfacing based in a natural way of communication (gestures). Civil area such as big robots in shipyards, mines, public works or cranes is some possible applications.

Teleoperation in harsh environments has to tackle the problem of working with images of poor quality, as the visual feedback means for the human operator. This paper describes a procedure for image augmentation, based on the models of the scene elements, which are built from the images acquired in a previous phase, before the execution of the task, when the visual environment conditions are still good enough.

Object recognition systems need image segmentation processes that relate image regions to world objects. These methods present often three problems: the generation of a large number of small regions, undersegmentation (different objects are associated to the same image region) and oversegmentation (a scene object is segmented in various regions). In order to overcome these problems, we propose an image segmentation method that combines depth information and object surface properties obtained from a pair of stereo images. The system work under the standard assumption that 3D objects have planar faces and regular shapes. First a region growing segmentation process is applied to both images generating two labeled images. Then, depth information of the region frontiers is obtained by matching the labeled segments from left and right image rows. The stereo matching problem is solved by finding a path through a 2D search plane whose axes are the left and right segmented lines. Original image regions are then merged based on their size, surface information and frontiers depth information. In this way, image regions are associated to surfaces that are contiguous in the 3D space and they present a common property (such as gray level, color or texture).

This paper faces up the problem of carrying out simple tasks, such as to fasten an object, tasks that become relatively complex in operations using robots, specially in underwater environments. The goal of this work is to perform such tasks by using intelligent strategies that plan step by step allowing for compensation of the robots own limitations. The strategy is based on the use of information from stereo vision and the required auxiliary devices or tools to contribute to the development of the task.

Presents a methodology for autonomous navigation in weakly structured outdoor environments such as dirt roads or mountain ways. The main problem to solve is the detection of an ill-defined structure-the way-and the obstacles in the scene, when working in variable lighting conditions. First, we discuss the road description requirements to perform autonomous navigation in this kind of environment and propose a simple sensors configuration based on vision. A simplified road description is generated from the analysis of a sequence of color images, considering the constraints imposed by the model of ill-structured roads. This environment description is done in three steps: region segmentation, obstacle detection and coherence evaluation.