Alberto Tellaeche

Robotics for the benefit of footwear industry

This paper presents the initial results achieved by the ROBOFOOT project aimed at contributing to the introduction of robotics in the Footwear Manufacturing Industry. In particular, user requirements, operations selected and technical achievements reached so far are described. Visual servoing solution developed for shoe pose identification is described with deeper detail. The introduction of this technology allows the coexistence of current working practices and robotic solutions, with minor changes in the production means already existing in most companies. This has been identified as one of the requirements by the end-users taking part in the project.

Automatic quality inspection of percussion cap mass production by means of 3d machine vision and machine learning techniques

The exhaustive quality control is becoming very important in the worlds globalized market One of these examples where quality control becomes critical is the percussion cap mass production These elements must achieve a minimum tolerance deviation in their fabrication This paper outlines a machine vision development using a 3D camera for the inspection of the whole production of percussion caps This system presents multiple problems, such as metallic reflections in the percussion caps, high speed movement of the system and mechanical errors and irregularities in percussion cap placement Due to these problems, it is impossible to solve the problem by traditional image processing methods, and hence, machine learning algorithms have been tested to provide a feasible classification of the possible errors present in the percussion caps.

Robotic solutions for Footwear Industry

Since September 2010, the ROBOFOOT consortium, a group of 10 partners, including Footwear Industry, Research institutes and Robotic solution providers is working together to promote the introduction of robotics in the European Footwear Manufacturing Industry. This paper presents the initial results achieved, in particular they are described the user requirements and operations selected and the technical achievements reached so far. The approach followed allows the coexistence of current working practices and facilities with robotic solutions. Due to the nature of the industry, either in size and financial capability, it has been identified as one of the requirements by end-users taking part in the project.

Use of machine vision in collaborative robotics: An industrial case.

Robotic applications are evolving to a paradigm of collaborative robotics, where human workers and compliant robots work together to solve complex tasks, until now done fully manually. These tasks also present another challenging issue for the use of robotics, variability in part and tool positions, in robot placement and even in the targets for the robot operation. To comply with all this uncertainties while solving the work efficiently, the robot needs to be equipped with sensors that allow it perceive the environment. For this, machine vision techniques in all its variants (2D, 3D, point clouds) becomes fundamental. This paper outlines a real industrial case of collaborative robotics, and the details of use of machine vision techniques to cope with variability and uncertainties. The industrial case presented has been developed as part of the EuRoC European project, under the 7th European Framework.

Natural multimodal communication for human–robot collaboration:

This article presents a semantic approach for multimodal interaction between humans and industrial robots to enhance the dependability and naturalness of the collaboration between them in real industrial settings. The fusion of several interaction mechanisms is particularly relevant in industrial applications in which adverse environmental conditions might affect the performance of vision-based interaction (e.g. poor or changing lighting) or voice-based interaction (e.g. environmental noise). Our approach relies on the recognition of speech and gestures for the processing of requests, dealing with information that can potentially be contradictory or complementary. For disambiguation, it uses semantic technologies that describe the robot characteristics and capabilities as well as the context of the scenario. Although the proposed approach is generic and applicable in different scenarios, this article explains in detail how it has been implemented in two real industrial cases in which a robot and a worker collaborate in assembly and deburring operations.

Enhancing safe human-robot collaboration through natural multimodal communication

This paper presents a semantic multimodal interaction approach between humans and industrial robots to enhance the dependability of the collaboration in real industrial settings. Although this generic approach can be applied in different industrial scenarios, this paper explains in detail how it is implemented in a real case to enhance the accuracy of requests interpretation in order to achieve a more efficient, easy to scale and maintain collaboration between humans and robots.

Human robot interaction in industrial robotics. Examples from research centers to industry

Current state of the art in industrial robotics presents highly autonomous production plants, where an undefined number of robots work in cooperation to perform complicated tasks in an automatic way. However, these tasks are repetitive and generally do not allow a minimum change or variation in the overall process to obtain a successful result. In addition to this, these layouts do not allow any fast or easy change in the programmed operation, being not flexible at all. Contrary to this typical industrial situation, current research activities in industrial robotics are focused on human robot interaction and safety, to carry out added value operations that need to be performed both the human and the robot working together. This paper outlines real research cases of this latest type, which have as the final objective their set up in industrial facilities, presenting a review of the current state of the art in human robot interaction to explain, in following sections, successful industrial cases developed in the framework of European Commission Projects.

6DOF pose estimation of objects for robotic manipulation. A review of different options

6DOF pose estimation of objects in robotics is nowadays an active field of research under different approaches. The solution of this problem depends directly on many aspects, such as the geometry of the object to be located, the flexibility of the solution needed, or the 3D sensors used. Reaching a robust and reliable solution to this problem is essential, being this the first step in present research in industrial robotics, where advanced manipulation and identification of complex objects is fundamental. This paper presents different results obtained solving the 6DOF pose estimation problem, using different software libraries and the Microsoft Kinect as 3D acquisition system to capture the scene.

Machine learning algorithms for quality control in plastic molding industry

Injection molding is a very complicated process to monitor and control. With its high complexity and many process parameters, the optimization of these systems is a very challenging problem. To meet the requirements and costs demanded by the market, there has been an intense development and research with the aim to maintain the process under control. This paper outlines the latest advances in algorithms for plastic injection process and monitoring, and presents a real case of application that verifies their performance.

Robust 3D Object Model Reconstruction and Matching for Complex Automated Deburring Operations

The deburring processes of parts with complex geometries usually present many challenges to be automated. This paper outlines the machine vision techniques involved in the design and set up of an automated adaptive cognitive robotic system for laser deburring of metal casting complex 3D high quality parts. To carry out deburring process operations of the parts autonomously, 3D machine vision techniques have been used for different purposes, explained in this paper. These machine vision algorithms used along with industrial robots and a high tech laser head, make a fully automated deburring process possible. This setup could potentially be applied to medium sized parts of different light casting alloys (Mg, AlZn, etc.).