ITRobotics Srl

Padova, Italy

ITRobotics Srl

Padova, Italy
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Pretto A.,University of Padua | Pretto A.,ITRobotics Srl | Menegatti E.,University of Padua | Menegatti E.,ITRobotics Srl | And 5 more authors.
Joint 41st International Symposium on Robotics and 6th German Conference on Robotics 2010, ISR/ROBOTIK 2010 | Year: 2010

This paper presents a new approach to automatically monitor an indoor environment on thermodynamic basis. It uses temperature as the driving parameter and is especially suited for comfort analysis or evaluation of moisture. The system measures all fundamental environment parameters (e.g., air temperature, relative humidity and air speed) by imaging with a thermal camera (IR camera) a set of special targets arranged in a grid (the reference grid), which can be placed close to a wall or in any other place of the room. The thermal camera is mounted on a pan-tilt unit to realize the monitoring process in an automatic way. The system processes the thermal images in real-time and autonomously controls the pan-tilt unit. A fast automatic learning procedure enables to recognize the special targets on the grid also in challenging environments and in different environment conditions, while a Particle Filter is used to update the state of the system (i.e., position of the intersection point between the optical axis of the camera and the planar surface of the grid). The system is able to perform a reliable global localization of the position of the thermal camera. During the scanning of the wall surfaces, a set of positions are automatically and sequentially reached by the moving IR camera: for each position a thermal image is recorded. Images are hence rectified in order to obtain a more accurate temperature sampling. We successfully tested our system in several challenging environments.

Agency: European Commission | Branch: H2020 | Program: IA | Phase: FOF-02-2016 | Award Amount: 7.26M | Year: 2016

COROMA project proposes to develop a cognitively enhanced robot that can execute multiple tasks for the manufacturing of metal and composite parts. COROMA will therefore provide the flexibility that European metalworking and advanced material manufacturing companies require to compete in the rapidly evolving global market. The main output of COROMA project will be a modular robotic system that will perform multitude of different manufacturing tasks in an autonomous way to adapt to the production requirements. The robot will be capable of performing drilling, trimming, deburring, polishing, sanding, non-destructive inspection and adaptive fixturing operations. Using a simple interface the robot will receive basic commands that require a minimum programming effort from the human operator. The robot will autonomously navigate in the workshop and will automatically perceive the manufacturing scene and locate the part that must be manufactured and even handle some of the required tools. Learning from previous experiences during displacement, tool grasping, part localisation and the manufacturing process itself, the robot will improve its performance. It will be able to interact with other machines in the shop floor and to work on a part even while other manufacturing operations are being performed by these other machines. Safe human-robot and machine-robot collaborations will be paramount and the robot will automatically react to the presence of both humans and other machines. The modularity of the COROMA robot will permit to customize it to meet specific requirements from different manufacturing companies. These challenges require a project consortium where the latest robotic technologies meet knowledge from manufacturing experts, including both industry and academia. COROMA project consortium presents a perfect balance between manufacturing and robotics sectors players.

Agency: European Commission | Branch: FP7 | Program: CP-FP | Phase: FoF.NMP.2011-3 | Award Amount: 3.50M | Year: 2012

Non-destructive testing of components is an important auxiliary process step, not only in post-production but also in regular maintenance. The detection of cracks is currently done by using magnetic particle inspection, which is a decades-old, inefficient and ecologically undesirable process. There is an urgent need in industry to replace this technology with more up-to-date methods that provide fully automatic testing. This project thus aims at the development of an autonomous robotic system for the inspection of metallic and composite parts using thermography. By combining automatic path planning for robots using a process model of thermographic image acquisition and knowledge-based image analysis methods, an inspection robot will be developed that can adapt to new parts within 15 minutes and achieves cycle times in the range of 20-30 seconds. Applications include inspection of metallic and composite parts in the automotive and aircraft industry as well as inspection during regular maintenance, mainly in the aircraft industry, where magnetic particle inspection is often a requirement. Market estimates show a potential of more than 1000 such inspection systems within 5-7 years after the end of the project. Despite a higher initial investment (compared to magnetic particle inspection) the robotic inspection system will save more than 400kEUR after 5 years of operation, thus contributing to a substantial increase in efficiency in these tasks. Furthermore, ecologically undesirable suspensions of magnetic particles that include corrosion-inhibitors can be avoided. The consortium consists of technology providers in robotics, industrial inspection and thermographic cameras and end-users that cover metallic and composite parts in the automotive and aircraft industry. SMEs play a leading role in the project and contribute 60% of the total effort.

Agency: European Commission | Branch: FP7 | Program: BSG-SME | Phase: SME-1 | Award Amount: 1.13M | Year: 2010

In Europe there are about 3000 SMEs working in the field of machine vision. These SMEs provide services and products to another 300.000 SMEs in the machine building and automation sector. One important application of machine vision is quality control and in particular checking the completeness (presence/absence of parts, correct type, position, orientation, ) of assemblies. Existing systems usually apply 2D cameras that provide a monochrome or color image. These images lack the information of depth and consequently have problems when dealing with non-rigid objects (hoses, cables) or low contrast between background and part and they often do not provide an optimal view on each single part of the assembly. This project aims at developing efficient 3D completeness inspection methods that exploit two different technologies. The first one is based on calculating arbitrary views of an object given a small number of images of this object, the second one aims at combining 3D shape data with color and texture information. Both of the technologies will cover the full chain from data acquisition via pre-processing to the final decision-making. They will focus on using standard hardware to create a cost efficient technology. The participating SMEs all have substantial resources for R&D and long experience in their own research activities, however, in order to develop 3D completeness inspection they want to subcontract RTD performers working in image acquisition, 3D/2D data combination and pattern recognition/matching. 3D Completeness inspection is a technological gap in the machine vision market. The SMEs expect substantial growth from entering into this market by integrating this new technology in their range of existing products. They expect a total additional turnover of more than 3 Mio EUR per year. Furthermore, this technology will strengthen the European machine vision market with its 3000 SMEs.

Pretto A.,University of Padua | Tonello S.,ITRobotics Srl | Menegatti E.,University of Padua
IEEE International Conference on Automation Science and Engineering | Year: 2013

In this paper, we present a robust and flexible vision system for 3D localization of planar parts for industrial robots. Our system is able to work with nearly any object with planar shape, randomly placed inside a standard industrial bin or on a conveyor belt. Differently from most systems based on 2D image analysis, which usually can manage parts disposed in single layers, our approach can estimate the 6 degrees of freedom (DoF) pose of planar objects from a single 2D image. The choice of a single camera solution makes our system cheaper and faster with respect to systems using expensive industrial 3D cameras, or laser triangulation systems, or laser range finders. Our system can work virtually with any planar piece, without changing the software parameters, because the input for the recognition and localization algorithm is the CAD data of the planar part. The localization software is based on a two step strategy: i) a candidates selection step based on a well-engineered voting scheme ii) a refinement and best match selection step based on a robust iterative optimize-and-score procedure. During this second step, we employ a novel strategy we called search-in-the-stack that avoids the optimization from being stuck on local minima (representing false positives) created when objects are almost regularly stacked. Our system is currently installed in seven real world industrial plants, with different setups, working with hundreds of different models and successfully guiding the manipulators to pick several hundreds of thousands of pieces per year. In the experiment section, we report statistics about our system at work in real production plants on more than 60000 cycles. © 2013 IEEE.

Boscolo N.,ITRobotics Srl | Tosello E.,University of Padua | Tonello S.,ITRobotics Srl | Finotto M.,ITRobotics Srl | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

This paper presents a motion optimization system for an industrial quality inspection process where a vision device coupled with amanipulator robot arm is able to perform quality and completeness inspection on a complex solid part. In order to be deployed in an industrial production plant, the proposed system has been engineered and integrated as a module of an offline simulator, called WorkCellSimulator, conceived to simulate robot tasks in industrial environments. The novelty of the paper concerns the introduction of time constraints into the motion planning algorithms. Then, these algorithms have been deeply integrated with artificial intelligence techniques in order to optimize the inspection cycle time. This integration makes the application suitable for time-constrained processes like, e.g., autonomous industrial painting or autonomous thermo-graphic detection of cracks in metallic and composite materials. © Springer International Publishing Switzerland 2014.

Ghidoni S.,University of Padua | Finotto M.,ITRobotics s.r.l. | Menegatti E.,University of Padua
IEEE Transactions on Automation Science and Engineering | Year: 2015

In this paper, an automatic optical inspection system for checking the sequence of colored wires in electric cable is presented. The system is able to inspect cables with flat connectors differing in the type and number of wires. This variability is managed in an automatic way by means of a self-learning subsystem and does not require manual input from the operator or loading new data to the machine. The system is coupled to a connector crimping machine and once the model of a correct cable is learned, it can automatically inspect each cable assembled by the machine. The main contributions of this paper are: (i) the self-learning system; (ii) a robust segmentation algorithm for extracting wires from images even if they are strongly bent and partially overlapped; and (iii) a color recognition algorithm able to cope with highlights and different finishing of the wire insulation. We report the system evaluation over a period of several months during the actual production of large batches of different cables; tests demonstrated a high level of accuracy and the absence of false negatives, which is a key point in order to guarantee defect-free productions. © 2004-2012 IEEE.

Tonello S.,ITRobotics Srl | Zanetti G.P.,ITRobotics Srl | Finotto M.,ITRobotics Srl | Bortoletto R.,University of Padua | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

This paper presents WorkCellSimulator, a software platform that allows to manage an environment for the simulation of robot tasks. It uses the most advanced artificial intelligence algorithms in order to define the production process, by controlling one or more robot manipulators and machineries present in the work cell. The main goal of this software is to assist the user in defining customized production processes which involve specific automated cells. It has been developed by IT+Robotics, a spin-off company of the University of Padua, founded in 2005 from the collaboration between young researchers in the field of Robotics and a group of professors from the Department of Information Engineering, University of Padua. © 2012 Springer-Verlag Berlin Heidelberg.

Yan So E.W.,University of Padua | Munaro M.,University of Padua | Michieletto S.,University of Padua | Tonello S.,ITRobotics Srl | Menegatti E.,University of Padua
Computers in Industry | Year: 2013

In this paper, we present a low-cost and highly configurable quality inspection system capable of capturing 2.5D color data, created using off-the-shelf machine vision components, open-source software libraries, and a combination of standard and novel algorithms for 2.5D data processing. The system uses laser triangulation to capture 3D depth, in parallel with a color camera and a line light projector to capture color texture, which are then combined into a color 2.5D model in real-time. Using many examples of completeness inspection tasks that are extremely difficult to solve with current 2D-based methods, we demonstrate how the 2.5D images and point clouds generated by our system can be used to solve these complex tasks effectively and efficiently. Our system is currently being integrated into a real production environment, showing that completeness inspection incorporating 3D technology can be readily achieved in a short time at low costs. © 2013 Elsevier B.V. All rights reserved.

Agency: European Commission | Branch: FP7 | Program: CP-FP | Phase: FoF.NMP.2013-10 | Award Amount: 2.88M | Year: 2013

This project aims at the development of an automatic quality control and feedback mechanism to improve draping of carbon fibres on complex parts. There is a strong need in the automotive industry for automatic systems that perform quality control and improve draping processes in order to allow high production volumes. The technology that is being developed in the project will include a new sensor system for robust detection of fibre orientation combined with a robotic system to scan complex parts. This is based on a new technology that uses reflection models of carbon fibre to solve the problems encountered with earlier vision-based approaches. The data coming from the inspection system will be fed into draping simulation to improve the accuracy of the processes. Draping is the process of placing woven carbon material on typically complex 3D parts (preforms) with the goal of having the fibres oriented along specific directions predicted by finite element calculations. This is done to maximize the strength-to-weight ratio of the part. There is a strong trend in the automotive industry towards lightweight parts to increase fuel efficiency, also considering the needs of electrical vehicles. Setting up the draping process for a complex part takes up to 50 preforms for trial-and-error improvements. Current production processes are thus not yet adequate to cover the expected volumes of several 100.000 parts per year. The project aims at shortening process development times by 90% and allowing automatic 100% quality control of fibre orientation. The industry-led consortium consists of European key partners in draping simulation, manufacturing of carbon parts for the automotive industry, sensor developers and robotic experts. It is complemented by a group of interested end users, e.g. European car manufacturers that are associated to the project.

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