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Projects

Since 2004, IAS-Lab of the University of Padova realized that the tools and the competencies it has been developing in the field of autonomous robots and artificial intelligence could be applied to industrial robotics. At that time, well in advance on the current trend of Industry 4.0, IAS-Lab understood more intelligence and more perception were needed for industrial robot. For this reason, in 2005, IAS-Lab founded the spin-off company called IT+Robotics srl. The expertise of IAS-Lab on intelligent industrial robots started to grow year after year and IAS-lab was able to win many industrial research grants in the competitive calls of the European Commission.

 

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Fig. 1 - UNIPD-Mask

 

COVID-19 pandemic spread rapidly in Italy in early 2020, with over 200000 positive cases and more than 30000 deaths (Protezione Civile Update). Hospitals faced a shortage of Continuous Positive Airway Pressure (C- PAP) mechanical ventilation masks. This non-invasive treatment offers essential support in the treatment of patients with respiratory difficulties, such as COVID-19 ones, and can potentially avoid their admission to intensive care. Through an airtight connection with patients' airways, C-PAP devices create a constant positive pressure airflow while improving the patients breathing capacity. As a result, it is absolutely necessary to supply C-PAP devices, and this need led to the development of alternative solutions.

The Department of Information Engineering and the Department of Medicine of the University of Padova (Italy) developed UNIPD-Mask (see Fig. 1 and 2): a set of valves that allows converting EasyBreath, the snorkeling mask developed and marketed by Decathlon, into a C-PAP mask (patent pendant n. IT 1020200008305). This repository collects 3D models of the developed parts: https://github.com/iaslab-unipd/UNIPD-Mask.git. They are freely accessible and replicable.

  

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Fig. 2 - UNIPD-Mask real picture

 

The proposed invention follows the idea of Isinnova SRL (Brescia, Italy) in the development of Charlotte and solves Charlotte problems related to the limited section of its air inlet and outlet ducts: during inhalation, systems equipped with Charlotte valve are not able to compensate the volume of air inhaled by the patient, resulting in a pressure drop. Moreover, during exhalation, this configuration does not allow for rapid air evacuation, causing a feeling of fatigue in the patient. UNIPD-Mask, using a double-channel for the incoming airflow, is instead able to provide a greater volume of air to the patient, without a drop in pressure inside the mask during inhalation (see Fig. 3).

 

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Fig. 3 - Comparison between Charlotte (blue) and UNIPD-Mask (red) in terms of pressure variation

 

Finally, UNIPD-Mask adds an anti-suffocation valve which, in the event of an accidental interruption of air and oxygen flow, allows the patient to continue breathing. It is also possible to connect an outgoing air filter.

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The objective of SPIRIT is to take the step from programming of robotic inspection tasks to configuring such tasks. This includes inspection tasks that use image-based sensors and require continuous motion to fully scan a part’s surface.

The project aims at:

• creating fully automatic offline path planning methods that ensure collision-free full coverage of the areas to be inspected on the part also for complex inspection processes. Instability is the maIn cause of falls for the elderly people, which increases the risk of fractures and therefore disability, with high health and social costs. Fall prevention is one of the targets of social and health policies for the promotion of active aging. The SoftAct project aims to meet this need by developing an innovative neuromuscular controller integrated in a "soft exoskeleton" (exosuit: soft wearable robot) for the lower limb, that can detect the loss of stability during walking or standing by the integration of biomechanical, cerebral and muscular signals. This information will activate compensation systems integrated in the soft exoskeleton to prevent possible fall, with an integrated feedback-feedforward system. The project will see the collaboration of two research groups: the Harvard University, with its experience in gait and muscle signal analysis (EMG), that created the prototype of the exosuit, and  the University of Padua, with its strong know-how in the field of brain signal analysis (EEG) and intelligent software for robotics.

• developing reactive inline path planning that is able to automatically adjust to small changes in the environment, such as a different part shape or obstacles.

• a seamless mapping of image sensor data to a 3D model of the part. 

• generating operational data of inspection robots in industrial environments. This will include data related to accuracy, cycle times and performance indicators of the integrated system.

The expected impact is:

• Reducing the engineering costs for setting up a robotic inspection task by 80%

• Creating a software framework that allows the shift from project-based, ad-hoc solutions to a product-based approach.

• Reducing the barrier when introducing automatic inspection systems by aiming at a return of investment of less than 2-3 years.

• Realizing a potential of several hundred additional robotic installations per year.

• Helping SMEs to reach out to worldwide markets by providing a proven framework for inspection robots.

 

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The SPIRIT project had its first meeting in Steyr 27-28 February. The general meeting will be 24-26 July at the University of Padova.

Harvard and Padova Universities to prevent the risk of falling in the elderly

Instability is the main cause of falls for the elderly people, which increases the risk of fractures and therefore disability, with high health and social costs. Fall prevention is one of the targets of social and health policies for the promotion of the active aging.

The SoftAct project aims to meet this need by developing an innovative neuromuscolar controller integrated in a "soft exoskeleton" (exosuit: soft wearable robot) for the lower limb, that can detect the loss of stability during walking or standing by the integration of biomechanical, cerebral, and muscolar signals. This information will activate compensation systems integrated in the soft exoskeleton to prevent possible falls, with an integrated feedback-feedforward system.

The project will see the cooperation of two research groups: the Harvard University, which has an experience in gait and muscle signal analysis (EMG), and which created the prototype of the exosuit, and the University of Padova, with its deep knowledge in the field of brain signal analisys (EEG) and intelligent software for robotics. Prof. Alessandra De Felice of the Neuroscience Department is the coordinator of the project, and is working with the Department of Engineering Information and prof.Emanuele Menegatti.

 

A European Project developing collaborative draping of carbon fiber parts 


DrapeBot

 

Draping is a process used for about 30% of all carbon fibre composite parts to place layers of carbon fiber fabric in a mould. During this process the flat fabric distorts to fit to the shape of the mould. Ensuring the accuracy of draping in terms of position and fiber orientation while avoiding wrinkles is a challenging task.

The DrapeBot project aims at human-robot collaborative draping. The robot is supposed to assist during the transport of the large material patches and to drape in areas of low curvature. The role of the human is to drape regions of high curvature.

To enable an efficient collaboration, DrapeBot develops a gripper system with integrated instrumentation, low-level control structures, and AI-driven human perception and task planning models. All of these developments aim at a smooth and efficient interaction between the human and the robot. Specific emphasis is put on trust and usability, due to the complexity of the task and the sizes of involved robots. The DrapeBot project runs over a period of four years from January 2021 to December 2024.

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eCraft2Learn (http://project.ecraft2learn.eu/) is a two-year Horizon2020 project started the 1st January 2017. The aim of the project is to reinforce personalized learning and teaching in STEAM education and to promote the development of 21st century skills and the employability of students aged 13-17 in the EU. For this purpose, eCraft2Learn will research, design and validate an ecosystem based on digital fabrication and make technologies for creating computer-supported artefacts.

Our role in ECraft2Learn project is the evaluation of suitable open source 3D printers and DIY electronics that will be integrated with the eCraft2Learn ecosystems through an unified user interface, and the planning of the unified ecosystem.

IAS-LAB ranks third at MBZIRC 2017

 

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IAS-LAB of the University of Padua ranked third in The 2017 Grand Challenge of the Mohamed Bin Zayed International Robotic Challenge (MBZIRC) with the unmanned mobile manipulator robot called RUR53. RUR53 is build on a custom modified version of the Summit-XL robot by Robotnik. Desert Lion, the team of IAS-LAB, participated in the Grand Challenge together with the Czech Technical University of Prague and the University of Pennsylvania.

RUR53 is able to navigate inside an outdoor arena; locate and reach a operation panel; locate and recognize a specific wrench; manipulate the wrench to physically operate a valve stem on the panel itself, both autonomously and in teleoperation mode.

MBZIRC, taking place in Abu Dhabi, is a biennal international robotics competition, which attracts the best international teams, by providing a demanding set of benchmark robotics challenges. It aims is to inspire the future of robotics through innovative solutions and technological excellence.

MBZIRC 2017 consist of three challenges and a triathlon type Grand Challenge. IAS-Lab is one of the 46 teams selected in 143 application received from 35 countries. IAS-Lab takes part in the Challenge 2 that requires an unmanned ground vehicle (UGV) with an onboard manipulator to operate a valve stem placed on a panel. To solve the task the robot must

identify the appropriate tool to use, pick it up, and manipulate it to rotate the valve stem one full circle.

 

More information can be found in the web site http://www.mbzirc.com/#

 

winner challenge

 

 

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The RoboESL project born within the project Erasmus+, the EU program in the fields of education, training, youth and sport for the period 2014-2020. The aim of the RoboESL project is to support school to tackle early school leaving (ESL) and disadvantage introducing robot in the extra curriculum activities. RoboESL acts supporting the networking of schools which promote collaborative and holistic approaches to teaching, developing methods and creating conditions for personalised teaching and learning in order to support each student, and developing monitoring and assessment suitable for such approaches.

The project involves Italy, Greece and Latvia.

The role of IAS and Litte Labs is to develop curricula for ten exemplary interdisciplinary robotic projects that will be carried out in the partner schools and to train the teachers in the use of robot.

 

More information can be found in the web site http://roboesl.eu/

focus

 

The FOCUS project aims to support methods for improved exploitation of FoF project results from the chosen five participating FoF clusters. Creating clusters of FoF project activities, according to their objectives and addressed themes, is an effective way to enhance the impact of FoF projects. The five participating clusters in FOCUS will share experiences and best-practices to stimulate the take-up of project results and investigate how to best exploit synergies. Not only within these participating clusters now but foremost to define an approach that can also work for future clusters.

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Dodich - Creation of a machine for viticulture powered by renewable sources.


DODICH Dodich is a project co-financed by European Agricultural Fund for Rural Development (EAFRD).

The DODICH project aims are to develop a cost effective and small sized autonomous machine capable to perform simple operations in vineyards with the possibility to be powered by renewable energy sources. Vineyard's owners could benefit, with this kind of vehicle by either costs reduction on wine production and better safety and quality for the automated operations. The IAS-Lab contribute to this project developing the autonomous navigation algorithms and the software application framework. A further contribute by the IAS-Lab is the development of a wireless battery charger system prototype and a complete autonomous docking algorithm to enable a robot to be 24/7 autonomous and operative.

il 26 Gennaio si è tenuto presso il Centro Interdipartimentale per la Ricerca in Viticoltura ed Enologia di Conegliano, il convegno finale del progetto DODICH. Sono disponibili al seguente link le presentazioni dei relatori: http://www.ethics.it/dodich/presentazioni-convegno-dodich/

Fibremap

 

FibreMap - Automatic Mapping of Fibre Orientation for Draping of Carbon Fibre Parts.



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.

The IAS-Lab contributes to this project developing algorithms for measuring fiber orientation during the draping process. This is done by means of image processing techniques and 3D data modeling for taking into account the perspective effect. The IAS-Lab is also responsible for the development of the experimental workcell, composed of a camera installed on an articulated robot.

More information can be found in the FibreMap web page

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