Some Past Research Grands: AGATE, SCIPA, CATIIS, A-ROADS, ADEPT, MIRA, RLN Negotiation Framework, ICARUS, ARGUMENT, AGENT-FISHBANK, AGCOR, COOP…
The main goal of ERRIC is to achieve excellence in research in selected areas of Intelligent Information Technologies: agreement technologies, semantic and collaborative technologies for the web, advanced grid technologies, large scale distributed system services, and adaptive intelligent control.
FP7-ICT No. 264207, 2010-2013.
A robotic platform designed to be used in a heterogeneous robotic swarm and that has many hardware and software state of the art features. Internal project, since 2015.
The aim of the AmIciTy initiative is to create a software infrastructure for Ambient Intelligence applications, that handles context at its constructive level, that works naturally with the user’s tasks and activities, and that manifests the robustness and reliability that is required for future ambient systems. Internal project, since 2014.
Mobile@Old extracts a mobility pattern for each user and generates remainders and alerts regarding his/her physical and other daily activities.
In case of low physical activity, the user is advised to increase the level of physical exercise.
The exercises are performed interactively, as an adaptive game, specifically designed for elderly persons.
The intensity level of the recommended physical exercises is generated and adapted based on both vital parameters of the user (analyzed using a medical expertise) and also on the observed behavior by a team of psychologists.
Mobile@Old is financed by PCCA programme, PNII, duration: 2014-2017.
IONIS will offer fully integrated and validated solutions for health monitoring, home automation, personal agenda with reminders, alerts, caregiver administrative tools. The platform will integrate technologies and services that can address dementia specific challenges and offer support to both caretakers and caregivers: location based services for indoor localization, LBS for object localization by integration of own solutions based on embedded Linux and commercial Bluetooth tags, LBS for outdoor localization and geofencing to allow dementia sufferers to enjoy outdoor activities and perform essential task such as shopping or visiting, alerting and easy communication through one-button calls to designated caregivers, sleep quality monitoring through non-wearable sensors, recommending outdoor activities according to the level of indoor effort extracted from mobility patterns and sleep quality. AI-MAS is member of the consortium.
The IONIS project is founded by the EU Active and Assisted Living Programme, duration: 2017-2020.
INCARE product is a software platform compatible with health devices, home automation sensors, robots and dedicated to the elderly and older adults living independently or in elderly care facilities. Our platform aims to help them live longer, more connected and more independent.
INCARE will turn national and European funded projects into viable products by building on two successful solutions developed within previous AAL and European projects. Both NITICS, funded within the AAL 2012 call, and RAPP, funded 2013-2016 through the EC through the 7th Framework Programme FP7, were designed to support elderly people to live independently in their home environment and social circle. Read more about these projects at NITICS and RAPP.
The INCARE project is partially funded by the Active and Assisted Living (AAL) – European Commission.
The objective of this project is the creation of an open source context management middleware solution presenting rich features that alleviate context-aware application development. Based on the existing platform CONSERT, developed in the AIMAS laboratory, the work improves upon CONSERT with: performance improvement and modularisation of the reasoning engine to support applications of variable complexity requirements, development of an IDE for context modeling and deployment to facilitate context-aware application programming based on the CONSERT Middleware, and advanced options for OSGi and Docker based deployments of CONSERT processing units to address dynamic change of context within an application.
The CONSERT project is financed by “Exploratory Grants” programme, PNIII, duration: 2016-2018.
SPARC is developed in cooperation with CITST company that aims at exploiting robotic technology as a service offer for its potential clients. The envisaged applications are product promotion and/or clients’ assistance in public places such as malls, museums, exhibitions by means of an autonomous robot. In this context, the main goal of the project is the design and implementation of a robotic platform to achieve integrated planning of robot tasks and robot interaction with the clients.
The SPARC project is financed by “Bridge Grant” programme, PNIII, duration: 2016-2018.
AI4GENDARED: Improving the Functional Capabilities of the GENDARED Application by using Artificial Intelligence Algorithms
AI4GENDARED (Imbunătățirea Capacităților Funcționale ale aplicației GENDARED prin utilizarea Algoritmilor de Inteligență Artificială) aims to improve the Functional Capabilities of the GENDARED Application by using Artificial Intelligence Algorithms.
The Intelligent Agent for Recipe Recommendation Based on Visual Identification of Available Ingredients (IntAli) project has as objectives the development of a software agent capable of recommending recipes to users based on what ingredients they have available in their fridge, to research, propose and validate methods for image-based recognition of ingredients placed on shelves of a fridge. In addition, IntAli has as objectives to create semantic models of the knowledge in the project for information querying and extraction, to build recommender systems for recipes, to analyze user interaction and feedback, as well as to design and build a prototype smart-fridge which integrates the sub-systems proposed in the project.
Subsidiary Project: 20175/30.10.2019, part of NETIO P_40_270 53/05.09.2016.
Improving the understanding of the scene by recognizing some Human Activities by the Assistive Robots
The scope of the project is to monitor daily activities and to recognize possible critical and dangerous situations (falls of the elderly).
Subsidiary project: 1268/22.01.2018, Deployment period: 22.01.2018 – 31.12.2020.
The scope of the project is to create an integrated and easily configurable solution for customizing social and assistive robots, using artificial intelligence methods that can provide a cognitive and autonomous character to robots.
Subsidiary project: 1226/22.01.2018, Deployment period: 22.01.2018 – 31.12.2020.
The employability of IT graduates is a challenge in the context of a rapidly evolving environment featuring novel and unexpected ideas and technologies.
Our target group are young, local and international IT master students and future IT leaders who wish to benefit from the experience of researchers and industry specialists, during seminars in which industry and academia collaborate for a shared curriculum.
The consortium is formed of 8 partners from 4 European countries: 4 leading Universities and 4 Corporate partners.
The project will organize 4 seminars with international students.
The project is Co-funded by the Erasmus+ Programme of the European Union.
Nemodrive brings together a group of motivated researchers with the required resources and support to build a self-driving car that will be tested on the UPB campus streets. The prototype will be developed to tackle the particularities of campus and other local roads. In this context, some of the most recurring challenges of self-driving cars will be empirically put to test, in a small scale, un-controlled environment.
Nemodrive is founded on a desire that drives both its theoretical as well as empirical aspirations: to continuously drive interest and understanding of machine learning, duration: 2018-2020.
CAMI is offering a fully integrated Ambient Assisted Living solution by offering services for health management, home management and wellbeing (including socialization, and reduced mobility support). CAMI builds an artificial intelligence ecosystem, which allows seamless integration of any number of ambient and wearable sensors with a mobile telepresence system endowed with multimodal interaction (touch, voice, person detection), including a telepresence robot. This will allow older adults to self-manage their daily life and prolong their involvement in the society while allowing their informal caregivers to continue working whilst caring for their loved ones.
The consortium comprises 8 partners from Romania, Sweden, Switzerland, Denmark, and Polland. AI-MAS is the consortium coordinator.
The CAMI project is founded by the EU Active and Assisted Living Programme (Call 2014 – “Care for the Future”), duration: 2015-2018.
ROBIN is a user centered project which develops systems and services for the use of robots in an interconnected digital society. It comprises 5 sub-projects. ROBIN-Social develops integrated and configurable solutions for the personalization of assistive and social autonomous robots. ROBIN-Car develops computer vision methods devoted to autonomous driving and a prototype systems to be tested on an electric car. ROBIN-Context creates a support platform for the semantic representation and management of data that becomes context in scenarios of personalized robotic assistance and AADS. ROBIN-Dialog develops a set of scenarios for micro-worlds and the technology for the Romanian language processing to achieve situational dialogs in these micro-worlds. ROBIN-Cloud builds a support platform for collecting data coming from the sensors of robotic systems and IoT devices that offers Cloud Edge and Cloud Robotics computing.
The ROBIN project is financed by UEFISCDI “Complex Projects” programme, PNIII, duration: 2018-2020.
Detecting, tracking, and recognizing people is a valuable capability for machines. However, these tasks are quite difficult to be achieved autonomously and, although significant results have been obtained, they are still a major technological challenge. People tracking, unlike other recognition and interpretation tasks, is difficult both from the point of view of the recognition and prediction of trajectory, and from the one of the identifications of the ground truth.
The main objective of the PETRA project is the development of a software platform enabling the development of applications requiring people detection and tracking in real environments. The design and implementation of the platform and the set of supported algorithms for people detection and tracking will be such that they can be easily used and integrated in tasks performed by social robots in closed spaces and in tasks in open spaces, such as the case for pedestrian detection and tracking. The scientific and technological challenge of the project is to start from our current developments on people detection and tracking in the contexts of user-robot interaction and autonomous driving to develop and implement novel solutions based on deep learning approaches. One of the project challenges is to extensively test the implementations towards several difficult benchmarks, but also on our own data sets, and strive to achieve results better than current state-of-the-art.
In healthcare, the cost of treating brain disorders accounts for a significant percentage of total treatment costs. In Europe, the budget for treating brain disorders alone far surpasses the combined budget for other diseases. Utilising cutting-edge AI, the EU-funded ALAMEDA project could greatly reduce the cost of treating these conditions by enabling personalised care and improved treatments for major brain disorders. The goal is to demonstrate AI-enabled prediction, prevention and intervention, making the treatment of disorders such as Parkinson’s, multiple sclerosis and strokes more affordable and easing the burden on healthcare systems across Europe.
ALAMEDA is a truly pan-European project and brings together a highly skilled and complementary consortium of 15 expert organisations from across 8 countries. University Politehnica of Bucharest is coordinating the WP3.
The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101017558.