A list of representative grants that are being run by our group.

Our open and ongoing projects in this area are listed below, together with a selection of completed projects where relevant.

3D object recognition with mobile robots - PN3-Innovative-Cheques

The main aim of the project is the application of the techniques developed by the academic partner in the industrial environment from the beneficiary. The developed open-source modules are reusable and they can be adopted to the needs of the industrial beneficial. In order to close the perception-planning loop active perception techniques are proposed to be used also for the mapping application part. 

Active Perception for Flexible Object Handling in Smart Manufacturing

Intelligent object handling is becoming a must in a smart manufacturing system especially with the recent appearance of the motion compliant dual handed industrial robotic systems. Also the enhanced 3D sensing capabilities from the robotics domain enables us to reconsider our view about the smart manufacturing by enabling on the fly spatial perception of the robot working space.

AUF-RO grant: AI methods for the networked control of assistive UAVs (NETASSIST)

This project develops methods for the networked control and sensing for a team of unmanned, assistive aerial vehicles that follows a group of vulnerable persons. On the control side, we consider multiagent and consensus techniques, while on the vision side the focus is egomotion estimation of the UAVs and cooperative tracking of persons with filtering techniques. NETASSIST is an international cooperation project involving the Technical University of Cluj-Napoca in Romania, the University of Szeged in Hungary, and the University of Lorraine at Nancy, France.

Young Teams grant: Handling non-smooth effects in control of real robotic systems

Robotics has a growing impact on our everyday life. Traditional applications are complemented by the integration of robots in the human environment. With the availability of low cost sensors, aerial robotics also became an active area of research. However, many of the practical challenges associated to the real time control of robotic systems are not yet resolved.

PHC Brancusi grant: Artificial-Intelligence-Based Optimization for the Stable and Optimal Control of Networked Systems (AICONS)

The optimal operation of communication, energy, transport, and other networks is of paramount importance in today's society, and will certainly become more important in the future. Operating these networks optimally requires the effective control of their component systems. Our project AICONS therefore focuses on the control of general networked systems. We consider both the coordinated behavior of multiple systems having a local view of the network, as well as the networked control of individual systems where new challenges arise from the limitations of the network.

Young Teams grant: Reinforcement learning and planning for large-scale systems

Many controlled systems, such as robots in open environments, traffic and energy networks, etc. are large-scale: they have many continuous variables. Such systems may also be nonlinear, stochastic, and impossible to model accurately. Optimistic planning (OP) is a recent paradigm for general nonlinear and stochastic control, which works when a model is available; reinforcement learning (RL) additionally works model-free, by learning from data. However, existing OP and RL methods cannot handle the number of continuous variables required in large-scale systems.

Young Teams grant: Observer design for structured distributed dynamic systems

Power systems, traffic and communication networks, irrigation systems, hydropower valleys, or smart grids are composed of structured interconnections of lower-dimensional subsystems. To monitor such systems, one has to know the values of the variables in the system. Since in general not all these variables can be measured, they must be estimated, based on the system model and available measurements. However, there is no general method to design estimators for nonlinear systems. The challenge of designing an estimator becomes even more difficult if the system is distributed.

Shape Based Active Perception

Perception for artificial systems is increasingly gaining importance especially for systems with multiple sensors. For example, the autonomous vehicles equipped with camera, lidar, inertial measurement units, positioning systems etc are becoming popular. More importantly they are using the information from the multiple sources in the mean time, in the same coordinate system in order to gain information about the environment. Thus, the need for fusing the information from several sources in a common space for reasoning purposes is crucial.

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