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

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.

Vision based quadrotor navigation around objects

This project focuses on the railway inspection scenario where the drone is meant to find and inspect certain target objects. Working with an AR.Drone quadrotor, we intend to have basic GPS waypoint navigation to approach the target object, target object detection and basic control architecture for navigating around the detected target.

Nonlinear control for commercial drones in autonomous railway maintenance

Drones are getting widespread and low-cost platforms already offer good flight and video recording experience. This project intends to use such drones in the context of railway maintenance by developing applications for autonomous navigation in railway environment.

Autonomous Guidance of a quadcopter based on vanishing point detection.

Unmanned aerial vehicles are increasingly being used and showing their advantages in many domains. However, their application to railway systems is very little studied. In this paper, we focus on controlling an AR.Drone UAV in order to follow the railway track.

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.

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