Optimal nonlinear control with AI techniques

Optimal control can address practical problems appearing in a wide variety of fields, such as automatic control, artificial intelligence, operations research, economics, medicine, etc. In these problems, a nonlinear dynamical system must be controlled so as to optimize a cumulative performance index over time. While optimal solutions have been theoretically characterized starting in the 1950s, computational methods to find them are still a challenging, open area of research.

In this research direction, we focus on the development of methods originating from artificial intelligence and their usage in automatic control. In particular, we are investigating online planning and reinforcement learning techniques, developing fundamental lines like complexity analysis on the one hand, and on the other hand adapting the techniques to solve to open problems in nonlinear control, such as networked or hybrid control systems. On the application side, we are investigating the application of these methods to the control of mobile robotic assistants.

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

1 postdoc, 2 PhDs: Robotic mapping of sea litter using learning and active sensing

We are hiring 1 postdoctoral researcher and 2 PhD students on mapping of sea litter using a mixed team of aerial (quadcopter), surface, and underwater robots; in the framework of the European project SeaClear, http://seaclear-project.eu. We will exploit machine learning and active sensing techniques to map litter both on the sea bottom and at the surface.

H2020 SeaClear - Search, Identification, and Collection of Marine Litter with Autonomous Robots

Litter disposal and accumulation in the marine environment is one of the fastest growing threats to the world's oceans. Plastic is the most common type of litter found on the seafloor, but the list is long and includes glass, metal, wood and clothing. The EU-funded SeaClear project is developing autonomous robots for underwater littler collection using new debris mapping, classification, and collection systems. Specifically, the project will build a mixed team of unmanned underwater, surface and aerial vehicles to find and collect litter from the seabed.

AIRGUIDE: A Learning Aerial Guide for the Elderly and Disabled

Robotic assistants can greatly improve the life of the ever-increasing elderly and disabled population. However, current efforts in assistive robotics are focused on ground robots and manipulators in controlled, indoor environments. AIRGUIDE will break away from this by exploiting unmanned aerial vehicles (UAVs) and their versatile motion capabilities. Specifically, the project will develop aerial assistive technology for independent mobility of an elderly or disabled person over a wide, outdoor area, via monitoring risks and guiding the person when needed.

Learning radio or litter maps with mobile robots

Mobile robots often need to learn an initially uknown map of a position-dependent parameter from sampled values. Examples include learning a map of radio transmission rates, or a map with the density of litter at each point. Moreover, learning this map is often only one part of the robot's task -- the robot may also have a navigation objective, low energy consumption goals, etc. In this project, we aim to design and study a robot motion control strategy that optimally takes into account both map learning and the other objectives of the robot.

Assistive robot arms

Robots that assist elderly or disabled persons in their day-to-day tasks can lead to a huge improvement in quality of life. At ROCON we are pursuing assistive manipulators, as well as UAVs for monitoring at-risk persons. This project focuses on the first direction, and presents a wide range of opportunities for a team of students, starting from low-level control design and vision tasks, to high-level control using artificial intelligence tools. Each student will work on one well-defined subtopic in these areas. Specific tasks include:

AI planning and learning for nonlinear control applications

Planning methods for optimal control use a model of the system and the reward function to derive an optimal control strategy. Here we will consider in particular optimistic planning, a recent predictive approach that optimistically explores  possible action sequences from the current state. Due to generality in the dynamics and objective functions that it can address, it has a wide range of potential applications to problems in nonlinear control.

Sliding mode control of inverted pendulum

This project will develop sliding mode controllers and observers for the Quanser rotational inverted pendulum. The control objective is to stabilize the inverted pendulum at the upward position from a single swing up. The control system should ensure robustness properties in respect with parametric uncertainties, measurement noise, external disturbance, small time delays. Preliminary results will be validated in simulations, after which real-time implementation and validation will be performed.

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.

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.

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.

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