WASP is very proud to have so many excellent researchers involved in the program. More than 450 researchers, reaching from assistant to senior professors, are affiliated with WASP. Some are international recruitments who have come to Sweden to join the WASP community, others are already well established in the Swedish academic system.
Through a series of portraits, you get the opportunity to get to know them a little bit better.
Meet Daniel Axehill
Daniel Axehill is a Senior Associate Professor at the Division of Automatic Control, Department of Electrical Engineering at Linköping University. Dr Axehill joined WASP in 2015.
What is your position/role in WASP?
From a research point of view, I am involved as a supervisor for several WASP PhD students. From an administrative point of view, I am a member of the WASP Graduate School Management group where I represent Linköping University. I am also the examiner for the Autonomous systems course and the Project course within the WASP Graduate School.
Why did you choose to join WASP?
WASP gathers some very interesting and relevant research areas. I think that it is very interesting and important to bring these areas together, in order to learn from each other.
What are the benefits you see in WASP?
WASP has a collection of very good researchers in the considered areas, so it contains both a large amount of knowledge but also interesting people to have in your network. It is also a good funding opportunity if you are interested in doing research in the WASP areas.
Briefly describe your research topic.
I have two main research directions; real-time optimization and (optimal) motion planning. The common denominator is that we are using optimization to compute (usually) a sequence of optimal decisions to solve different tasks. Our focus is on developing the algorithms that actually compute these decisions and also on theory that typically guarantee performance and/or reliable operation. The main application is control problems (including motion planning), but there are other applications as well. The main challenges in real-time optimization are that the problems usually are to be solved very quickly and the systems are embedded so there is usually no possibility for an operator to monitor the algorithms in real-time. The main challenges that we consider within motion planning are relatively complicated dynamics, tightly coupled logic and dynamics, and different forms of uncertainties.
We have recently done significant progress in the area of exact complexity and real-time certification of some commonly used optimization algorithms. We have also developed freely available and high-performing software which makes these results available to both other researchers as well as to industry. When I started this research, I considered it to be of rather high risk, today it is working and the software is easily available at, e.g., github:
I am looking forward to extending the results to more advanced setups in a newly obtained WASP PhD project!
In what way can your research be of importance to our society in the future?
The idea with the combination of automatic control and optimization is to compute optimal decisions how to make something behave as one desires. This is very relevant in many applications, not the least in autonomous systems as well as energy production and optimization.
For more information about Dr Axehill, see https://liu.se/en/employee/danax42
Published: June 26th, 2023