Current Projects

Development of intelligent technologies for autonomous maritm systems (EITAMS)

The autonomous systems research group is part of the EITAMS project. Please refer to the project webpage for further information.

Development and evaluation of an intelligent and distributed sensorsystem for the flexilbe investigation of the marine environment

The long term goal of this research is to develop a flexible, low-cost and autonomous platform for submarine exploration. Such a platform could be used, for example, for locating submarine sources of interest, i.e. dumped waste or lost harmful cargo. Furthermore, the search for submarine groundwater discharges (SGD) could be a possible scenario for such an observatory.

For these project an autonomous surface vehicle (ASV) and an autonomous underwater vehicle (AUV) are currently under development. The ASV was developed within student projects. The AUV based on an off-the-shelf BlueROV 2. The remotely-operated BlueROV 2 was turned into an autonomous operating vehicle. The Figure shows the ASV and the AUV during a first performance test.

This PhD project is carried out in cooperation with the Marine Sensor Systems Group of the Institute of Chemistry and Biology of the Marine Environment (ICBM) at the University of Oldenburg.

Contact:

Prof. Lars Nolle

Duration:

01.08.2016 - 31.07.2021

Funding:

Jade2Pro

Cooperation:

ICBM, Carl von Ossietzky University, Oldenburg, Germany

 

 

 

Automatic Algorithm Design through Quantifying the Features of Optimisation Algorithms

A large number of algorithms for solving optimisation problem has been developed. However, solving any optimisation problem requires selecting a suitable algorithm with a suitable configuration. Different methodologies have been followed to automate deciding on the right algorithm and the right configuration. Hyper-heuristics approaches may not compete with state-of-the-art problem-specific approaches because they focus on the performance of the optimisation algorithms rather than the problem. On the other hand, most of Rice’s features-based model approaches depict the Algorithm Selection (AS) and the Algorithm Configuration (AC) problems as two separated problems. This separation can affect the algorithm efficiency or narrow the range of problems, on which an approach can be applied.

This research project aims to investigate extending Rice’s framework for solving the AS problem to enable producing search results that comparable to that produced by state-of-the-art problem-specific approaches. By incorporating the algorithms’ features into the features-based model, powerful and more general, solver can be developed without losing domain-independence.

Contact:

Dr. Tarek El-Mihoub

Duration:

01.07.2020 - 30.06.2022

Funding:

Philipp Schwartz-Fellowship of Jade University