In this report we compare three different assignment algorithms in how they can be used to assign a set of drones to get to a set of goal locations in an as resource efficient way as possible. An experiment is set up to compare how these algorithms perform in a somewhat realistic simulated environment. The Robot Operating system (ROS) is used to create the experimental environment. We found that by introducing a threshold for the Hungarian algorithm we could reduce the total time it takes to complete the problem while only sightly increasing total distance traversed by the drones.

Indiana University-Purdue University Indianapolis (IUPUI) / This research improves the realism of an electronic warfare (EW) environment involving dynamic motion of assets and transmitters. Particle Swarm Optimization (PSO) continues to be used to place assets in such a manner where they can communicate with the largest number of highest priority transmitters. This new research accomplishes improvement in three areas. First, the previously stationary assets and transmitters are given a velocity component, allowing them to change positions over time. Because the assets now have a starting position and velocity, they require time to reach the PSO solution. In order to optimally assign each asset to move in the direction of a PSO solution location, a graph-based method is implemented. This encompasses the second area of research. The graph algorithm runs in O(n^3) time and consumes less than 0.2% of the total measured computation time to find a solution. Transmitter location updates prompt a recalculation of the PSO, causing the assets to change their assignments and trajectories every second. The computation required to ensure accuracy with this behavior is less than 0.5% of the total computation time. The final area of research is the completion of algorithmic performance analysis. A scenario with 3 assets and 30 transmitters only requires an average of 147ms to update all relevant information in a single time interval of one second. Analysis conducted on the data collected in this process indicates that more than 95% of the time providing automatic updates is spent with PSO calculations. Recommendations on minimizing the impact of the PSO are also provided in this research.

Our research focuses on one of the more fundamental issues in multi-agent, mobile robotics: the formation control problem. The idea is to create controllers that cause robots to move into a predefined formation shape. This is a well studied problem for the scenario in which the robots know in advance to which point in the formation they are assigned. In our case, we assume this information is not given in advance, but must be determined dynamically. This thesis presents an algorithm that can be used by a network of mobile robots to simultaneously determine efficient robot assignments and formation pose for rotationally and translationally invariant formations. This allows simultaneous role assignment and formation sysnthesis without the need for additional control laws. The thesis begins by introducing some general concepts regarding multi-agent robotics. Next, previous work and background information specific to the formation control and assignment problems are reviewed. Then the proposed assignment al- gorithm for role assignment and formation control is introduced and its theoretical properties are examined. This is followed by a discussion of simulation results. Lastly, experimental results are presented based on the implementation of the assignment al- gorithm on actual robots.

Today, inventory management at Marabou is organised in such way that articles are stored based on which production line they belong to and are sent to storage locations close to their production line. However, some storage locations are not optimised, insofar articles are stored out of pure habit and follow what is considered most convenient. This means that the storage locations are not based on any fixed instructions or standard. In this report, we propose optimal storage locations with respect to transportation cost by modelling the problem mathematically as a minimal cost matching problem, which we solve using the so-called Hungarian algorithm. To be able to implement the Hungarian algorithm, we collected data regarding the stock levels of articles in the factory throughout 2016. We adjusted the collected data by turning the articles into units of pallets. We considered three different implementations of the Hungarian algorithm. The results from the different approaches are presented together with several suggestions regarding pallet optimisation. In addition to the theoretical background, our work is based on an empirical study through participant observations as well as qualitative interviews with factory employees. In addition to our modelling work, we thus offer several further suggestions for efficiency savings or improvements at the factory, as well as for further work building on this report. / Idag är lagerhanteringen i Marabou fabriken ordnat på sådant sätt att artiklarna är lagrade utifrån vilken linje den tillhör och därmed står i ett lager nära den specifika linjen. Dock finns det lagerplatser idag som inte är optimerade, i den mån att det endast är lagrade från vana och vad som anses enklast. Därmed är lagerplatserna inte ordnade utifrån någon standard.I detta examensarbete föreslår vi därför de mest optimala lagerplatserna med hänsyn till totala transportkostnaderna. Det här problemet kan modelleras som ett matchningsproblem som kan lösas av en så kallad Ungersk algoritm. Denna ska resultera i den optimala matchningen mellan produktionslinjens behov mot lagerplatserna i fabriken med tillhörande kostnad. För att använda Ungerska algoritmen samlade vi in data av den totala mängd artiklar som fanns i fabriken för 2016, vilket togs fram genom datasystemet SAP som Marabou använder sig av. Därefter justerade vi datat genom att dela upp alla artiklarna i antalet pallar samt vilken linje den tillhör. Denna information kompletterades med empiriska undersökningar genom egna observationer samt kvalitativa intervjuer med de anställda i fabriken. I metoden använder vi tre olika implementeringar av den Ungerska algoritmen. I resultatet presenteras resultaten från de olika tillvägagångsätten tillsammans med flera palloptimeringsförslag. I slutet sammanställs flera förbättringsförslag och idéer om vidareutveckling i rapporten.