Advanced Bayesian Statistical Methods für Fast Prediction of Human Motion in Cooperative Human-Robot Interaction using Space-Time Models

… ist der Titel des Roland-Mittermeir-Preises 2020 und wurde vom Förderverein Technische Fakultät mit EUR 1.500,00 ausgezeichnet. Der Autorin und Preisträgerin, Frau Dipl.-Ing.in Kathrin Spendier BSc, wurde der Preis im Rahmen einer TEWI-Veranstaltung (gemeinsam mit der Vergabe der TEWI-Schüler*innenpreise und Best Performer Awards) am 16. September 2022 übergeben und die Arbeit wird hier kurz vorgestellt:

The increasing demand for collaborative robot systems in industrial applications has led to new and inter-
esting research areas in the field of personal safety. By guaranteeing personal safety, the common working area must be monitored completely by sensors. For this purpose, there are a large number of standard-certified safety devices such as laser scanners or safety light curtains. In addition to the classic safety devices, imaging systems are also tested for their suitability in practical applications. The project comprises the modelling of spatially and temporally variable data in an industrial context. To improve safety between humans and robots, safety devices have to be installed at suitable positions in the room. These sensors have to be analysed in space and time and evaluated on the basis of statistical methods or models, the overall uncertainty should be evaluated. The thesis consists of theoretically processing statistical models to analyze and model human-robot interaction. Among other things, the calculation of the minimum distance between the human and the robot should ensure a safe human-robot relationship.


It is also important to pay attention to the technical specifications and standards which must be fullfilled
by the JOANNEUM RESEARCH ROBOTICS institute. These technical specifications, in conjunction with
the safety requirements, are relevant to the collaborative operation of industrial robots. In order to gain the most precise understanding of the safety of robot systems, the corresponding standards must be worked through. In particular, space-time models for three-dimensional data are examined and processed, which provide information about the safety of human-robot collaboration. The goal is to predict human motion in order to guarantee a confident working atmosphere between a human and the robot. The focus in this work is the usage of Gaussian processes in connection with the efficient Integrated Nested Laplace Approximation (INLA) method, which will be used for model estimation. The developed models are generated on the basis of existing data, with processing of existing R packages. If packages in R are not available yet, suitable algorithms are implemented. The implementation includes data preparation, model estimation and the corresponding application of the model to the data. Finally, a corresponding toolbox is to be set up in R for the robot safety. This is followed by an assessment of the quality and precision of the models and methods from a statistical perspective. The output is very satisfactory and the collected data can be used for further data analysis and human motion predictions.

This project is planned in cooperation with the JOANNEUM RESEARCH institute ROBOTICS on risk mod-
els for autonomous robots in a shared work environment with humans. The practical aspects, corresponding implementations and graphical creations are completely done in R.

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