This thesis presents a computational framework aimed at enabling the analysis and modeling of boomerangs from example shapes. The goal is to provide a systematic and data-driven tool for boomerang design based on real-world geometries. A key challenge in this context is establishing accurate shape correspondences between handcrafted, asymmetric, and aerodynamically functional boomerangs.
To address this, the proposed pipeline integrates multiple components: landmark-based pre-alignment, boundary extraction using alpha shapes, curve parameterization, and Least Squares Conformal Mapping (LSCM) to compute surface correspondences. Building on these correspondences, the framework further incorporates principal component analysis (PCA) and Free-Form Deformation (FFD) to enable the generation of new shapes.
Experimental results show that the method achieves low Hausdorff and Chamfer distances and has been evaluated using area- and shear-based distortion metrics. Nonetheless, some localized inaccuracies - particularly near high-curvature regions - highlight areas for improvement in boundary handling and local control. Additional limitations include the reliance on manual landmark selection, the linearity of PCA, and the sensitivity of the pipeline to alpha shape parameters.
By combining elements of computational geometry with principles of aerodynamic design, this research bridges the gap between empirical craftsmanship and formal shape analysis. The resulting methodology not only enables the comparison and modeling of boomerangs but also lays the groundwork for future tools in boomerang shape exploration and design.

In the field of cooperative AI, an environment is created called Overcooked AI based on the popular Overcooked game. Originally the environment is used to study deep reinforcement learning, on the other hand it also allows for cooperative planning methods of which the paper will focus on. These methods include coupled based planning with replanning and model-based planning. This research paper attempts to reproduce the results the Overcooked AI environment developers obtained and to improve the Coupled Planning algorithm to gain higher results. In particular, experiments were performed against themselves and a human model for the planning methods, and an improved coupled planning algorithm, in which the failures are handled by deviating from optimal play, under different game steps. And a study on collision failures is performed. The results concluded that extrapolation of results are sub-optimal and that collision failures can be significantly reduced by handling collision differently; walking into the opposite direction.