One commonly used representation of complex objects in physics-based simulations are triangle meshes. This representation utilizes a collection of triangles to approximate an object. An alternative representation is a Signed Distance Field (SDF). This thesis aims to evaluate the effectiveness of representing a heavy machine bucket as an SDF, specifically in the application of collision detection with a de-formable terrain. Additionally, this thesis describes the implementation of two collision detection routines which uses SDFs to detect collisions with spheres and heightfields. The SDFs are stored using two alternative spatial data structures, a uniform grid and an octree. The implementations are compared against a triangle mesh representation. While there are limitations to the SDF representation, such as the need for high resolutions to capture fine details or that small features may become heavily distorted, the benefits of using SDFs include the ability to perform point to distance queries and provide a robust description of an object’s interior and exterior. The findings of this study showed that the SDF stored in a uniform grid demonstrated better performance in the benchmarks and was able to reproduce comparable data to the triangle mesh in the digging tests. These results indicate that the SDF representation could be a promising alternative to the triangle mesh representation. However, further development and research are required.