Granular materials, large collections of macroscopic particles, are something that is commonly found in both nature and industry. Examples of such can be sand, ore, grains, seeds or snow. Simulations of granular materials are important in many industrial cases. It gives an opportunity to study the behavior of the particles as they interact with machinery and gives an indication of how efficient new designs perform. In some areas, such as vehicle-terrain interaction, plastic deformation of the particles can be an important factor. The Ume˚a based company Algoryx Simulation can simulate granular materials in their physics engine AGX Dynamics using a nonsmooth discrete element approach (NDEM), but currently lack support for plastic deformation. The purpose of this thesis is to implement a plastic contact model in the source code of AGX Dynamics, such that plastic deformation can be observed. The implementation was first tested for single particle-particle compression where measured contact forces were compared to theoretical models. Uniaxial compression tests were performed for bulk testing, filling a cylinder with particles and compressing them while monitoring the axial stress and strain. The results from the single particle compression correspond well to theory, giving the correct plastic deformation for a given contact force and correctly illustrates the effects of changing different model parameters. Plastic deformation could also be observed in the results from bulk testing. Additionally, it was observed that the current version of the implementation is best suited for simulating either very cohesive materials, where particles stick to each other when colliding, or cohesionless materials, where colliding particles are separated after impact. Additional research is needed to study how the separation velocity for colliding particles should be updated in a way that is consistent with the plastic model parameters and experimental results.

The demand for collaborative, easy to use robots has increased during the last decades in hope of incorporating the use of robotics in smaller production scales, with easier and faster programming. Artificial intelligence (AI) and Machine learning (ML) are showing promising potential in robotics and this project has attempted to automatically solve a specific assembly task with Behavior trees (BTs). BTs can be used to elegantly divide a problem into different subtasks, while being modular and easy to modify. The main focus is put towards developing a Genetic algorithm (GA), that uses the fundamentals of biological evolution to produce BTs that solves the problem at hand. As a comparison to the GA result, a so-called Automated planner was developed to solve the problem and produce a benchmark BT. With a realistic physics simulation, this project automatically generated BTs that builds a tower of Duplo-like bricks and achieved successful results. The results produced by the GA showed a variety of possible solutions, a portion resembling the automated planner's results but also alternative, perhaps more elegant, solutions. As a conclusion, the approach used in this project shows promising signs and has many possible improvements for future research.