Optimal Control and Race Line Planning for an Autonomous Race Car

Abstract: In autonomous racing, arguably the most impactful modules in terms of lap time minimization are planning and control. Linköping University's Formula Student team has recently begun an organizational evolution towards building electric and autonomous race cars and this thesis aims to provide a solid foundation for a planning and control solution that the organization can continue to build upon. This thesis compares and evaluates different combinations of planners, controllers, and vehicle models to suggest the combination best suited for a racing scenario. The planners considered in this thesis were two sampling-based planners with different curves connecting the sampled points, and one minimum curvature planner. To control the vehicle to follow the planned trajectory one non-linear model predictive controller (MPC) and one linear MPC were implemented and tested using both a kinematic and a dynamic single-track vehicle model. The optimal combination turned out to be the minimum curvature planner with a non-linear MPC using a kinematic vehicle model.