Optimal control based maneuvering for virtual vehicle prototypes

This thesis covers the implementation of a collision-free maneuver generation and regulation algorithm for a virtual vehicle in transverse coordinates. The first part of the work is centered around the formulation and solution of the trajectory generation task, which is recast as a nonconvex, nonlinear optimal control problem. Using the CasADi Python library, the problem is solved so to produce feasible and safe trajectories for the vehicle, exploiting a transverse coordinates approach. The generated optimal trajectories are given to a commercial simulation software, to perform software-in-the-loop experiments with a virtual MPC-based driver. The generated trajectories are then transformed from the spatial to the temporal domain through linear interpolation, a procedure that allows to obtain time-dependent optimal state-input profiles. Successively, a closed-loop time-varying maneuver regulation controller is designed, by firstly performing a projection of the vehicle state on the optimal trajectory, and then linearizing the transverse dynamics around the optimal solution. Results are finally demonstrated for several types of reference maneuvers, highlighting how the vehicle can follow a trajectory that safely navigates around both static and moving obstacles while maintaining minimal control effort.