Optimal motion generation for autonomous vehicle in maze-like environment

In car-like autonomous vehicle systems, it is an essential task of generating the motion commands according to a given path/strategy. Quite a few theories and techniques have been proposed for the generation of the motion commands in autonomous vehicles, such as pure pursuit, Stanley´s nonlinear feedback, chained-form control of kinematic model and the linearized optimal control of dynamic model. Here a non-linear optimization algorithm based on the vehicle´s kinematic model and the actuators´ model is proposed, which combines the control system dynamic behaviors and gives out the control sequences directly. It starts with modeling the local kinematic behavior and actuators´ dynamics. Then online optimization algorithm is applied to the objective function of minimizing the energy cost, execution time and tracking error with some trade-off weights among them. The experiments showed that it worked well for vehicles running in maze-like environment.