Trajectory Following using Nonlinear Model Predictive Control and 3D Point-Cloud-based Localization for Autonomous Driving
In 2019 European Conference on Mobile Robots (ECMR), (ECMR-2019), 04.9.-06.9.2019, Prag, IEEE, Sep/2019.
In autonomous driving, the trajectory follower is one of the critical controllers which should be capable of handling different driving scenarios. Most of the existing controllers are limited to a particular driving scenario and for a specific vehicle model. In this work, the trajectory follower is formulated as a nonlinear model predictive control problem and solved using the multiple-shooting trajectory optimization method, Gauss-Newton Multiple Shooting. This solver has already been used for other control applications and provides the flexibility to use different nonlinear models.
The controller is tested using a retrofitted autonomous driving platform, along with the 3D point-cloud-based mapping and localization algorithms. The nonlinear model being used is a classical kinematic bicycle model. Due to the high nonlinearity between the vehicle inputs, throttle and brake, and the acceleration, the longitudinal speed control uses an additional piece-wise linear mapping. The results from the initial tests, while following a predefined trajectory on a Go-Kart test-track, are evaluated and presented here.