Path Planning and Tracking Algorithms for Autonomous Off-Road Vehicles

The increasingly diffusion of autonomous driving has transformed the mobility framework, sparking heightened interest among both academic and industrial researchers in the field of autonomous vehicles. The main goal of this paper is the implementation and integration of path planning (PP) and path tracking (PT) algorithms to enable the vehicle to fully cross an obstacle-filled map. This technique can be adopted both in case of on-road mobility and off-road scenarios, where road natural unevenness are treated as equivalent virtual obstacles. The adopted path planning algorithm has been optimized for the generation of the shortest and best feasible trajectory based on the vehicle dynamics constraints. The resulting path is then input to the path tracking logic, configured as a Linear Quadratic Regulator controller whose design is based on a linearized vehicle model. A speed controller is also designed and implemented to improve the vehicle roll dynamic and safety. The controller is then applied to a higher fidelity vehicle model developed in IPG-CarMaker environment, thus validating the methodology through a simulation assessment approach. Optimal results confirm the efficacy of the proposed path planning and tracking strategies.