Combining local trajectory planning and tracking control for autonomous ground vehicles navigating along a reference path

In this paper, we develop an integrated local trajectory planning and control scheme for the navigation of autonomous ground vehicles (AGVs) along a reference path with avoidance of static obstacles. Instead of applying traditional cross track-based feedback controllers to steer the vehicle to track the reference path as closely as possible, we decompose the path following task into two subtasks. Firstly, in order to follow the reference path with smooth motions and avoid obstacles as well, we apply an efficient model-based predictive trajectory planner, which considers geometric information of the desired path, kinematic constraints and partial-dynamic constraints to obtain a collision-free, and dynamically-feasible trajectory in each planning cycle. Then, the generated trajectory is fed to the low-level trajectory tracking controller. Relying on the steady-state steering characteristics of vehicles, we develop an internal model controller to track the desired trajectory, while rejecting the negative effects resulting from model uncertainties and external disturbances. Simulation results demonstrate capabilities of the proposed algorithm to smoothly follow a reference path while avoiding static obstacles at a high speed.