Safe steering of UGVs in polygonal environments

This paper presents an application of a model predictive control for trajectory generation of an unmanned ground vehicle. An optimal tracking problem while avoiding collision with obstacles is formulated in terms of cost minimization under constraints. The cost function includes terms corresponding to the deviation from the desired trajectory, magnitude of the control input, proximity to the obstacles and the final destination point, respectively. Information on obstacles can be incorporated online in the nonlinear model predictive framework and the resulting constrained optimization problem can be solved using nonlinear programming techniques such as augmented Lagrangian. Then kinematic constraints are treated by the Karush-Kuhn-Tucker (KKT) condition. This approach has been applied for generating safe trajectories for the nonlinear dynamics of a vehicle with a nonlinear tire model in a 2D polygonal environment. Simulation results show that the satisfactory performance was achieved in terms of short and safe trajectory satisfying input constraints.