Trajectory Generation and Tracking Control of an Autonomous Vehicle Based on Artificial Potential Field and optimized Backstepping Controller

Abstract

This paper presents a global trajectory generation and tracking control algorithms for a tracked unmanned ground vehicle (UGV) in cluttered environment. First, it is assumed that the surrendering environment is fully known. Then, the UGV path is planned based on a modified artificial potential field (APF), for the vehicle to move from the start location to the desired destination while avoiding the collision with the surrounding obstacles. Next, an optimized back-stepping controller is developed to achieve the trajectory tracking control. In order to find the optimum controller's gains, the trajectory tracking problem is solved as an optimization problem where the objective is to minimize the error between the UGV actual and desired positions. The optimization problem is formulated as a sequential quadratic problem (SQP) considering the UGV kinematic and dynamic constraints. Finally, numerical simulations are conducted in order to show the effectiveness of the proposed algorithms.