Research on Path Planning and Path Tracking Control of Autonomous Vehicles Based on Improved APF and SMC

Abstract

Path planning and tracking control is an essential part of autonomous vehicle research. In terms of path planning, the artificial potential field (APF) algorithm has attracted much attention due to its completeness. However, it has many limitations, such as local minima, unreachable targets, and inadequate safety. This study proposes an improved APF algorithm that addresses these issues. Firstly, a repulsion field action area is designed to consider the velocity of the nearest obstacle. Secondly, a road repulsion field is introduced to ensure the safety of the vehicle while driving. Thirdly, the distance factor between the target point and the virtual sub-target point is established to facilitate smooth driving and parking. Fourthly, a velocity repulsion field is created to avoid collisions. Finally, these repulsive fields are merged to derive a new formula, which facilitates the planning of a route that aligns with the structured road. After path planning, a cubic B-spline path optimization method is proposed to optimize the path obtained using the improved APF algorithm. In terms of path tracking, an improved sliding mode controller is designed. This controller integrates lateral and heading errors, improves the sliding mode function, and enhances the accuracy of path tracking. The MATLAB platform is used to verify the effectiveness of the improved APF algorithm. The results demonstrate that it effectively plans a path that considers car kinematics, resulting in smaller and more continuous heading angles and curvatures compared with general APF planning. In a tracking control experiment conducted on the Carsim–Simulink platform, the lateral error of the vehicle is controlled within 0.06 m at both high and low speeds, and the yaw angle error is controlled within 0.3 rad. These results validate the traceability of the improved APF method proposed in this study and the high tracking accuracy of the controller.

Path Planning of a Mobile Delivery Robot Operating in a Multi-Story Building Based on a Predefined Navigation Tree

“…In summary, the detection of an obstacle blocking a segment of the planned path requires the classification of this segment as blocked, the recalculation of the graph (Equation ( 3)) and the recalculation of the shortest path to the destination using Dijkstra's algorithm. In future works, the artificial potential field (APF) [124] algorithm will be applied to decide if an obstacle blocking the planned path can be safely avoided [125] using simple maneuvers instead of calculating an alternative path. and then detects that the trajectory from node U6 to node U8 is blocked by an obstacle.…”

Deviation Sequence Neural Network Control for Path Tracking of Autonomous Vehicles

“…For secure and efficient driving, autonomous vehicles need to track precisely the reference trajectory generated by the path planning module. In the past years, to improve the path tracking control performance of AVs, many significant results have been reported with the applications of advanced linear and nonlinear control techniques, such as PID [2,3], LQR [4,5], MPC [6,7] and SMC [8,9]. Model predictive control (MPC) is a highly competitive solution with respect to the other possible control technologies [1].…”