Research on Path Planning and Path Tracking Control of Autonomous Vehicles Based on Improved APF and SMC
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 ina...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-09-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/18/7918 |
| _version_ | 1797576942448279552 |
|---|---|
| author | Yong Zhang Kangting Liu Feng Gao Fengkui Zhao |
| author_facet | Yong Zhang Kangting Liu Feng Gao Fengkui Zhao |
| author_sort | Yong Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T22:00:58Z |
| format | Article |
| id | doaj.art-0058757f56de48b885c4e81628e11a78 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T22:00:58Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0058757f56de48b885c4e81628e11a782023-11-19T12:56:02ZengMDPI AGSensors1424-82202023-09-012318791810.3390/s23187918Research on Path Planning and Path Tracking Control of Autonomous Vehicles Based on Improved APF and SMCYong Zhang0Kangting Liu1Feng Gao2Fengkui Zhao3College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaPath 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.https://www.mdpi.com/1424-8220/23/18/7918autonomous vehiclepath planningpath trackingartificial potential fieldsliding mode control |
| spellingShingle | Yong Zhang Kangting Liu Feng Gao Fengkui Zhao Research on Path Planning and Path Tracking Control of Autonomous Vehicles Based on Improved APF and SMC Sensors autonomous vehicle path planning path tracking artificial potential field sliding mode control |
| title | Research on Path Planning and Path Tracking Control of Autonomous Vehicles Based on Improved APF and SMC |
| title_full | Research on Path Planning and Path Tracking Control of Autonomous Vehicles Based on Improved APF and SMC |
| title_fullStr | Research on Path Planning and Path Tracking Control of Autonomous Vehicles Based on Improved APF and SMC |
| title_full_unstemmed | Research on Path Planning and Path Tracking Control of Autonomous Vehicles Based on Improved APF and SMC |
| title_short | Research on Path Planning and Path Tracking Control of Autonomous Vehicles Based on Improved APF and SMC |
| title_sort | research on path planning and path tracking control of autonomous vehicles based on improved apf and smc |
| topic | autonomous vehicle path planning path tracking artificial potential field sliding mode control |
| url | https://www.mdpi.com/1424-8220/23/18/7918 |
| work_keys_str_mv | AT yongzhang researchonpathplanningandpathtrackingcontrolofautonomousvehiclesbasedonimprovedapfandsmc AT kangtingliu researchonpathplanningandpathtrackingcontrolofautonomousvehiclesbasedonimprovedapfandsmc AT fenggao researchonpathplanningandpathtrackingcontrolofautonomousvehiclesbasedonimprovedapfandsmc AT fengkuizhao researchonpathplanningandpathtrackingcontrolofautonomousvehiclesbasedonimprovedapfandsmc |