Anti-Collision Path Planning and Tracking of Autonomous Vehicle Based on Optimized Artificial Potential Field and Discrete LQR Algorithm
This paper introduces an enhanced APF method to address challenges in automatic lane changing and collision avoidance for autonomous vehicles, targeting issues of infeasible target points, local optimization, inadequate safety margins, and instability when using DLQR. By integrating a distance adjus...
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| Format: | Article |
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MDPI AG
2024-11-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/15/11/522 |
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| author | Chaoxia Zhang Zhihao Chen Xingjiao Li Ting Zhao |
| author_facet | Chaoxia Zhang Zhihao Chen Xingjiao Li Ting Zhao |
| author_sort | Chaoxia Zhang |
| collection | DOAJ |
| description | This paper introduces an enhanced APF method to address challenges in automatic lane changing and collision avoidance for autonomous vehicles, targeting issues of infeasible target points, local optimization, inadequate safety margins, and instability when using DLQR. By integrating a distance adjustment factor, this research aims to rectify traditional APF limitations. A safety distance model and a sub-target virtual potential field are established to facilitate collision-free path generation for autonomous vehicles. A path tracking system is designed, combining feed-forward control with DLQR. Linearization and discretization of the vehicle’s dynamic state space model, with constraint variables set to minimize control-command costs, aligns with DLQR objectives. The aim is precise steering angle determination for path tracking, negating lateral errors due to external disturbances. A Simulink–CarSim co-simulation platform is utilized for obstacle and speed scenarios, validating the autonomous vehicle’s dynamic hazard avoidance, lane changing, and overtaking capabilities. The refined APF method enhances path safety, smoothness, and stability. Experimental data across three speeds reveal reasonable steering angle and lateral deflection angle variations. The controller ensures stable reference path tracking at 40, 50, and 60 km/h around various obstacles, verifying the controller’s effectiveness and driving stability. Comparative analysis of visual trajectories pre-optimization and post-optimization highlights improvements. Vehicle roll and sideslip angle peaks, roll-angle fluctuation, and front/rear wheel steering vertical support forces are compared with traditional LQR, validating the optimized controller’s enhancement of vehicle performance. Simulation results using MATLAB/Simulink and CarSim demonstrate that the optimized controller reduces steering angles by 5 to 10°, decreases sideslip angles by 3 to 5°, and increases vertical support forces from 1000 to 1450 N, showcasing our algorithm’s superior obstacle avoidance and lane-changing capabilities under dynamic conditions. |
| format | Article |
| id | doaj-art-d0233bdf28f0409b897bb1cb00615e17 |
| institution | OA Journals |
| issn | 2032-6653 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-d0233bdf28f0409b897bb1cb00615e172025-08-20T02:04:45ZengMDPI AGWorld Electric Vehicle Journal2032-66532024-11-01151152210.3390/wevj15110522Anti-Collision Path Planning and Tracking of Autonomous Vehicle Based on Optimized Artificial Potential Field and Discrete LQR AlgorithmChaoxia Zhang0Zhihao Chen1Xingjiao Li2Ting Zhao3School of Mechanicalan Electrical Engineering and Automation, Foshan University, Foshan 528225, ChinaSchool of Mechanicalan Electrical Engineering and Automation, Foshan University, Foshan 528225, ChinaCenter of Teacher’s Teaching Development, Guangdong University of Education, Guangzhou 510303, ChinaSchool of Mechanicalan Electrical Engineering and Automation, Foshan University, Foshan 528225, ChinaThis paper introduces an enhanced APF method to address challenges in automatic lane changing and collision avoidance for autonomous vehicles, targeting issues of infeasible target points, local optimization, inadequate safety margins, and instability when using DLQR. By integrating a distance adjustment factor, this research aims to rectify traditional APF limitations. A safety distance model and a sub-target virtual potential field are established to facilitate collision-free path generation for autonomous vehicles. A path tracking system is designed, combining feed-forward control with DLQR. Linearization and discretization of the vehicle’s dynamic state space model, with constraint variables set to minimize control-command costs, aligns with DLQR objectives. The aim is precise steering angle determination for path tracking, negating lateral errors due to external disturbances. A Simulink–CarSim co-simulation platform is utilized for obstacle and speed scenarios, validating the autonomous vehicle’s dynamic hazard avoidance, lane changing, and overtaking capabilities. The refined APF method enhances path safety, smoothness, and stability. Experimental data across three speeds reveal reasonable steering angle and lateral deflection angle variations. The controller ensures stable reference path tracking at 40, 50, and 60 km/h around various obstacles, verifying the controller’s effectiveness and driving stability. Comparative analysis of visual trajectories pre-optimization and post-optimization highlights improvements. Vehicle roll and sideslip angle peaks, roll-angle fluctuation, and front/rear wheel steering vertical support forces are compared with traditional LQR, validating the optimized controller’s enhancement of vehicle performance. Simulation results using MATLAB/Simulink and CarSim demonstrate that the optimized controller reduces steering angles by 5 to 10°, decreases sideslip angles by 3 to 5°, and increases vertical support forces from 1000 to 1450 N, showcasing our algorithm’s superior obstacle avoidance and lane-changing capabilities under dynamic conditions.https://www.mdpi.com/2032-6653/15/11/522path planningartificial potential field algorithmscollision avoidancepath trackinglinear quadratic optimal controller |
| spellingShingle | Chaoxia Zhang Zhihao Chen Xingjiao Li Ting Zhao Anti-Collision Path Planning and Tracking of Autonomous Vehicle Based on Optimized Artificial Potential Field and Discrete LQR Algorithm World Electric Vehicle Journal path planning artificial potential field algorithms collision avoidance path tracking linear quadratic optimal controller |
| title | Anti-Collision Path Planning and Tracking of Autonomous Vehicle Based on Optimized Artificial Potential Field and Discrete LQR Algorithm |
| title_full | Anti-Collision Path Planning and Tracking of Autonomous Vehicle Based on Optimized Artificial Potential Field and Discrete LQR Algorithm |
| title_fullStr | Anti-Collision Path Planning and Tracking of Autonomous Vehicle Based on Optimized Artificial Potential Field and Discrete LQR Algorithm |
| title_full_unstemmed | Anti-Collision Path Planning and Tracking of Autonomous Vehicle Based on Optimized Artificial Potential Field and Discrete LQR Algorithm |
| title_short | Anti-Collision Path Planning and Tracking of Autonomous Vehicle Based on Optimized Artificial Potential Field and Discrete LQR Algorithm |
| title_sort | anti collision path planning and tracking of autonomous vehicle based on optimized artificial potential field and discrete lqr algorithm |
| topic | path planning artificial potential field algorithms collision avoidance path tracking linear quadratic optimal controller |
| url | https://www.mdpi.com/2032-6653/15/11/522 |
| work_keys_str_mv | AT chaoxiazhang anticollisionpathplanningandtrackingofautonomousvehiclebasedonoptimizedartificialpotentialfieldanddiscretelqralgorithm AT zhihaochen anticollisionpathplanningandtrackingofautonomousvehiclebasedonoptimizedartificialpotentialfieldanddiscretelqralgorithm AT xingjiaoli anticollisionpathplanningandtrackingofautonomousvehiclebasedonoptimizedartificialpotentialfieldanddiscretelqralgorithm AT tingzhao anticollisionpathplanningandtrackingofautonomousvehiclebasedonoptimizedartificialpotentialfieldanddiscretelqralgorithm |