LAPSE:2023.36385
Published Article
LAPSE:2023.36385
Research on Path Planning and Tracking Control of Autonomous Vehicles Based on Improved RRT* and PSO-LQR
July 13, 2023
Path planning and tracking control are essential parts of autonomous vehicle research. Regarding path planning, the Rapid Exploration Random Tree Star (RRT*) algorithm has attracted much attention due to its completeness. However, the algorithm still suffers from slow convergence and high randomness. Regarding path tracking, the Linear Quadratic Regulator (LQR) algorithm is widely used in various control applications due to its efficient stability and ease of implementation. However, the relatively empirical selection of its weight matrix can affect the control effect. This study suggests a path planning and tracking control framework for autonomous vehicles based on an upgraded RRT* and Particle Swarm Optimization Linear Quadratic Regulator (PSO-LQR) to address the abovementioned issues. Firstly, according to the driving characteristics of autonomous vehicles, a variable sampling area is used to limit the generation of random sampling points, significantly reducing the number of iterations. At the same time, an improved Artificial Potential Field (APF) method was introduced into the RRT* algorithm, which improved the convergence speed of the algorithm. Utilizing path pruning based on the maximum steering angle constraint of the vehicle and the cubic B-spline algorithm to achieve path optimization, a continuous curvature path that conforms to the precise tracking of the vehicle was obtained. In addition, optimizing the weight matrix of LQR using POS improved path-tracking accuracy. Finally, this article’s improved RRT* algorithm was simulated and compared with the RRT*, target bias RRT*, and P-RRT*. At the same time, on the Simulink−Carsim joint simulation platform, the PSO-LQR is used to track the planned path at different vehicle speeds. The results show that the improved RRT* algorithm optimizes the path search speed by 34.40% and the iteration number by 33.97%, respectively, and the generated paths are curvature continuous. The tracking accuracy of the PSO-LQR was improved by about 59% compared to LQR, and its stability was higher. The position error and heading error were controlled within 0.06 m and 0.05 rad, respectively, verifying the effectiveness and feasibility of the proposed path planning and tracking control framework.
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Keywords
autonomous vehicle, linear quadratic regulator, Particle Swarm Optimization, path planning, RRT*, tracking control
Subject
Suggested Citation
Zhang Y, Gao F, Zhao F. Research on Path Planning and Tracking Control of Autonomous Vehicles Based on Improved RRT* and PSO-LQR. (2023). LAPSE:2023.36385
Author Affiliations
Zhang Y: College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
Gao F: College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
Zhao F: College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
Gao F: College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
Zhao F: College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
Journal Name
Processes
Volume
11
Issue
6
First Page
1841
Year
2023
Publication Date
2023-06-19
ISSN
2227-9717
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Original Submission
Other Meta
PII: pr11061841, Publication Type: Journal Article
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Published Article
LAPSE:2023.36385
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External Link
https://doi.org/10.3390/pr11061841
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[v1] (Original Submission)
Jul 13, 2023
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Jul 13, 2023
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https://psecommunity.org/LAPSE:2023.36385
Record Owner
Calvin Tsay
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