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Investigating the Path Tracking Algorithm Based on BP Neural Network.

Lu Liu^1,2, Mengyuan Xue¹, Nan Guo^1,3

¹School of Engineering, Anhui Agricultural University, Hefei 230036, China.

Sensors (Basel, Switzerland)

|May 13, 2023

Summary

This summary is machine-generated.

This study introduces an adaptive path tracking algorithm using a back propagation (BP) neural network combined with the Pure Pursuit (PP) algorithm. The novel BP-PP approach significantly reduces vehicle path tracking errors in simulations and real-world tests.

Keywords:

BP neural network automated vehicles look-ahead distance path tracking pure pursuit

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Area of Science:

Robotics and Control Systems
Artificial Intelligence
Automotive Engineering

Background:

Vehicle path tracking is crucial for autonomous driving and robotics.
Traditional algorithms like Pure Pursuit (PP) face limitations in accuracy due to nonlinear factors and varying path curvatures.
Improving path tracking performance requires adaptive control strategies.

Purpose of the Study:

To develop and validate an adaptive path tracking algorithm that enhances accuracy and robustness.
To integrate a back propagation (BP) neural network with the PP algorithm for improved steering angle control.
To reduce the impact of nonlinear system dynamics and road curvature variations on tracking performance.

Main Methods:

The study derives the front wheel steering angle using the kinematic model and the PP algorithm.
A BP neural network is trained with vehicle speed, path curvature radius, and lateral error as inputs.
The BP network's output serves as a control coefficient for the PP algorithm, creating the BP-PP algorithm.

Main Results:

Simulation results show reduced average tracking errors compared to traditional methods, particularly on curved paths.
Real vehicle experiments on a rectangular and a large curvature road demonstrated significant error reduction (0.1 m and 0.086 m, respectively).
The BP-PP algorithm exhibited enhanced robustness and performance across various path types and speeds.

Conclusions:

The proposed BP-PP algorithm effectively improves vehicle path tracking accuracy and system robustness.
The algorithm successfully mitigates the influence of nonlinear factors without requiring complex computations.
The BP-PP algorithm has practical applications, as demonstrated by its successful implementation on an autonomous driving patrol vehicle.