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Improved Immune Moth-Flame Optimization Based on Gene Correction for Automatic Reverse Parking.

Gang Liu^1,2, Xinli Xu³, Longda Wang⁴

¹College of Engineering, Inner Mongolia Minzu University, Tongliao 028000, China.

Sensors (Basel, Switzerland)

|April 13, 2024

Summary

This summary is machine-generated.

This study introduces an improved moth-flame optimization algorithm for smoother, shorter automatic reverse parking trajectories. The new method enhances trajectory optimization and tracking control accuracy, ensuring safer parking maneuvers.

Keywords:

adaptive inertia weight coefficient automatic reverse parking gene correction immune moth–flame optimization parameter optimization reference trajectory optimization

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Area of Science:

Robotics
Artificial Intelligence
Control Systems Engineering

Background:

Achieving precise reference trajectories during automatic reverse parking is challenging, often leading to collisions or suboptimal paths.
Existing methods struggle to simultaneously optimize trajectory smoothness, length, and berthing angle.

Purpose of the Study:

To develop an optimized reference trajectory for automatic reverse parking systems.
To improve trajectory smoothness, reduce length, and minimize berthing inclination angle.
To enhance collision avoidance and tracking control accuracy.

Main Methods:

An improved immune moth-flame optimization algorithm incorporating gene correction was developed.
A reference trajectory optimization model was constructed using cubic spline fitting and a boundary-crossing solution.
Nonlinear decline strategies and a fusion-distance-based solution-set maintenance mechanism were integrated to boost global optimization.

Main Results:

The proposed algorithm demonstrated higher accuracy in reference trajectory optimization.
Effective tracking control of the optimized reference trajectory was achieved.
Experimental validation was conducted at a specific garage location.

Conclusions:

The improved immune moth-flame optimization method offers a superior approach for automatic reverse parking trajectory planning.
The method effectively addresses challenges in smoothness, length, and inclination angle optimization.
Enhanced accuracy in trajectory optimization and tracking control contributes to safer and more efficient autonomous parking.