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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Two-stage genetic algorithm optimization of a freeform prism AR system design.

Guanqiao Su, Galina Romanova

|June 10, 2026

Summary

We developed a two-stage genetic algorithm (GA) for designing augmented reality (AR) systems with freeform prisms. This method speeds up initial design and optimizes image quality for AR applications.

Area of Science:

Optics and Photonics
Computer Science
Engineering

Background:

Augmented reality (AR) system design is complex, often requiring multi-stage processes.
Conventional methods involve iterative optimization with specific equations and weights, which can be time-consuming.

Purpose of the Study:

To develop a faster and more efficient method for freeform prism AR system design.
To improve the image quality of AR systems through advanced optimization techniques.

Main Methods:

A two-stage optimization process using genetic algorithms (GA) was employed.
The first stage utilized GA for initial structure search and rough optimization.
The second stage applied a multi-objective GA to enhance image quality.

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Main Results:

The GA method successfully identified a suitable ray-tracing structure.
The multi-objective GA stage significantly improved image quality.
The process was implemented in MATLAB and verified through simulations.

Conclusions:

The proposed two-stage GA method offers an efficient approach to freeform prism AR system design.
This method accelerates the initial design phase and optimizes optical performance.
The simulation results validate the effectiveness of the GA-based design approach.