Adaptive Ant Colony Optimization with Sub-Population and Fuzzy Logic for 3D Laser Scanning Path Planning

Area of Science:

• Metrology and Measurement Science
• Computer Vision and Robotics
• Artificial Intelligence and Optimization

Background:

• Precise measurement of complex surfaces requires accurate positioning and path planning of laser sensor probes.
• Current methods for laser scanning path planning often struggle to optimize measurement velocity and accuracy simultaneously.

Purpose of the Study:

• To propose a novel 3D surface laser scanning path planning technique, termed SFACO (adaptive ant colony optimization with sub-population and fuzzy logic).
• To enhance the accuracy and efficiency of complex surface measurements by optimizing laser sensor probe attitude and scan path.

Main Methods:

• Utilizing a four-coordinate measuring machine and a point laser displacement sensor probe.
• Establishing a coordinate system and transforming measurements to determine the optimal measuring attitude.
• Developing a fuzzy Ant Colony Optimization (ACO) algorithm with adaptive sub-populations and dynamic domain structures (SFACO).
• Creating a nominal distance matrix based on optimal measuring attitudes for path planning.

Main Results:

• The SFACO algorithm demonstrated effectiveness in addressing the path planning problem for 3D surface laser scanning.
• Experimental verification using 13 popular Traveling Salesperson Problem (TSP) benchmark datasets confirmed the algorithm's complexity and efficacy.
• The proposed method successfully integrates measurement point layout, probe attitude, and path planning for improved results.

Conclusions:

• The SFACO algorithm offers a robust solution for optimizing 3D surface laser scanning path planning.
• The integration of fuzzy logic and adaptive sub-populations significantly enhances the performance of Ant Colony Optimization for this application.
• This technique provides a foundation for more accurate and efficient complex surface metrology.