Research on high-precision localization method for transport robots in industrial environments based on Improved AMCL...

Area of Science:

• Robotics
• Industrial Automation
• Sensor Fusion

Background:

• Handling robots are crucial in industrial settings.
• Existing positioning systems face challenges in accuracy and cost.
• High-precision localization is essential for efficient robotic operations.

Purpose of the Study:

• To develop a cost-effective, high-precision positioning scheme for industrial handling robots.
• To address limitations in current robot localization accuracy and cost.
• To enhance the performance of autonomous mobile robots in industrial environments.

Main Methods:

• Utilized the Cartographer algorithm for multi-sensor data fusion and map accuracy improvement.
• Proposed an improved adaptive Monte Carlo localization (AMCL) algorithm for enhanced global localization.
• Integrated a 2D code (QR code) assisted positioning system for fine-tuning local accuracy.
• Employed the YOLO Fastest algorithm for efficient QR code recognition via DNN inference.

Main Results:

• Achieved high-precision positioning with minimal error in industrial environments.
• Demonstrated positioning errors of ±0.068 m (x-direction) and ±0.069 m (y-direction).
• Reported a heading angle error of ±0.107 radians.
• Validated the effectiveness of the proposed multi-sensor fusion and QR code assistance approach.

Conclusions:

• The proposed scheme offers a viable low-cost solution for high-precision robot positioning.
• This approach significantly improves the accuracy and efficiency of handling robots in industrial settings.
• The study contributes to advancing cost-effective control methods for industrial robotics.