Dynamic Obstacle Avoidance with Enhanced Social Force Model and DWA Algorithm Using SparkLink.

Area of Science:

• Robotics and Automation
• Industrial Engineering
• Computer Science

Background:

• Industry 4.0 environments present complex challenges for Automated Guided Vehicles (AGVs).
• Dynamic obstacle avoidance is critical for efficient and safe AGV operation in industrial settings.
• Existing methods often struggle with the speed and reliability required for real-time adaptation.

Purpose of the Study:

• To develop an advanced algorithm for dynamic obstacle avoidance in AGVs within Industry 4.0.
• To enhance AGV environmental perception and operational efficiency.
• To leverage novel communication technology for improved data transmission.

Main Methods:

• Integration of an enhanced Social Force Model (SFM) with an improved Dynamic Window Approach (DWA).
• Utilization of SparkLink communication technology to boost data transmission speed and reliability.
• Implementation and testing in complex industrial environments simulating Industry 4.0 conditions.

Main Results:

• The proposed algorithm significantly improves the dynamic obstacle-avoidance performance of AGVs.
• SparkLink technology enhances AGV environmental perception capabilities.
• Experimental validation confirms increased efficiency in AGV operations.

Conclusions:

• The integrated SFM and DWA algorithm, augmented by SparkLink, offers a robust solution for AGV navigation.
• This approach provides significant practical value for optimizing AGV performance in dynamic industrial settings.
• The study contributes to the advancement of intelligent automation in Industry 4.0.