Modular Robot Task Execution Framework for Industrial PCB Handling: A Behavior Tree Approach

Handling printed circuit boards (PCBs) in industrial environments presents significant challenges due to design variability, high precision requirements, and the need for scalability. Traditional manual workflows are laborintensive and error-prone, while fixed robotic systems lack the adaptability to manage diverse PCB types and task variations. This paper proposes a Behavior Tree (BT)-based framework for automating PCB handling tasks, integrating advanced robotic skills such as detection, grasping, and precise placement. The framework is enhanced by a multimodal Vision System for robust pose estimation, an intuitive Human Interface for task reconfiguration, and a modular Robotic Manipulation Module for reliable execution. The BT-based architecture ensures modularity, and adaptability, addressing the demands of dynamic industrial workflows. Experimental validation demonstrates the framework's ability to handle PCB variability and execute precision-critical tasks efficiently, showcasing its potential for scalable deployment in industrial settings.