AI-driven force torque control strategies for further automate flexible high-precision, contact-intensive assemblies

Abstract

Robotic assembly processes are integral to industrial manufacturing, traditionally relying on pre-programmed sequences that exhibit limited adaptability to uncertain conditions. Recent advancements propose AI-enhanced vision systems as viable solutions to mitigate uncertainty in automated tasks. Despite the potential of vision, contact-intensive, high-precision assembly processes need customized force-torque feedback strategies to perform the task. Parametrization for setup and adaption of force-torque processes are time consuming, but decisive for quality. This work outlines an AI-driven force-torque control strategic solution for peg-in-hole assembly task, which employs object detection algorithm to grasp the workpiece and determine flexible assembly position, with force-torque-informed DNN algorithm for error correction in terms of position, and digital twin to monitor the process and collect virtual data.