Memory-updated-based Framework for 100% Reliable Flexible Flat Cables Insertion

TL;DR

This paper presents a novel memory-updated framework inspired by human tactile sensing to achieve 100% reliable Flexible Flat Cable insertion in automated assembly lines, significantly improving success rates and error detection accuracy.

Contribution

It introduces a new framework combining tactile sensing, perception, and Bayesian memory modules for reliable FFC insertion, surpassing previous approaches in accuracy and success rate.

Findings

Achieved 97.92% accuracy in detecting alignment errors.

Reached 100% success rate in completed insertions after few iterations.

Demonstrated robustness in complex insertion tasks.

Abstract

Automatic assembly lines have increasingly replaced human labor in various tasks; however, the automation of Flexible Flat Cable (FFC) insertion remains unrealized due to its high requirement for effective feedback and dynamic operation, limiting approximately 11% of global industrial capacity. Despite lots of approaches, like vision-based tactile sensors and reinforcement learning, having been proposed, the implementation of human-like high-reliable insertion (i.e., with a 100% success rate in completed insertion) remains a big challenge. Drawing inspiration from human behavior in FFC insertion, which involves sensing three-dimensional forces, translating them into physical concepts, and continuously improving estimates, we propose a novel framework. This framework includes a sensing module for collecting three-dimensional tactile data, a perception module for interpreting this data into meaningful physical signals, and a memory module based on Bayesian theory for reliability estimation and control. This strategy enables the robot to accurately assess its physical state and generate reliable status estimations and corrective actions. Experimental results demonstrate that the robot using this framework can detect alignment errors of 0.5 mm with an accuracy of 97.92% and then achieve a 100% success rate in all completed tests after a few iterations. This work addresses the challenges of unreliable perception and control in complex insertion tasks, highlighting the path toward the development of fully automated production lines.