Industrial-Grade Robust Robot Vision for Screw Detection and Removal under Uneven Conditions

TL;DR

This paper presents an industrial-grade robot vision system for screw detection and removal in recycling, achieving high accuracy and success rates under challenging conditions.

Contribution

It introduces a novel two-stage detection and local calibration method that enhances robustness and precision in screw disassembly tasks.

Findings

Screw detection recall of 99.8% under severe degradation

Disassembly success rate of 78.3% in real-world tests

Average cycle time of 193 seconds

Abstract

As the amount of used home appliances is expected to increase despite the decreasing labor force in Japan, there is a need to automate disassembling processes at recycling plants. The automation of disassembling air conditioner outdoor units, however, remains a challenge due to unit size variations and exposure to dirt and rust. To address these challenges, this study proposes an automated system that integrates a task-specific two-stage detection method and a lattice-based local calibration strategy. This approach achieved a screw detection recall of 99.8% despite severe degradation and ensured a manipulation accuracy of +/-0.75 mm without pre-programmed coordinates. In real-world validation with 120 units, the system attained a disassembly success rate of 78.3% and an average cycle time of 193 seconds, confirming its feasibility for industrial application.