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

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.

Figures (14)

• Figure 1: Overview of the automated disassembly system.
• Figure 2: Required manipulation precision of $\pm 0.75$ mm for screw removal within the workspace ($900 \times 400 \times 750$ mm, width $\times$ depth $\times$ height).
• Figure 3: Associated challenges in the proposed system.
• Figure 4: Screws existing in actual recycling factory.
• Figure 5: Proposed method for detecting screws with variable visual conditions.