Industrial-Grade Robust Robot Vision for Screw Detection and Removal under Uneven Conditions
arXiv:2603.29363v1 Announce Type: new 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
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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.
Comments: 19 pages, 14 figures
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2603.29363 [cs.RO]
(or arXiv:2603.29363v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2603.29363
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Takuya Kiyokawa [view email] [v1] Tue, 31 Mar 2026 07:36:12 UTC (26,363 KB)
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