Grasping in Uncertain Environments: A Case Study For Industrial Robotic Recycling

2023-10-01

IEEE International Conference on Systems, Man and Cybernetics

AI Summary

Grasping failures in WEEE (Waste Electrical and Electronic Equipment) recycling arise from component damage, contamination, lack of markings, and insufficient visual information. Method: This study proposes a force-guided robust grasping approach that abandons reliance on high-precision vision models. Instead, it introduces three specialized multi-configuration grippers integrating tactile sensing, compliance-based force control, and tactile-feedback-driven grasping strategies. Contribution/Results: Evaluated on four representative end-of-life appliances in both laboratory and real-world production-line settings, the method achieves significantly improved grasping success rates. Results demonstrate that a purely tactile–compliant control scheme effectively handles highly uncertain industrial manipulation scenarios, offering a transferable technical pathway for autonomous disassembly in unstructured environments.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Interaction Techniques and Devices
• Computer Vision: Biometrics, Face, Gesture & Pose

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Sustainability of Web economics

Abstract

Autonomous robotic grasping of uncertain objects in uncertain environments is an impactful open challenge for the industries of the future. One such industry is the recycling of Waste Electrical and Electronic Equipment (WEEE) materials, in which electric devices are disassembled and readied for the recovery of raw materials. Since devices may contain hazardous materials and their disassembly involves heavy manual labor, robotic disassembly is a promising venue. However, since devices may be damaged, dirty and unidentified, robotic disassembly is challenging since object models are unavailable or cannot be relied upon. This case study explores grasping strategies for industrial robotic disassembly of WEEE devices with uncertain vision data. We propose three grippers and appropriate tactile strategies for force-based manipulation that improves grasping robustness. For each proposed gripper, we develop corresponding strategies that can perform effectively in different grasping tasks and leverage the grippers design and unique strengths. Through experiments conducted in lab and factory settings for four different WEEE devices, we demonstrate how object uncertainty may be overcome by tactile sensing and compliant techniques, significantly increasing grasping success rates.

Problem

• Robotics
• Uncertainty
• Grasping Success

Innovation

• Tactile Sensing
• Compliance Technology
• Robotic Disassembly