Vision-Enhanced System For Human-Robot Disassembly Factory Cells: Introducing A New Screw Dataset

Abstract

Waste from electrical and electronic equipment is exacerbating the global environmental crisis. There is an urgent need to build a robust infrastructure capable of providing effective e-waste disposal options. In this work, a novel hybrid human-robot and system-agnostic application for relevant waste disassembly and recycling has been developed. Working on cells, collaborative robots, enhanced with state-of-the-art computer vision capabilities, can achieve near-real-time performance and high precision in the disassembly process. Additionally, a new screw dataset suitable for three separate computer vision tasks, namely instance segmentation, object detection, and semantic segmentation, is introduced to facilitate future research, which can be utilized almost for any screwing/unscrewing application beyond the current disassembly topic. Experiments demonstrating the robustness of the visual object detection and robotic 3D deprojection modules, which are the core aspects of the proposed architecture, have been conducted.