A Disassembly Scoring Framework for Human–Robot Collaboration Based on Robotic Capabilities

Abstract

Product disassembly is integral to remanufacturing and recovery operations of end-of-use devices. Traditionally, disassembly has been conducted manually with significant safety risks to human workers. In recent years, robotic disassembly has gained popularity to alleviate human workload and safety concerns. Despite these advancements, robots have limited capabilities in handling all disassembly tasks independently. It is essential to assess whether a robot is capable of performing specific disassembly tasks or not. This study proposes a disassembly scoring framework that evaluates robotic feasibility for disassembling components based on five design-related factors: weight, shape, size, accessibility, and positioning. For each factor, a disassembly score is defined to analyze its specific impact on robotic grasping and placement capabilities. Further, the relationship between the five factors and robotic capabilities, such as grasping and placing, is discussed by an example of the UR5e manipulator. To show the potential for automating the generation of disassembly metric, the Multi-Axis Vision Transformer (MaxViT) model is used to determine component sizes through image processing of the XPS 8700 desktop. Moreover, the application of the proposed disassembly scoring framework is discussed in terms of determining the appropriate work setting for disassembly operations under three main categories: human–robot collaboration (HRC), Semi-HRC, and Worker-Only settings. A disassembly time metric for calculating disassembly time for HRC is also proposed. The study outcomes determine the proper work settings based on the robotic capability.