Task Scheduling Optimization for Multi-Human Multi-Robot Collaborative Remanufacturing

Abstract

The increasing proliferation and evolution of robotics and its capabilities is having a significant impact on smart manufacturing and remanufacturing. Within the popular frameworks of Industry 4.0 and Industry 5.0, Human-robot collaboration (HRC) has emerged to integrate the best capabilities of humans like their problem solving with those of robots like their precision. These systems are continuing to scale rapidly and are beginning to introduce multi-human multi-robot collaboration (MHMRC) environments, offering a greater degree of productivity and flexibility. Both HRC and MHMRC are still faced with underexplored challenges, such as task allocation and scheduling. In this study, we propose a nature-inspired, objective function-constrained task scheduling optimization solution for multi-human multi-robot collaborative remanufacturing. Different objective functions for the Dingo Optimization Algorithm are developed to investigate how human participants perceive task assignments and interpret the disassembly process under varying objectives in MHMRC. We conduct a real-world multi-human multi-robot collaborative remanufacturing user study in which participants disassemble an end-of-life desktop computer in a shared workspace with two robots to test and validate the proposed approach. Participants are surveyed using the NASA-TLX, along with additional questions. Experimental results demonstrate the effectiveness of the developed approach, and directions for future work are also discussed.