Task Scheduling Optimization for Multi-Human Multi-Robot Collaborative Remanufacturing
Emilio Herrera, Weitian Wang
AI summary
Problem
Multi-human multi-robot collaboration in remanufacturing faces underexplored challenges in task allocation and scheduling due to increased scale, complexity, and human factors. This paper addresses how to optimally distribute labor and schedule tasks to maximize efficiency while maintaining human trust and satisfaction.
Approach
The authors propose an objective function-constrained task scheduling solution using the Dingo Optimization Algorithm, testing two different objective functions (distance-only versus distance/weight/size) in a real-world user study where human participants disassemble a computer alongside two robots.
Key results
- Development of a nature-inspired, objective function-constrained task scheduling framework for MHMRC remanufacturing
- Implementation and comparison of two distinct Dingo Optimization Algorithm objective functions for task assignment
- Successful real-world validation through a multi-human multi-robot disassembly user study with fourteen participants
- Demonstration of rapid algorithmic convergence (under 15 iterations) and effective constraint handling for balanced task distribution
Why it matters
This work provides actionable insights for designing human-centric, efficient task allocation systems in collaborative remanufacturing, directly supporting the scalability and practical deployment of Industry 5.0 smart factories.
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.