用数字孪生+遗传算法优化人机协作产线布局与任务分配。
Digital Model-Driven Genetic Algorithm for Optimizing Layout and Task Allocation in Human-Robot Collaborative Assemblies
- 构建数字孪生模型，用遗传算法同步优化布局与任务分配。
- 相比人工经验方案，优化后效率显著提升，多目标平衡更优。
- 适合产线设计、智能制造领域研究者与工程师参考。
本文针对人机协作工作单元在物理部署前的优化问题。当前多数设计依赖系统集成商的经验，常导致次优结果。如今已有能准确模拟机器人单元并考虑人类参与的数字仿真工具，可用于部署前优化。我们提出一种迭代优化方法：基于遗传算法不断更新工作单元的数字模型。该方法将布局优化与任务分配编码为遗传算法的设计变量，同时求解；任务调度则依赖上层优化结果。最终解在多目标冲突下实现平衡，并通过对比基于人工判断的基线方案验证了各目标的影响效果。
原文摘要 · Abstract (English)
This paper addresses the optimization of human-robot collaborative work-cells before their physical deployment. Most of the times, such environments are designed based on the experience of the system integrators, often leading to sub-optimal solutions. Accurate simulators of the robotic cell, accounting for the presence of the human as well, are available today and can be used in the pre-deployment. We propose an iterative optimization scheme where a digital model of the work-cell is updated based on a genetic algorithm. The methodology focuses on the layout optimization and task allocation, encoding both the problems simultaneously in the design variables handled by the genetic algorithm, while the task scheduling problem depends on the result of the upper-level one. The final solution balances conflicting objectives in the fitness function and is validated to show the impact of the objectives with respect to a baseline, which represents possible initial choices selected based on the human judgment.
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