优化移动机械臂协作任务调度与定位，提升人机协同效率
Optimized Scheduling and Positioning of Mobile Manipulators in Collaborative Applications
- 基于数字孪生建模，用粒子群算法协调多目标冲突
- 在协作装箱场景中缩短周期时间，提升任务排序合理性
- 适合需要动态适应人类活动的工业自动化场景
移动机器人在共享工作空间中的集成日益增多，亟需高效路径规划与协同机制，兼顾安全与生产效率。本文提出一种基于数字模型的优化框架，用于确定移动机械臂的基座位姿序列与任务调度顺序。整个问题被建模为黑箱优化问题，采用粒子群优化（PSO）算法以平衡多个关键绩效指标（KPIs）之间的冲突。在人机协作的装箱任务场景中，实验验证了该方法在降低周期时间、优化任务序列以及适应人类存在方面的显著改进。
原文摘要 · Abstract (English)
The growing integration of mobile robots in shared workspaces requires efficient path planning and coordination between the agents, accounting for safety and productivity. In this work, we propose a digital model-based optimization framework for mobile manipulators in human-robot collaborative environments, in order to determine the sequence of robot base poses and the task scheduling for the robot. The complete problem is treated as black-box, and Particle Swarm Optimization (PSO) is employed to balance conflicting Key-Performance Indicators (KPIs). We demonstrate improvements in cycle time, task sequencing, and adaptation to human presence in a collaborative box-packing scenario.
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