多机器人协同制造中,用分层异步调度避免碰撞并保证工期。
LASER: Level-Based Asynchronous Scheduling and Execution Regime for Spatiotemporally Constrained Multi-Robot Timber Manufacturing
- 将任务按时空约束分层,同层异步执行,层间同步保障安全。
- 实测在2.4m×6m木板上完成108个子程序、352颗螺丝,全在胶水时效内完成。
- 适合需要高精度协同的大型自动化制造场景,如木结构建筑。
大型木材制造自动化需多机器人系统应对紧密耦合的时空约束,如避碰和工艺时限。本文提出LASER(基于层级的异步调度与执行机制),一个完整的复杂装配任务调度与执行框架,在螺栓压紧式木材板制造中验证。核心贡献是将基于屏障的机制融入约束规划(CP)调度模型,将任务划分为时空不交的层级,使机器人可在层级内异步并行作业,仅在层级屏障处同步,从而构造性保证无碰撞,并增强对时间不确定性的鲁棒性。针对大规模问题,提出两种专用算法:一种用于异构任务序列的迭代时序松弛法,另一种用于同质任务的双层分解法以平衡负载。通过两台安装于平行导轨的机器人系统,成功制造出2.4m×6m的全尺寸木板,协调执行108个子程序和352颗螺丝,均在严格的胶水时效窗口内完成。计算实验表明,该方法在规模扩大时仍保持稳定性能,优于单体方法。
原文摘要 · Abstract (English)
Automating large-scale manufacturing in domains like timber construction requires multi-robot systems to manage tightly coupled spatiotemporal constraints, such as collision avoidance and process-driven deadlines. This paper introduces LASER (Level-based Asynchronous Scheduling and Execution Regime), a complete framework for scheduling and executing complex assembly tasks, demonstrated on a screw-press gluing application for timber slab manufacturing. Our central contribution is to integrate a barrier-based mechanism into a constraint programming (CP) scheduling formulation that partitions tasks into spatiotemporally disjoint sets, which we define as levels. This structure enables robots to execute tasks in parallel and asynchronously within a level, synchronizing only at level barriers, which guarantees collision-free operation by construction and provides robustness to timing uncertainties. To solve this formulation for large problems, we propose two specialized algorithms: an iterative temporal-relaxation approach for heterogeneous task sequences and a bi-level decomposition for homogeneous tasks that balances workload. We validate the LASER framework by fabricating a full-scale 2.4m x 6m timber slab with a two-robot system mounted on parallel linear tracks, successfully coordinating 108 subroutines and 352 screws under tight adhesive time windows. Computational studies show our method scales steadily with size compared to a monolithic approach.
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