提出分层规划框架，高效完成实验室筛分仪双臂操作
Dual-Arm Hierarchical Planning for Laboratory Automation: Vibratory Sieve Shaker Operations
- 用高斯混合模型提升窄空间采样效率
- 规划时间减少80.4%，路径点减少89.4%
- 适合复杂实验自动化场景，如材料实验室
本文针对材料实验室中振动筛分仪操作的自动化难题，聚焦三项关键任务：1）在3厘米狭小空间内双臂开盖操作；2）重叠工作区内的双手交接；3）有方向约束的粉末容器输送。这些任务面临狭窄通道采样低效、轨迹不平滑易洒料、传统方法路径欠优等问题。为此，提出结合先验引导路径规划与多步轨迹优化的分层规划框架。前者采用有限高斯混合模型提升窄空间采样效率，后者通过缩短路径、简化结构、施加关节约束及B样条平滑进行优化。实验表明，该框架可使规划时间减少高达80.4%，路径点减少89.4%。物理实验验证了系统完整执行振动筛分仪操作流程的能力，证实其在复杂实验室自动化中的实用价值。
原文摘要 · Abstract (English)
This paper addresses the challenges of automating vibratory sieve shaker operations in a materials laboratory, focusing on three critical tasks: 1) dual-arm lid manipulation in 3 cm clearance spaces, 2) bimanual handover in overlapping workspaces, and 3) obstructed powder sample container delivery with orientation constraints. These tasks present significant challenges, including inefficient sampling in narrow passages, the need for smooth trajectories to prevent spillage, and suboptimal paths generated by conventional methods. To overcome these challenges, we propose a hierarchical planning framework combining Prior-Guided Path Planning and Multi-Step Trajectory Optimization. The former uses a finite Gaussian mixture model to improve sampling efficiency in narrow passages, while the latter refines paths by shortening, simplifying, imposing joint constraints, and B-spline smoothing. Experimental results demonstrate the framework's effectiveness: planning time is reduced by up to 80.4%, and waypoints are decreased by 89.4%. Furthermore, the system completes the full vibratory sieve shaker operation workflow in a physical experiment, validating its practical applicability for complex laboratory automation.
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