Laboratory Automation (LA) has the potential to accelerate solid-state materials discovery by enabling continuous robotic operation without human intervention. While robotic systems have been developed for tasks such as powder grinding and X-ray diffraction (XRD) analysis, fully automating powder handling at the milligram scale remains a significant challenge due to the complex flow dynamics of powders and the diversity of laboratory tasks. To address this challenge, this study proposes a novel, funnel-shaped, flexible robotic hand that preserves the softness and conical sheet designs in prior work while incorporating a controllable valve at the cone apex to enable precise, incremental dispensing of milligram-scale powder quantities. The hand is integrated with an external balance through a feedback control system based on a model of powder flow and online parameter identification. Experimental evaluations with glass beads, monosodium glutamate, and titanium dioxide demonstrated that 80% of the trials achieved an error within 2 mg, and the maximum error observed was approximately 20 mg across a target range of 20 mg to 3 g. In addition, by incorporating flow prediction models commonly used for hoppers and performing online parameter identification, the system is able to adapt to variations in powder dynamics. Compared to direct PID control, the proposed model-based control significantly improved both accuracy and convergence speed. These results highlight the potential of the proposed system to enable efficient and flexible powder weighing, with scalability toward larger quantities and applicability to a broad range of laboratory automation tasks.

翻译：实验室自动化（LA）通过实现无需人工干预的连续机器人操作，具有加速固态材料发现的潜力。尽管已开发出用于粉末研磨和X射线衍射（XRD）分析等任务的机器人系统，但由于粉末的复杂流动动力学及实验室任务的多样性，在毫克尺度上实现粉末处理的完全自动化仍是一项重大挑战。为应对这一挑战，本研究提出了一种新颖的、漏斗形柔性机械手，该设计保留了先前工作中的柔软性和锥形片结构，同时在锥顶集成了可控阀门，以实现毫克级粉末的精确、增量式分配。该机械手通过基于粉末流动模型和在线参数识别的反馈控制系统与外部天平集成。使用玻璃珠、谷氨酸钠和二氧化钛进行的实验评估表明，80%的试验误差在2毫克以内，在20毫克至3克的目标范围内观察到的最大误差约为20毫克。此外，通过整合料斗常用的流动预测模型并执行在线参数识别，系统能够适应粉末动力学的变化。与直接PID控制相比，所提出的基于模型的控制显著提高了精度和收敛速度。这些结果凸显了所提系统在实现高效、灵活的粉末称量方面的潜力，具备向更大规模扩展的能力，并适用于广泛的实验室自动化任务。