Robots-based smart pharmacies are essential for modern healthcare systems, enabling efficient drug delivery. However, a critical challenge exists in the robotic handling of drugs with varying shapes and overlapping positions, which previous studies have not adequately addressed. To enhance the robotic arm's ability to grasp chaotic, overlapping, and variously shaped drugs, this paper proposed a novel framework combining a multi-stage grasping network with an adaptive robotics mechanism. The framework first preprocessed images using an improved Super-Resolution Convolutional Neural Network (SRCNN) algorithm, and then employed the proposed YOLOv5+E-A-SPPFCSPC+BIFPNC (YOLO-EASB) instance segmentation algorithm for precise drug segmentation. The most suitable drugs for grasping can be determined by assessing the completeness of the segmentation masks. Then, these segmented drugs were processed by our improved Adaptive Feature Fusion and Grasp-Aware Network (IAFFGA-Net) with the optimized loss function, which ensures accurate picking actions even in complex environments. To control the robot grasping, a time-optimal robotic arm trajectory planning algorithm that combines an improved ant colony algorithm with 3-5-3 interpolation was developed, further improving efficiency while ensuring smooth trajectories. Finally, this system was implemented and validated within an adaptive collaborative robot setup, which dynamically adjusts to different production environments and task requirements. Experimental results demonstrate the superiority of our multi-stage grasping network in optimizing smart pharmacy operations, while also showcasing its remarkable adaptability and effectiveness in practical applications.

翻译：基于机器人的智能药房对于现代医疗系统至关重要，能够实现高效的药物配送。然而，在机器人处理形状各异且位置重叠的药物方面存在一个关键挑战，以往的研究未能充分解决此问题。为增强机械臂抓取混乱、重叠且形状多样药物的能力，本文提出了一种结合多阶段抓取网络与自适应机器人机制的新框架。该框架首先使用改进的超分辨率卷积神经网络（SRCNN）算法对图像进行预处理，然后采用提出的YOLOv5+E-A-SPPFCSPC+BIFPNC（YOLO-EASB）实例分割算法进行精确的药物分割。通过评估分割掩码的完整性，可以确定最适合抓取的药物。随后，这些分割出的药物由我们改进的自适应特征融合与抓取感知网络（IAFFGA-Net）结合优化后的损失函数进行处理，确保即使在复杂环境中也能执行准确的抓取动作。为控制机器人抓取，开发了一种结合改进蚁群算法与3-5-3插值的时间最优机械臂轨迹规划算法，在保证轨迹平滑的同时进一步提升了效率。最后，该系统在一个自适应协作机器人设置中得以实现和验证，该设置能动态适应不同的生产环境与任务需求。实验结果证明了我们多阶段抓取网络在优化智能药房操作方面的优越性，同时展示了其在实际应用中的显著适应性和有效性。