智能药房机器人可精准抓取形状各异的重叠药品。
Optimizing Drug Delivery in Smart Pharmacies: A Novel Framework of Multi-Stage Grasping Network Combined with Adaptive Robotics Mechanism
- 分阶段网络结合自适应机械臂，提升复杂环境抓取能力。
- 实验显示该系统在重叠药品场景下识别准确率超95%。
- 适合医疗自动化、智能仓储等实际场景应用。
基于机器人的智能药房对现代医疗系统至关重要，可实现高效药物配送。然而，以往研究未能充分解决机器人在处理形状各异且相互重叠的药品时的抓取难题。本文提出一种结合多阶段抓取网络与自适应机器人机制的新框架。首先，采用改进的超分辨率卷积神经网络（SRCNN）预处理图像；随后，利用提出的YOLOv5+E-A-SPPFCSPC+BIFPNC（YOLO-EASB）实例分割算法实现精确药物分割，通过评估分割掩码的完整性确定最优抓取目标。这些分割后的药物由改进的自适应特征融合与抓取感知网络（IAFFGA-Net）处理，并配合优化损失函数，确保在复杂环境中仍能准确执行抓取动作。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插值的时间最优轨迹规划算法，进一步提高效率并保证轨迹平滑性。最终，该系统在自适应协作机器人平台上实现并验证，能动态适应不同生产环境与任务需求。实验结果表明，所提多阶段抓取网络显著优化了智能药房运行效率，在实际应用中展现出卓越的适应性与有效性。
原文摘要 · Abstract (English)
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.
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