随着人机协作领域的快速发展,对机械手的性能要求越来越高。本研究针对机械手在抓取不同形状物体时对承载力和通用性的高要求,设计了一种基于鳍条效应原理的变刚度柔性机械手。该机械手融合了欠驱动技术和柔性材料的优点,展现出卓越的...随着人机协作领域的快速发展,对机械手的性能要求越来越高。本研究针对机械手在抓取不同形状物体时对承载力和通用性的高要求,设计了一种基于鳍条效应原理的变刚度柔性机械手。该机械手融合了欠驱动技术和柔性材料的优点,展现出卓越的被动柔顺性,并通过变刚度结构设计,显著提升了其承载能力。研究中提出了一种新的变刚度手指设计方案,通过外骨骼集成的概念,实现了机械手的双模态操作能力:低刚度模式下轻柔抓取易损物品,高刚度模式下牢固握持重物。通过ABAQUS仿真和实验测试,验证了变刚度机构对机械手性能的提升效果,结果表明,开启变刚度机构后,机械手的承载能力最高提升了近9.89倍,同时保持了良好的柔顺性。本研究为柔性机械手技术的发展提供了新的解决方案,并为未来的应用提供了广阔的前景。With the rapid development of the field of human-robot collaboration, the performance requirements for robotic arms are increasingly high. This study addresses the high demands for carrying capacity and versatility when the robotic arm grasps objects of different shapes, and has designed a variable stiffness flexible robotic arm based on the fin ray effect principle. The robotic arm integrates the advantages of under actuated technology and flexible materials, demonstrating excellent passive compliance, and significantly enhances its carrying capacity through the design of a variable stiffness structure. A new variable stiffness finger design scheme was proposed in the study, which, by integrating the concept of an exoskeleton, realizes the dual-mode operation capability of the robotic arm: gently grasping fragile objects in low stiffness mode and firmly holding heavy objects in high stiffness mode. ABAQUS simulation and experimental testing were used to verify the performance enhancement effect of the variable stiffness mechanism, and the results showed that after the variable stiffness mechanism was activated, the maximum carrying capacity of the robotic arm was increased by nearly 9.89 times, while maintaining good compliance. This study provides a new solution for the development of flexible robotic arm technology and offers a broad prospect for future applications.展开更多
为实现轨道车辆螺栓松动智能检测与螺栓预紧力实时监测,提高轨道车辆生产、运维中螺栓的预紧精度,提出一种基于机器视觉技术的螺栓松动角度非接触定量测量方法,基于该方法探究螺栓在预紧过程中各参量之间的关系模型。首先,进行相机内参...为实现轨道车辆螺栓松动智能检测与螺栓预紧力实时监测,提高轨道车辆生产、运维中螺栓的预紧精度,提出一种基于机器视觉技术的螺栓松动角度非接触定量测量方法,基于该方法探究螺栓在预紧过程中各参量之间的关系模型。首先,进行相机内参标定;然后实时获取螺栓图像,对获取到的图像后进行透视变换、滤波降噪等预处理,通过设定特定的通道阈值来提取图像的感兴趣区域(Region of interest,ROI),使用Sklansky算法在ROI平面点集进行凸包迭代,找出最少点集的矩形特征,同时利用旋转卡尺算法Rotating calipers返回凸包的最小面积外接矩形轮廓,以矩形中心点与Width边为特征计算出矩形特征的旋转角度θ;最终通过实验构建螺栓预紧力、拧紧力矩分别与旋转角度、螺杆行径量关系模型,以及预紧力与拧紧力矩关系模型。结果表明螺栓松动角度检测方法最大测量偏差为0.54°,最大相对误差为3.25%。该方法具备测量精度高,系统成本低、部署方便的特点,满足轨道车辆大批量的螺栓松动角度的非接触与自动化检测要求。轨道车辆螺栓智能精准预紧新工艺预紧力、拧紧力矩的计算精度达到90%以上,比传统的扭矩−转角法预紧精度高15%~40%。研究成果为轨道车辆螺栓松动智能运维提供技术支撑。展开更多
文摘随着人机协作领域的快速发展,对机械手的性能要求越来越高。本研究针对机械手在抓取不同形状物体时对承载力和通用性的高要求,设计了一种基于鳍条效应原理的变刚度柔性机械手。该机械手融合了欠驱动技术和柔性材料的优点,展现出卓越的被动柔顺性,并通过变刚度结构设计,显著提升了其承载能力。研究中提出了一种新的变刚度手指设计方案,通过外骨骼集成的概念,实现了机械手的双模态操作能力:低刚度模式下轻柔抓取易损物品,高刚度模式下牢固握持重物。通过ABAQUS仿真和实验测试,验证了变刚度机构对机械手性能的提升效果,结果表明,开启变刚度机构后,机械手的承载能力最高提升了近9.89倍,同时保持了良好的柔顺性。本研究为柔性机械手技术的发展提供了新的解决方案,并为未来的应用提供了广阔的前景。With the rapid development of the field of human-robot collaboration, the performance requirements for robotic arms are increasingly high. This study addresses the high demands for carrying capacity and versatility when the robotic arm grasps objects of different shapes, and has designed a variable stiffness flexible robotic arm based on the fin ray effect principle. The robotic arm integrates the advantages of under actuated technology and flexible materials, demonstrating excellent passive compliance, and significantly enhances its carrying capacity through the design of a variable stiffness structure. A new variable stiffness finger design scheme was proposed in the study, which, by integrating the concept of an exoskeleton, realizes the dual-mode operation capability of the robotic arm: gently grasping fragile objects in low stiffness mode and firmly holding heavy objects in high stiffness mode. ABAQUS simulation and experimental testing were used to verify the performance enhancement effect of the variable stiffness mechanism, and the results showed that after the variable stiffness mechanism was activated, the maximum carrying capacity of the robotic arm was increased by nearly 9.89 times, while maintaining good compliance. This study provides a new solution for the development of flexible robotic arm technology and offers a broad prospect for future applications.
文摘为实现轨道车辆螺栓松动智能检测与螺栓预紧力实时监测,提高轨道车辆生产、运维中螺栓的预紧精度,提出一种基于机器视觉技术的螺栓松动角度非接触定量测量方法,基于该方法探究螺栓在预紧过程中各参量之间的关系模型。首先,进行相机内参标定;然后实时获取螺栓图像,对获取到的图像后进行透视变换、滤波降噪等预处理,通过设定特定的通道阈值来提取图像的感兴趣区域(Region of interest,ROI),使用Sklansky算法在ROI平面点集进行凸包迭代,找出最少点集的矩形特征,同时利用旋转卡尺算法Rotating calipers返回凸包的最小面积外接矩形轮廓,以矩形中心点与Width边为特征计算出矩形特征的旋转角度θ;最终通过实验构建螺栓预紧力、拧紧力矩分别与旋转角度、螺杆行径量关系模型,以及预紧力与拧紧力矩关系模型。结果表明螺栓松动角度检测方法最大测量偏差为0.54°,最大相对误差为3.25%。该方法具备测量精度高,系统成本低、部署方便的特点,满足轨道车辆大批量的螺栓松动角度的非接触与自动化检测要求。轨道车辆螺栓智能精准预紧新工艺预紧力、拧紧力矩的计算精度达到90%以上,比传统的扭矩−转角法预紧精度高15%~40%。研究成果为轨道车辆螺栓松动智能运维提供技术支撑。
