基于欠驱动原理的多指灵巧手结构设计及实验研究

针对传统的多指灵巧手手指驱动源多、结构复杂,抓取过程中驱动电机保持堵转状态导致发热量大、寿命短,以及无法准确感知目标物体的位姿并施加合理的抓取力等问题,对灵巧手手指结构、系统构型及传感器等方面进行了研究。提出了一种具有欠驱动特性及自锁特性的多指灵巧手手指结构方案及具有不同承载能力的腱传动结构方案,设计了二指、三指及五指灵巧手系统构型,开发了高灵敏度、低成本一维力传感器及触觉传感器;以三指灵巧手为例,加工了实验样机,利用实验样机进行了相关实验。研究结果表明:该灵巧手的手指结构能够有效降低驱动源数量,实现机构减重,通过更改腱传动机构,可实现不同承担不同负载,具有较好的安全性和可靠性,一维力传感器及触觉传感器具有较高的灵敏度、准确性及低成本特性,该灵巧手指具有较高的重复定位精度,可完成针对不同形状物体的稳定、可靠的抓取任务。     

三指灵巧手运动学分析及抓持仿真

参考人手的结构特征,设计了一种三指灵巧手,手指采用模块化设计,各手指结构相同。手指各主动关节均由直流伺服电机驱动,通过锥齿轮和钢丝绳传动机构传动,直流伺服电机和传动机构均配置在手指内部。采用D-H法建立手指坐标系,推导出运动学正、逆解,为灵巧手控制和抓取规划的研究奠定基础。运用仿真软件A D A M S对灵巧手抓持球体过程进行仿真分析,结果表明三指灵巧手在其工作空间内能够实现对物体的精确抓取。     

基于并联手指结构的多功能灵巧手的设计与研究

为满足工业生产对机器人末端夹持器灵巧性和承载能力的要求,设计了一种基于并联手指结构的多功能灵巧手。对手指机构的运动学、工作空间、奇异性进行了分析,对灵巧手的承载能力进行优化,并通过ADAMS仿真进行了验证。样机实验说明灵巧手具有理想的工作空间和较好的自适应性,可以实现对不同形状物体的随形抓取,适应柔性化工业生产的要求。     

人工肌肉驱动的多指灵巧手运动学计算与分析

目前对灵巧手的运动学分析对象多为单指结构,很少涉及多指协调的运动学分析,为了更好地完成HUST灵巧手的抓取规划,主要针对多指灵巧手的运动学问题以及各手指的操作可达性进行分析计算与验证。根据气动人工肌肉驱动的HUST灵巧手的结构特点,采用标准D-H参数法建立各手指指尖相对于手掌坐标系的运动方程,分析求解多指气动灵巧手的正逆运动学问题,基于Matlab对灵巧手的工作空间进行仿真分析,得出了各手指在手掌坐标系下的操作可达空间,并通过抓取实例验证了运动学与仿真分析的正确性,为多指灵巧手的抓取规划提供了重要依据。     

基于仿肌弹性欠驱动手的设计与分析

针对仿肌腱传动灵巧手存在的驱动单元多、机构体积过大、控制系统复杂等问题,提出一种新型的仿肌弹性欠驱动手。以人类手指解剖学特征为思路,进行了仿人手指设计,通过引入双肌腱和弹性元件来实现手指的欠驱动,并给出合理选择弹性参数的方法,保证了手指的可达空间和抓取可靠性。进行了手指运动学分析,建立了关节空间到肌腱空间的映射关系。为获得最优的手掌尺寸及抓取范围,提出一种手指位置参数优化方法,并给出了评价指标,通过对比验证了该方法的优势。最后,进行了不同形状、尺寸物体的抓取实验,结果表明该欠驱动手具有较强的自适应抓取能力、广泛的物体抓取范围。     

SHU-II五指仿人灵巧手的运动学及抓取分析

根据人手结构设计一种五指仿人灵巧手。该灵巧手共有5根手指,总计16个自由度。灵巧手的手指关节虚拟化,手指更加轻量。灵巧手采用欠驱动方式,并将驱动系统内置,整体结构集成度高,易于安装到不同的机械臂平台。在理论研究上,通过齐次变换矩阵得到灵巧手的运动学模型,进而得到五指的运动轨迹,并进而对灵巧手控制策略进行探讨,之后对灵巧手进行手势与抓取实验,实验表明灵巧手能完成夹、捏、握等10种动作。把灵巧手应用在不同的机械臂平台上,验证了灵巧手的抓取范围广及其在不同环境下的抓取能力。     

水下灵巧手的结构优化及控制系统设计

设计了3指水下灵巧手及其防腐密封结构,对结构参数进行了优化设计.基于CAN总线构建了灵巧手控制系统.根据优化设计结果研制了3指水下灵巧手原理样机,测试结果表明,该灵巧手可灵活抓取多种复杂表面的物体,部分代替人工水下作业.     

直线平夹自适应机器人手抓取分析与实验

传统机器人手在平夹模式下,其手指末端的运动轨迹为圆弧,难以精确捏持薄板物体。针对传统机器人手的不足,设计一种可实现直线平夹精确捏持和自适应包络通用抓取的机器人手,分析了该机器人手的工作原理,结构组成,并且对机器人手抓取模式、平夹抓取最大抓取质量和自适应包络抓取特性进行分析,为优化机器人手设计提供依据。研制了机器人手样机,并在手指表面安置了压力传感器实时监测抓取过程中的受力情况,开展了定性抓取实验和定量测试实验,通过对传感器反馈数据进行分析,得出了机器人手在不同抓取模式、抓取不同特性物体时的受力情况,验证了机器人手抓取性能。     

医用排牙多指灵巧手的结构参数优化设计

针对排牙多指灵巧手手指的工作状态,通过D-H坐标的建立,借助于雅可比矩阵和一些结构参数优化的评价指标,利用多目标函数优化的方法,并结合排牙工作的特点和多指灵巧手抓取物体的特性建立约束条件,对手指各项参数进行了优化计算.结果证明：此优化能有效减少灵巧手指的长度,使其各标准综合达到最优.得出了包括1个手掌、3个手指、9个回转关节的灵巧手结构.     

欠腱驱动多指灵巧手的设计

提出一种欠腱驱动多指灵巧手，它具有结构简单，操作灵巧，控制容易，较高的操作适应性，每个多指节手指仅用一个腱驱使手指弯曲运动。文中描述了欠腱驱动多指灵巧手的结构设计与手指位移分析，与现有的多指灵巧手和欠驱动多指杆机器人手相比，这种手指灵巧手的结构更为简单紧凑，且能减少控制的复杂性、重量和成本，并能实现多功能地抓取不同物体的能力。     

Development of Gesture-Changeable under-actuated Humanoid Robotic Finger

Robotic fingers, which are the key parts of robot hand, are divided into two main kinds: dexterous fingers and under-actuated fingers. Although dexterous fingers are agile, they are too expensive. Under-actuated fingers can grasp objects self-adaptively, which makes them easy to control and low cost, on the contrary, under-actuated function makes fingers feel hard to grasp things agilely enough and make many gestures. For the purpose of designing a new finger which can grasp things dexterously, perform many gestures and feel easy to control and maintain, a concept called "gesture-changeable under-actuated" (GCUA) function is put forward. The GCUA function combines the advantages of dexterous fingers and under-actuated fingers: a pre-bending function is embedded into the under-actuated finger. The GCUA finger can not only perform self-adaptive grasping function, but also actively bend the middle joint of the finger. On the basis of the concept, a GCUA finger with 2 joints is designed, which is realized by the coordination of screw-nut transmission mechanism, flexible drawstring constraint and pulley-belt under-actuated mechanism.Principle analyses of its grasping and the design optimization of the GCUA finger are given. An important problem of how to stably grasp an object which is easy to glide is discussed. The force analysis on gliding object in grasping process is introduced in detail. A GCUA finger with 3 joints is developed. Many experiments of grasping different objects by of the finger were carried out. The experimental results show that the GCUA finger can effectively realize functions of pre-bending and self-adaptive grasping, the grasping processes are stable. The GCUA finger excels under-actuated fingers in dexterity and gesture actions and it is easier to control and cheaper than dexterous hands, becomes the third kinds of finger.     

三指灵巧手结构设计与动态抓取研究

针对目前多自由度灵巧手存在的结构复杂、体积庞大等问题,设计了一种结构紧凑、质量轻盈的电机直驱式8自由度三指灵巧手.通过微型电机与齿轮减速器配合使用,实现了关节直驱方式,减少了驱动力损耗,手指驱动力得到有效提高.针对灵巧手在动态抓取模式下的不足,通过嵌入式超声波传感器、压力传感器和角度传感器的联合使用,对灵巧手的动态抓取...     

A fingertip force sensor for underwater dexterous hand

In order to safely grasp an unknown object and accurately perceive its position in the fingers, a finger-tip force sensor which can detect the force and position simultaneously was developed for the underwater dexterous hand. This paper introduced the finger-tip force sensor model which was built by a cylinder elastic body. The principle of force measuring was analyzed theoretically, which proved the reasonableness of the structure and the equation was inferred for measuring force. Because of nonlinear and coupling, a feedforward artificial neural network was employed to calibrate the sensor utilizing the data which were obtained from the tests. The characteristic tests of the sensor showed the maximum error was below 6% in force measurements and the maximum position error was Φ1.6 mm. Then, the application experiments were carried out and the results showed the finger system can track the expectation of force trajectory, which indicated measurement accuracy of the sensor met the demands of research on the underwater dexterous hand. What’s more, the sensor structure can compensate the effect of water pressure. So the sensor can be integrated into the finger of the underwater manipulator and used under the water.     

大柔性灵巧手指弯曲仿真研究

针对传统机器人灵巧手弯曲柔性度不足等问题,提出了一种新型气动柔性弯曲关节—大柔性灵巧手指。为验证大柔性灵巧手指转角静态模型的正确性,首先,将该手指等效为悬臂梁,然后,通过ANSYS仿真软件模拟了手指在不同内腔气压下的弯曲模型,建立了手指弯曲角度与内腔压力的仿真关系曲线,结果显示该仿真关系曲线与手指转角静态模型曲线基本吻合;最后,通过实验研究得到了手指实际弯曲角度与实际内腔压力之间的数据关系,并通过Matlab软件整理仿真与实验数据得出了理论弯曲曲线与实际充放气曲线对比图。研究结果表明,该大柔性灵巧手指转角静态模型是正确的,这可为以后手指的进一步研究奠定一定的理论基础。     

五指仿人机器人灵巧手DLR/HIT Hand II

基于机电一体化设计思想和最新的驱动技术,研制DLR/HIT II仿人灵巧手。该灵巧手由5个相同结构的模块化手指和1个独立的手掌构成,每个手指有4个关节、3自由度,所有的驱动器和电路板均集成在手指或手掌内。采用新型的体积小输出力矩大的盘式无刷直流驱动电动机、质量轻的谐波减速器、齿形皮带等的驱动传动方案,使手指的体积和质量得到显著减小;采用钢丝耦合传动方案,实现手指末端两个关节的1:1耦合运动;手指具有位置、力/力矩、温度等多种感知功能。层次化的灵巧手硬件结构由手指电气系统、手掌电气系统和PCI总线控制卡等组成,灵巧手具有点对点串行通信、CAN以及网络等多种通信接口。在灵巧手的外观设计中,将外观设计与灵巧手的本体设计融为一体,实现灵巧手与人手相近的体积和外观。5指灵巧手的质量为1.5 kg,手指的指尖输出力10 N。     

鼠标形桌面主操作手的设计

为了实现对灵巧手的主从控制,设计了一个鼠标形桌面主操作手。主操作手将操作者手指的运动位置实时传递给从手,在从手抓取物体的过程中,利用直流电机作为主操作手的主动力反馈控制源,通过直流电机的驱动电路控制电机的转矩,将从手感受的力反馈给操作者。整个系统由DSP控制器进行控制。     

多指手的操作灵巧性设计

将多指手的设计分解为手指机构的灵巧性设计及手指的合理布位两个子问题。针对子问题一,以手指雅可比矩阵条件数的倒数为位形操作灵巧性指标确定了合理的手指杆长比例及指端的灵巧操作区间。以灵巧操作速度和灵巧操作力为指端操作灵巧性指标,给出了确定手指驱动器功率的算法。对于子问题二,根据操作灵巧性对各指端灵巧操作区间相对位姿关系的要求,给出了手指布位的一般性原则。     

Mechanical design,modeling, and identification for a novel antagonistic variable stiffness dexterous finger

This study traces the development of dexterous hand research and proposes a novel antagonistic variable stiffness dexterous finger mechanism to improve the safety of dexterous hand in unpredictable environments, such as unstructured or man-made operational errors through comprehensive consideration of cost, accuracy, manufacturing, and application. Based on the concept of mechanical passive compliance, which is widely implemented in robots for interactions, a finger is dedicated to improving mechanical robustness. The finger mechanism not only achieves passive compliance against physical impacts, but also implements the variable stiffness actuator principle in a compact finger without adding supererogatory actuators. It achieves finger stiffness adjustability according to the biologically inspired stiffness variation principle of discarding some mobilities to adjust stiffness. The mechanical design of the finger and its stiffness adjusting methods are elaborated. The stiffness characteristics of the finger joint and the actuation unit are analyzed. Experimental results of the finger joint stiffness identification and finger impact tests under different finger stiffness presets are provided to verify the validity of the model. Fingers have been experimentally proven to be robust against physical impacts. Moreover, the experimental part verifies that fingers have good power, grasping, and manipulation performance.