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形状自适应欠驱动三关节机器人手指设计 总被引:5,自引:0,他引:5
根据欠驱动原理研制的三指10个自由度的机器人手爪具有驱动元件数量少、抓取物体范围广泛等优点.在欠驱动手爪的4个主要机构中,欠驱动手指对抓取物体具有被动柔顺和形状自适应的特性.首先对三关节欠驱动手指机构进行静力学分析,提出合理的设计目标和约束条件;然后根据设计目标,采用遗传算法得到手指机构的各个关节连杆尺寸和抓取物体时的特殊构形,使得在抓取给定物体时各关节指面的接触力达到均匀分布,得到高效的力传递和更加紧凑的机构尺寸. 相似文献
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针对五指机械手抓取成功率低和抓取任务相对简单的问题,基于区域姿态解算方法设计并实现了一种满足不同复杂任务的五指抓取系统。首先,设计了一种气动五指软爪,该软爪使用多种不同刚度的材料制成,由1根主气管和5根支气管驱动,控制复杂度较低,机械性能和抓取性能良好,适用不同的抓取策略。进一步结合软爪的特点提出了基于区域姿态解算的抓取策略。通过预测人手抓取物体时在物体上的接触区域,求解接触区域与软爪指尖在空间上的姿态解,计算软爪的抓取姿态和关节弯曲角度,该策略能够生成高鲁棒性的抓取姿态。然后,设计了包含大量物体的接触数据集。对数据集中的物体标注人手在抓取操作中指尖的接触区域,并尽可能地去除场景信息,提高了数据集的通用性,可用作基准数据集测试算法性能。最后,设计了一系列实验来验证软爪和抓取策略在复杂场景下的抓取性能,实验结果表明了所设计的五指软爪抓取系统在复杂场景下的有效性和可靠性。 相似文献
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为了使机械手灵巧、稳妥地抓取物体,设计了一种新型结构的单电机驱动4 指12 关节机械手爪.该手
爪由电机驱动一根十字连杆,其端部分别连接4 个手指的第1 动力连杆;每个手指有3 个指节,由2 个平行四边形
的指节结构确保手指末端做平移运动;每个手指的第2 动力连杆具有延伸滑槽,当第2 动力连杆运动时,经过特别
设计的滑槽在固定支点滑动,可使手指末端匀速运动.该新型的单电机驱动手爪设计方案实现了机械手控制简单、
抓握可靠的目的. 相似文献
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依据“融差性思维”,提出了无需精确感知依旧可以在一定范围内有效工作的融差控制方法。具体分析了融差抓取方法如何运用相同控制量实现不同抓取任务的工作原理,这一原理使得融差抓取方法在面对一大类抓取任务时,不需要知道物体的具体参数,只需要知道这一大类物体的边界条件。进一步分析了融差抓取方法在欠驱动手爪上的适用性,并发现了欠驱动手爪的局限性。实验表明,在控制量设定不变的情况下,依据融差抓取方法,柔性手爪可以抓住且不抓坏宽度范围为5~45 mm的嫩豆腐,且能够成功抓取宽度范围为5~60 mm的硬质长方体;弹簧关节欠驱动手爪可以抓住且不抓坏宽度范围为20~40 mm的嫩豆腐,且能够成功抓取宽度范围为5~60 mm的硬质长方体。这体现了融差抓取方法的通用性和欠驱动手爪在抓取柔性物体时的局限性。最后,展示了柔性手爪使用融差抓取方法在桌面抓取应用中以简单的控制策略成功抓取不同形状、不同材质的物体。这充分说明了融差抓取方法不依赖于精确的对象感知及物体模型,能够简化控制策略。 相似文献
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针对纱筒上下料对人力过度依赖的问题,在研究仿生学手指基础上,构建面向智能制造的纱筒抓取仿生机械手。首先,采取模块化设计思想,设计适合纱筒抓取的仿生机械手结构模型,并选择绳索传动作为驱动方式;其次,详细分析仿生机械手的组成及其抓取原理,运用D-H坐标法,实现机械手指坐标系和手指基座坐标系之间变换,推导机械手末端位置方程,得到最优抓取姿态;最后,利用有限元软件,建立三维欠驱动仿生机械手模型并对其进行虚拟装配与运动仿真分析,以验证机械手抓取纱筒的可行性和稳定性,形成机器人智能抓取仿生机械手的关键技术。 相似文献
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结合刚性欠驱动抓取机构与柔顺机构,提出一种多模式刚柔结合欠驱动抓取机构,并对所提出的机构进行分析与实验研究。基于刚体替代法,设计了二指多模式欠驱动抓取机构的刚柔结合方案。运用正运动学分析与载荷平衡方程,对驱动单元进行静力学建模。结合伪刚体模型法、载荷平衡方程与操作对象平衡位置枚举搜索,建立两点抓取与包络抓取模式下抓取力与驱动力矩的关系式。将驱动单元静力学建模与抓取单元静力学建模相结合,可以得到完整的多模式抓取力模型。进一步地,考虑由于接触引起的柔性杆件变形,结合线性插值,对抓取力模型进行修正。基于修正后的抓取力模型,对机构尺寸参数进行优化设计,综合提升机构在两点抓取模式和包络抓取模式下的载荷输出性能。RecurDyn仿真结果显示,在两点抓取模式和包络抓取模式下,修正后完整的抓取力模型与仿真值的最大相对误差为7.62%,并且所提出的优化算法有效提升了机构的两点抓取力与综合包络抓取力。实验结果显示,优化后的抓取机构抓取力有较大的提升,修正后完整的抓取力模型与实验值的最大相对误差为1.87%,验证了抓取力建模、优化设计的有效性。 相似文献
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在平夹模式下,传统机器人手指末端的运动轨迹为圆弧,工作空间小,不适合抓取工作台上的薄板物体.为此,本文提出了一种共圆滑杆直线机构,分析了该机构的工作原理、运动特性和工作空间,并基于该直线机构设计了一种新型的直线平夹自适应机器人手.设计的机器人手包含2根手指,共4个自由度,仅采用2根电机驱动,结构简单.每个手指由基座、电机、簧件、L型连杆和2个指段等组成.该装置具备直线平夹和自适应包络两种抓取模式,捏持精度高,无需借助额外的传感和控制系统即可适应不同位置、姿态和形状的物体.针对设计的机器人手进行了不同抓取模式分析、运动分析和受力分析,研究了不同参数对抓取力的影响,为机器人手的设计和优化提供依据.并且研制了原理样机,开展了抓取实验,结果表明:机器人手的设计和分析合理,该装置可以实现直线平夹和自适应抓取功能,既能直线平夹物体,也能稳定包络抓取形状、大小各异的物体. 相似文献
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在传统基于固定视觉的排爆机器人抓取系统中,相机视觉易被遮挡且不能保证拍摄清晰度。基于随动视觉技术,提出一种将深度相机置于机械手末端并随机械手运动的排爆机器人自主抓取系统。利用深度相机计算目标物体的三维坐标,采用坐标转换方法将目标物体的位置坐标信息实时转换至机器人全局坐标系,并研究相机坐标系、机器人全局坐标系与末端执行器手爪工具坐标系三者的动态映射关系,实现排爆机器人的自主抓取。实验结果表明,与传统固定视觉方法相比,随动视觉方法可在误差2cm内,使得机器人机械手爪准确到达目标物体所在位置,且当机器人距离目标物体100cm~150cm时,抓取效果最佳。 相似文献
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A novel coupled and self-adaptive under-actuated multi-fingered hand with gear–rack–slider mechanism
Aiming to overcome the serious disadvantages of two kinds of under-actuated fingers: coupled finger and self-adaptive finger, this paper proposed a novel grasping mode, called Coupled and Self-Adaptive (COSA) grasping mode, which includes two stages: first coupled and self-adaptive grasping. A 2-joint COSA finger with a double gear–rack–slider mechanism (called COSA-GRS finger), is developed based on the COSA grasping mode: at the beginning, the 2-joint finger bends with coupled mode, two joints of the finger rotate simultaneously with a fixed ratio until the proximal phalanx touches the grasped object, then the finger will automatically decouple and rotate with self-adaptive mode, the distal phalanx quickly rotates until it touches the object. The new finger unit has the advantages of coupled fingers and self-adaptive fingers. The finger is not only able to rotate all joints simultaneously to pre-shape before grasping objects, but also able to self-adapt different sizes and shapes of objects. Using the same mechanism as the 2-joint finger, a 3-joint COSA finger is designed. Force analyses and a structure optimization rule of the new finger are given and discussed. The simulation results show that the finger unit is effective: it can successfully realize coupling and decoupling and it can stably grasp objects. An under-actuated humanoid robot hand is developed, called the COSA-GRS Hand. The hand has 5 fingers, 15 joints and 6 motors. All fingers of the hand are COSA fingers. The hand is more similar to human hand in appearance and actions, able to grasp different objects more dexterously and stably than traditional coupled or self-adaptive under-actuated hands. 相似文献
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气动肌肉驱动的柔顺机器人操作手的设计和实现 总被引:5,自引:0,他引:5
以气动肌肉为主要驱动器,设计了一个柔顺机器人操作手,在腕部和手部两个不同的关节,使用了两种不同型号的气动肌肉,分别采取了两种不同的传动方式.给出了腕部俯仰关节的位置控制策略.建立了手指的静力学模型,以此为基础,分析了气动肌肉的输入压力与指端夹持力的关系.实验结果表明,该机器人操作手可实现对物体安全、柔顺的抓取操作. 相似文献
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In this study, we improved an underactuated finger mechanism by using Solidworks to simulate the grasp operation of a finger in some different situations. In addition, a robot palm is designed for the three-finger robot hand with the designed underactuated fingers. A Solidworks simulation was used to verify the rationality of the design. Some parts of the hand were modified to fit for 3D printing, and a prototype of the hand was produced by 3D printing, which could reduce the cost of the production process, as well as provide design flexibility and other advantages. Finally, some grasping experiments were made with the prototype. The results showed that the robot could grasp objects with different sizes, and further verified the rationality of the design and feasibility of fabricating the robot hand using 3D printing. 相似文献
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《International Journal of Industrial Ergonomics》2014,44(5):761-768
This study aimed to investigate commonly used voluntary grasp types according to object shape, size, and direction, with the participation of 50 students. The grasp type classifications consisted of grasping (G) and pinching (P) and were further subdivided based on the number and use of fingers (T, thumb; I, index finger; M, middle finger; R, ring finger; or L, little finger) and palm (P). Seven grasp types were commonly used by the participants: 5P (TIMRL), 5G (TIMRL), 3P (TIM), 4P (TIMR), 2P (TI), 4G (TIMR), and 3G (TIM). Participants most frequently held cylindrical and square pillar objects using 5G (TIMRL) and 5P (TIMRL), respectively. 3P (TIM) was commonly used to hold 1- to 4-cm objects, whereas 5G (TIMRL) and 5P (TIMRL) were commonly used to hold 8- to 12-cm objects. The up-down direction-oriented objects were commonly held with 5G (TIMRL), and the left-right direction-oriented objects were held with 2P (TI), 3P (TIM), and 5G (TIMRL). These results provide basic information regarding common patterns of human grasp types.Relevance to industryThis paper provides a guideline on the selection of hand tools in industry in terms of their shapes and sizes, so that workers can use appropriate hand postures to reduce hand stresses. 相似文献