一种新型三维力柔性阵列触觉传感器研究

研究了一种基于压敏导电橡胶的新型三维力柔性阵列触觉传感器,突破了目前触觉传感器不能兼有柔韧性和检测三维力的局限性,实现了真正类皮肤.该传感器利用了压敏橡胶的压敏特性,通过检测橡胶的电阻变化来分析受力信息.介绍了该传感器的基本结构,分析了其工作原理,给出了导电橡胶受力时的有限元分析结果,并对传感器进行了初步的仿真研究,结果表明该传感器能够实现检测三维力信息,为三维力柔性阵列触觉传感器的设计研究提供了新思路.     

基于力敏导电橡胶的新型三维力柔性触觉传感器仿真研究

针对目前触觉传感器研究中不能兼有柔韧性和多维力测量等难题,设计了一种基于力敏导电橡胶的具有整体两层非对称网状式结构的触觉传感器,通过检测导电橡胶的电阻值变化来分析三维力信息。本文介绍了该传感器的基本结构,并基于理想力敏导电橡胶的力学特性建立了三维力并行测量的数学模型,通过对该模型的求解解决了三维力及各受力点之间复杂的耦合问题。仿真实验结果表明该传感器能够实现对表面任意单点三维力、多点三维力以及三维面力信息的测量。     

一种可感知三维力的新型柔性触觉传感器及滑觉识别算法研究

针对智能机器人在软抓取过程中对三维力感知和滑觉识别的需求,以炭黑/石墨烯为敏感材料,硅橡胶为基体,聚酰亚胺为柔性电极,设计了可以实现三维力感知的柔性触觉传感器;同时设计了一种可以实现滑觉识别的基于小波变换-相空间重构-支持向量机(DWT-PSR-SVM)的融合算法.针对机器人软抓取的实验结果表明,该传感器能够有效地检测三维力,通过所设计融合算法能识别在机器人抓取任务中的滑动状态和非滑动状态,可以进一步应用在机器人软抓取的工作中.     

基于柔性三维力触觉传感器的滑觉识别算法研究

针对智能机器人在软抓取过程中对三维力感知和滑觉识别的需求,本文以炭黑/碳纳米管为敏感材料,硅橡胶为基体,聚酰亚胺为柔性电极,设计了可以实现三维力感知的柔性触觉传感器;同时设计了一种可以实现滑觉识别的基于小波变换-相空间重构-支持向量机(DWT-PSR-SVM)的融合算法。针对机器人软抓取的实验结果表明,该传感器能够有效地检测三维力,通过所设计融合算法能识别在机器人抓取任务中的滑动状态和非滑动状态,可以进一步应用在机器人软抓取的工作中。     

基于卷积神经网络的软硬触觉感知方法研究

智能机器手的应用已经遍布医疗、军工、农业及装配行业等领域.软硬作为物体的重要物理属性之一,对机器手的抓取控制物体有重大影响.在深度学习框架下,基于卷积神经网络提出了用于触觉感知的软硬物体的识别方法.使用薄膜压力传感器采集手指按压软硬物体的数据,建立训练和测试数据集,在Caffe中训练网络,以模拟触觉识别软硬物体.实验结果显示:对软硬物体的识别准确率达94.52%,表明,卷积神经网络对于识别软硬物体有比较好的分类效果.     

一种新型机器人三维力柔性触觉传感器的设计

基于柔性力敏导电橡胶材料,设计了一种能测量三维力的新型机器人柔性触觉传感器。研究了力敏导电橡胶材料的压阻效应,阐述了触觉传感器的设计思想,分别进行了触觉传感器单元设计和阵列结构设计和研究。获得了计算三维力的数学模型,并通过实验进行了三维力的验证。结果表明,设计的机器人三维力柔性触觉传感器具有设计简单,造价低廉,柔顺性好等优点,而且布置成阵列结构可用于医疗、体育、机器人等领域中检测三维力信息。     

基于PVDF的三维指尖力传感器设计

在智能机械手控制过程中,为了获取指尖力三维信息,设计了一种基于聚偏二氟乙烯(PVDF)压电薄膜的指尖力触觉传感器,并通过数学模型描述了压电薄膜及传感探头原理,设计了相应的调理电路。以和接触面所呈不同角度的指尖力为测试对象。测试结果显示:所设计的指尖力传感器能准确检测指尖力的三维信息,可用于实际的指尖力检测。     

基于机器视觉的目标定位与机器人规划系统研究

为完成机械臂在非特定复杂背景环境下的自主抓取,通过设计RGB-D相机对场景内的物体进行实时检测,采用基于深度学习的目标检测定位方法,并对相机-机械臂-目标物体的三维标定模型进行研究。将物体的三维坐标信息通过ROS话题机制发送给机械臂,并通过moveIT编程规划抓取规划。 通过设计一套基于ROS的视觉检测和机械臂抓取系统,将计算机视觉检测技术以及机械臂运动规划抓取应用在机器人操作系统ROS平台上。实验结果表明,该系统可以实时高效地操作机器人来完成指定的控制作业,提高了系统对环境的适应能力,该系统具有抓取准确、物体识别准确率高的特点,解决了传统机械臂操控中的不足。     

机器人红外接近觉、光纤触觉和握力觉传感器

设计和研制了红外接近党传感器以探测物体的存在；设计和研制了机器人触须探测物体的触觉和模拟机械爪抓取物体的握力觉传感器。单片机系统控制传感器数据采集和初步判断，为机器人控制和驱动提供感觉信息。     

一种虚拟环境温度触觉再现仿真系统设计

分析了温度触觉感知特性,提出了手指和物体接触时各自表面的温度变化过程,结合单热源温度触觉再现装置设计了一种基于三维力反馈手控器的虚拟环境温度触觉再现仿真系统。手控器可控制虚拟手的动作,对虚拟物体进行温度和力的感知。仿真系统中能再现手指与不同热属性物体接触时的温度变化和力变化,且视觉效果逼真,体现了温度触觉再现的实时性和有效性。     

A sensor for dynamic tactile information with applications in human–robot interaction and object exploration

We present a novel tactile sensor, which is applied for dextrous grasping with a simple robot gripper. The hardware novelty consists of an array of capacitive sensors, which couple to the object by means of little brushes of fibers. These sensor elements are very sensitive (with a threshold of about 5 mN) but robust enough not to be damaged during grasping. They yield two types of dynamical tactile information corresponding roughly to two types of tactile sensor in the human skin. The complete sensor consists of a foil-based static force sensor, which yields the total force and the center of the two-dimensional force distribution and is surrounded by an array of the dynamical sensor elements. One such sensor has been mounted on each of the two gripper jaws of our humanoid robot and equipped with the necessary read-out electronics and a CAN bus interface. We describe applications to guiding a robot arm on a desired trajectory with negligible force, reflective grip improvement, and tactile exploration of objects to create a shape representation and find stable grips, which are applied autonomously on the basis of visual recognition.     

Robust bin-picking system using tactile sensor

ABSTRACT

This paper presents a robust bin-picking system utilizing tactile sensors and a vision sensor. The object position and orientation are estimated using a fast template-matching method through the vision sensor. When a robot picks up an object, the tactile sensors detect the success or failure of the grasping, and a force sensor detects the contact with the environment. A weight sensor is also used to judge whether the lifting of the object has been successful. The robust and efficient bin-picking system presented herein is implemented through the integration of different sensors. In particular, the tactile sensors realize rope-shaped object picking that has yet to be made possible with conventional picking systems. The effectiveness of the proposed method was confirmed through grasping experiments and in a competitive event at the World Robot Challenge 2018.     

Whole-finger manipulation with a two-fingered robot hand

This paper describes a method for whole-finger rolling manipulation using a two-fingered robot hand. 'Whole-finger' refers to the use of the complete phalangeal surface during the manipulation. An example of whole-finger manipulation by the human hand is the rolling of a pen between two fingers. The proposed method is based on a two-dimensional model for modelling an object manipulation and is derived from a study of the movement of the contact line between both fingers. Also, the method uses tactile sensor information to estimate the contact point position together with the local curvature of the object. This whole-finger dexterous manipulation is demonstrated on a prototype two-fingered hand. This 5 d.o.f. hand consists of a tendon driven index and thumb, and is equipped with force and tactile sensors. The dimensions and performance of this device are 'human-sized'. A hybrid force-position control scheme is used. The hierarchical control structure is implemented on a dual transputer system. This paper first describes the kinematic model used for whole-finger manipulation. In the second part, the main emphasis is put on the mechanical design and on the transputer-based control system.     

The design and control of a robot finger for tactile sensing

This article describes the design and control of a lightweight robot finger intended for tactile sensing research. The finger is a three-link planar chain with the joints actuated through cables by two motors. Kinematic coupling of the three joints provides two degrees of freedom for finger tip manipulation, and a curling action of the finger for enclosing an object. Hall effect sensors in each joint provide position feedback, and strain gage sensors on each cable provide tension information. To minimize weight and power consumption, a high speed low torque motor together with a 172:1 speed reducer is used as the actuator. A force control loop around the motor speed reducer system reduces the effect of the friction inherent in the speed reducer. Flat mounting plates are provided on each link for special purpose grasping surfaces and sensors.     

A strategy for fast grasping of unknown objects using partial shape information from range sensors

In this paper, we present a strategy for fast grasping of unknown objects based on the partial shape information from range sensors for a mobile robot with a parallel-jaw gripper. The proposed method can realize fast grasping of an unknown object without needing complete information of the object or learning from grasping experience. Information regarding the shape of the object is acquired by a 2D range sensor installed on the robot at an inclined angle to the ground. Features for determining the maximal contact area are extracted directly from the partial shape information of the unknown object to determine the candidate grasping points. Note that since the shape and mass are unknown before grasping, a successful and stable grasp cannot be in fact guaranteed. Thus, after performing a grasping trial, the mobile robot uses the 2D range sensor to judge whether the object can be lifted. If a grasping trial fails, the mobile robot will quickly find other candidate grasping points for another trial until a successful and stable grasp is realized. The proposed approach has been tested in experiments, which found that a mobile robot with a parallel-jaw gripper can successfully grasp a wide variety of objects using the proposed algorithm. The results illustrate the validity of the proposed algorithm in term of the grasping time.     

Dextrous hands: human, prosthetic, and robotic

The sensory and motor capacities of the human hand are reviewed in the context of providing a set of performance characteristics against which prosthetic and dextrous robot hands can be evaluated. The sensors involved in processing tactile, thermal, and proprioceptive (force and movement) information are described, together with details on their spatial densities, sensitivity, and resolution. The wealth of data on the human hand's sensory capacities is not matched by an equivalent database on motor performance. Attempts at quantifying manual dexterity have met with formidable technological difficulties due to the conditions under which many highly trained manual skills are performed. Limitations in technology have affected not only the quantifying of human manual performance but also the development of prosthetic and robotic hands. Most prosthetic hands in use at present are simple grasping devices, and imparting a "natural" sense of touch to these hands remains a challenge. Several dextrous robot hands exist as research tools and even though some of these systems can outperform their human counterparts in the motor domain, they are still very limited as sensory processing systems. It is in this latter area that information from studies of human grasping and processing of object information may make the greatest contribution.     

Development of multi-fingered dexterous hand for grasping manipulation

In this paper, a developed multi-fingered dexterous hand with flexible tactile skin is described. The dexterous hand has 5-fingers with 6-DOFs and each finger is equipped with a small harmonic drive gear and a fine high-power mini actuator. To achieve the goal of grasping with high accuracy, each fingertip is covered with the tactile array sensors for determination of the force between the finger and the grasped object. Some preliminary experiments are conducted to illustrate the performance of the grasping of the developed dexterous hand.     

用于机器人皮肤的柔性多功能触觉传感器设计与实验

为实现智能机器人皮肤对三维力和温度的检测,设计并制作了一种柔性多功能触觉传感器.基于碳黑-硅橡胶显著的压阻效应设计了四电极对称结构的三维力传感器,基于碳纤维-PDMS(聚二甲硅氧烷)显著的温度敏感效应设计了叉指电极结构的温度传感器,分析了检测三维力和温度的工作原理.针对两种导电复合材料存在的力学/温度敏感特性交叉干扰问...     

一种高分辨力柔性触觉传感器

本文描述了一种基于全内反射原理，用透明橡胶材料作为波导板的柔性触觉传感器。该传感器除具有一般刚性波导板触觉传感器的高分辨力等特点外，还具有柔性好，力灵敏阈值低，不怕碰撞等特点，该传感器特别适合装在智能机器人手爪上，为机器人系统高效率地获取物体形状，位置和姿态信息，实现智能抓握物体，操作物体等功能提供了有效的手段。