虚拟环境下物体的手抓取

本文分析了手模型及其虚拟环境下物体的操作方法,提出了点接触平面法矢的手握持抓取方法。在此基础上运用虚拟手模型和抓取规则实现了在虚拟环境下对物体的抓取、移动和释放,实例说明能够使虚拟手正确地抓取虚拟环境中的物体。     

虚拟环境中灵巧手主从抓持的实现

研究了虚拟现实环境中人手和灵巧手的抓持动作.利用数据手套采集人手的运动信息,将人手的运动 映射给灵巧手,通过搭建人手和灵巧手的模型,在虚拟环境下实现了主从抓持操作.探讨了关键技术问题:异构系统 运动映射、碰撞检测、虚拟力建模、稳定抓持的判据.􀁱     

基于未知物体三维点云特征的机器人六自由度抓取

针对非结构化环境中任意位姿的未知物体,提出了一种基于点云特征的机器人六自由度抓取位姿检测方法,以解决直接从点云中获取目标抓取位姿的难题.首先,根据点云的基本几何信息生成抓取候选,并通过力平衡等方法优化这些候选;然后,利用可直接处理点云的卷积神经网络ConvPoint评估样本,得分最高的抓取将被执行,其中抓取位姿采样和评估网络都是以原始点云作为输入;最后,利用仿真和实际抓取实验进行测试.结果表明,该方法在常用对象上实现了88.33%的抓取成功率,并可以有效地拓展到抓取其他形状的未知物体.     

Robust object manipulation for tactile-based blind grasping

Tactile-based blind grasping addresses realistic robotic grasping in which the hand only has access to proprioceptive and tactile sensors. The robotic hand has no prior knowledge of the object/grasp properties, such as object weight, inertia, and shape. There exists no manipulation controller that rigorously guarantees object manipulation in such a setting. Here, a robust control law is proposed for object manipulation in tactile-based blind grasping. The analysis ensures semi-global asymptotic and exponential stability in the presence of model uncertainties and external disturbances that are neglected in related work. Simulation and hardware results validate the effectiveness of the proposed approach.     

基于点云采样权重估计的未知物体抓取位姿生成方法

针对机械臂在非结构环境中对未知物体抓取位姿生成困难及抓取稳定性差的问题,提出一种基于点云采样权重估计的抓取位姿生成方法.首先通过移动深度相机的方式拼接得到较完整的物体点云信息,并对物体的几何特性进行分析,有效避开物体不宜抓取的位置进行抓取位姿样本生成;然后结合几何约束条件实现抓取位姿搜索,并利用力封闭条件对样本稳定性进行评估;最后为了对实际的抓取位姿进行评价,根据其稳定性、夹取深度、夹取角度等设定抓取可行性指标,据此在工作空间输出最佳抓取位姿并完成指定的抓取任务.实验结果表明,采用所提方法能够高效生成大量且稳定的抓取位姿,并在仿真环境中有效实现机械臂对单个或多个随机摆放的未知物体的抓取任务.     

虚拟手模型及其抓取技术

在深入分析手的解剖结构和运动特点的基础上，采用层次结构建模方法，建立了虚拟手模型、然后针对已有的虚拟手抓取规则过于简化的特点，提出了一种新的虚拟手抓取规则．该规则能够使虚拟手正确地抓取虚拟环境中的物体．最后运用虚拟手模型和抓取规则实现了虚拟手对虚拟物体的抓取、移动和释放操作．     

仿生假手抓握力控制策略

为了使仿生假手完成各种精细作业,提出一种抓握力控制策略.在自由空间和约束空间中分别使用基于位置的阻抗控制和力跟踪阻抗控制.在过渡过程中使用模糊观测器切换控制模式.两种控制模式采用同一个基于位置的阻抗控制器,在约束空间向阻抗控制器中引入参考力,以满足约束空间的抓握力控制要求.这种方法可以使关节在自由空间和约束空间中分别实现良好的轨迹跟踪和力矩跟踪,在过渡过程中实现控制模式的可靠切换和系统的稳定过渡.提出一种自适应滑模摩擦力补偿方法,利用终端滑模思想设计了滑模函数,使得系统跟踪误差在有限时间内收敛,避免了传统线性滑模面状态跟踪误差无法在有限时间内收敛至0的问题.根据指数形式摩擦力的特点,利用终端滑模控制思想获得包含摩擦力参数估计的滑模控制律,并基于李亚普诺夫稳定性定理推导了估计参数的在线自适应律.对该抓握力控制策略在HIT假手上进行了抓取实验,实验结果证明了控制策略的有效性.     

Contour-based next-best view planning from point cloud segmentation of unknown objects

A novel strategy is presented to determine the next-best view for a robot arm, equipped with a depth camera in eye-in-hand configuration, which is oriented to autonomous exploration of unknown objects. Instead of maximizing the total size of the expected unknown volume that becomes visible, the next-best view is chosen to observe the border of incomplete objects. Salient regions of space that belong to the objects are detected, without any prior knowledge, by applying a point cloud segmentation algorithm. The system uses a Kinect V2 sensor, which has not been considered in previous works on next-best view planning, and it exploits KinectFusion to maintain a volumetric representation of the environment. A low-level procedure to reduce Kinect V2 invalid points is also presented. The viability of the approach has been demonstrated in a real setup where the robot is fully autonomous. Experiments indicate that the proposed method enables the robot to actively explore the objects faster than a standard next-best view algorithm.     

点云模型表面物体自由拖拽定位

为实现在点云表面自由、合理地拖动其他物体,提出了一种基于硬件深度缓冲的拖拽算法。首先在鼠标按下阶段独立渲染一次指定的点云,快照出当前屏幕中该点云的深度缓冲,接着在鼠标移动阶段利用记录的深度缓冲计算参考多边形下一个位置处的质心坐标和法向,再利用相邻两个参考多边形质心和法向建立物体运动的旋转和平移分量,从而实现物体在点云表面的拖拽。实验结果表明,利用该方法可以实时、合理地在指定点云表面自由拖动任何物体。     

A differential-geometric approach for 2D and 3D object grasping and manipulation

This article presents an expository work on a differential-geometric treatment of fundamental problems of 2D and 3D object grasping and manipulation by a pair of robot fingers with multi-joints under holonomic or nonholonomic constraints. First, Lagrange’s equation of motion of a fingers-object system whose motion is confined to a vertical plane is derived under holonomic constraints when rolling contacts between finger-ends and object surfaces are permitted. Then, a class of control signals called “blind grasping” and constructed without knowing the object kinematics or using any external sensing like vision or tactile sensation is shown to realize stable object grasping in a dynamic sense. Stability of motion and its convergence to an equibrium manifold are treated on the basis of differential geometry of solution trajectories of the closed-loop dynamics on the constraint manifolds. Second, a mathematical model of 3D object grasping and manipulation by a pair of multi-joint robot fingers is derived under the assumption that spinning motion of rotation around the opposing axis between contact points does no more arise. It is shown that, differently from the 2D case, the instantaneous axis of rotation of the object is time-varying, which induces a nonholonomic constraint expressed as a linear differential equation of rotational motion of the pinched object. It is shown that there is a class of control signals constructed without knowing the object kinematics or using external sensings that can realize “blind grasping” in a dynamic sense. Finally, it is shown that the proposed differential geometric treatment of stability can naturally cope with redundancy resolution problems of surplus degrees-of-freedom (d.f.) of the overall fingers-object system, which is closely related to Bernstein’s d.f. problem.     

Adaptive inverse dynamics control of robots with uncertain kinematics and dynamics

It has been about two decades since the first globally convergent adaptive tracking controller was derived for robots with dynamic uncertainties. However, not until recently has the problem of concurrent adaptation to both the kinematic and dynamic uncertainties found its solution. This adaptive controller belongs to passivity-based control. Though passivity-based controllers have many attractive properties, in general, they are not able to guarantee the uniform performance of the robot over the entire workspace. Even in the ideal case of perfect knowledge of the manipulator parameters, the closed-loop system remains nonlinear and coupled. Thus the closed-loop tracking performance is difficult to quantify, while the inverse dynamics controllers can overcome these deficiencies. Therefore, in this work, we will develop a new adaptive Jacobian tracking controller based on the inverse manipulator dynamics. Using the Lyapunov approach, we have proved that the end-effector motion tracking errors converge asymptotically to zero. Simulation results are presented to show the performance of the proposed controller.     

A scalable model-based hand posture analysis system

Model-based hand posture analysis systems fall into two categories: hand without markers and hand with markers methods. The hand model fitting method proposed in this paper belongs to the second category. However, the main problems that make conventional marker-based hand posture analysis systems inapplicable are their inefficient calculation of the inverse kinematics, their inability to scale the size of the hand model, and their inability to analyze hand posture when some markers are occluded. In this paper, a scalable hand posture analysis system is proposed to overcome these problems. The proposed system consists of three new techniques: (1) the generation of scalable inverse kinematics solutions for the finger-positioning process, (2) a scale calibration process for the 3D hand model, and (3) a 3D marker position prediction method for occluded markers. The experimental results illustrate that the scalable hand posture analysis system outperforms conventional marker-based hand posture analysis systems.Received: 26 January 2003, Accepted: 19 July 2004, Published online: 10 February 2005     

Towards fully automatic reliable 3D acquisition: From designing imaging network to a complete and accurate point cloud

This paper describes a novel system for accurate 3D digitization of complex objects. Its main novelties can be seen in the new approach, which brings together different systems and tools in a unique platform capable of automatically generating an accurate and complete model for an object of interest. This is performed through generating an approximate model of the object, designing a stereo imaging network for the object with this model and capturing the images at the designed postures through exploiting an inverse kinematics method for a non-standard six degree of freedom robot. The images are then used for accurate and dense 3D reconstruction using photogrammetric multi-view stereo method in two modes, including resolving scale with baseline and with control points. The results confirm the feasibility of using Particle Swarm Optimization in solving inverse kinematics for this non-standard robot. The system provides this opportunity to test the effect of incidence angle on imaging network design and shows that the matching algorithms work effectively for incidence angle of 10°. The accuracy of the final point cloud generated with the system was tested in two modes through a comparison with a dataset generated with a close range 3D colour laser scanner.     

散乱点云数据曲率估计方法

针对带有强噪声离散点云数据曲率计算问题,提出一种基于稳健统计的曲率估计方法。首先,用一个二次曲面拟合三维空间采样点处的局部形状;其次,随机地选择该采样点邻域内的子集,多次执行这样的拟合过程,通过变窗宽的最大核密度估计,就得到了最优拟合曲面;最后,将采样点投影到该曲面上,计算投影点曲率信息,就得到采样点曲率。实验结果表明,所提方法对噪声和离群点是稳健的,特别是随着噪声方差的增大,要明显好于传统的抛物拟合方法。     

由点云数据重建的隐式曲面的可视化算法

针对由点云数据重建的隐式曲面提出一种新的基于粒子系统的可视化算法。首先,基于平行线束的初始化方法在隐式模型表面找到均匀分布的采样点,避免原来粒子系统中的分割-死亡过程;用共轭梯度法替代原来粒子系统中的梯度下降法作为优化算法,将每一个椭圆粒子累进移动到低能量状态,避免了较长的收敛时间和围绕最小值的摆动现象;用贪婪选择法选择能够覆盖整个曲面的且不产生空洞的活动子集;松弛过程进一步改善依赖曲率的各向异性粒子采样。本文的粒子专门为基于Splats的表示法而设计,可以直接转换为椭圆Splats而不需要任何改动。因此,本算法可以快速、高质量地绘制出复杂隐式曲面模型。     

新的点云数据精简存储方法

海量点云数据的精简存储是逆向建模的一个关键环节,针对单站地面固定式三维激光扫描点云扇形等特点,提出了一种新的点云精简存储方法--扇形网格法。对点云数据遍历一次,即完成对点云的精简、降噪与存储,并用VC++6.0编写实现。多站扫描点云的配准、拼接,如果在单站点云经过扇形网格法处理后进行,会更快速高效。在与传统点云压缩算法分析对比的基础上,对其特点进行了分析,对在战场地形数字化中的适用性进行了验证。     

一种改进的点云数据精简方法

针对Kim的算法在简化散乱点云时经常丢失过多几何特征的不足,提出一种改进的精简方法。首先对点云进行最小二乘抛物面拟合求出所有点的主曲率;然后以数据点主曲率的Hausdorff距离为依据,提取并保留点云中的特征点;最后对具有不同特征的测量数据进行了精简分析。仿真实验结果表明,改进方法既能较大程度地简化数据点云,简化结果比较均匀,又具有不破坏细小特征的特点,能够充分保留原始点云中的几何特征;而且在保证简化质量的前提下提高了算法的效率。该方法能够为后续的三维重建提供有效的数据信息,节约后续工作的处理时间和硬件资源。     

面向RGBD深度数据的快速点云配准方法

目的 真实物体的3维重建一直是计算机图形学、机器视觉等领域的研究热点。针对基于RGBD数据的非匀速非固定角度旋转物体的3维重建问题,提出一种利用旋转平台重建物体3维模型的配准方法。方法 首先通过Kinect采集位于旋转平台上目标物的深度数据和颜色数据,对齐融合并使用包围盒算法去除背景噪声和不需要的外部点云,获得带有颜色信息的点云数据。并使用基于标定物不同角度上的点云数据标定出旋转平台中心轴的位置,从而获得Kinect与旋转平台之间的相对关系;然后通过曲率特征对目标点云进行特征点提取并寻找与相邻点云的对应点;其中对于特征点的选取,首先针对点云中的任意一点利用kd-tree搜寻其k个邻近点,对这些点进行曲面拟合,进而计算其高斯曲率,将高斯曲率绝对值较大的n个点作为点云的特征点。n的取值由点云的点个数、点密度和复杂度决定,具体表现为能反映物体的大致轮廓或表面特征信息即可。对于对应点的选取,考虑到欧氏距离并不能较好反映点云中的点对在旋转过程中的对应关系,在实际配准中,往往会因为点云重叠或距离过远等原因找到大量错误的对应点。由于目标物在扫描过程中仅绕旋转轴进行旋转,因此采用圆弧最小距离寻找对应点可有效减少错误点对。随后,使用二分迭代寻找绕中心轴的最优旋转角度以满足点云间的匹配误差最小;最后,将任意角度获取的点云数据配准到统一的坐标系下并重建模型。结果 使用斯坦福大学点云数据库和自采集数据库分别对该方法和已有方法在算法效率和配准结果上进行对比实验,实验结果显示在拥有平均75 000个采样点的斯坦福大学点云数据库上与传统ICP算法和改进ICP算法相比,迭代次数分别平均减少86.5%、57.5%,算法运行时间分别平均减少87%、60.75%,欧氏距离误差平方和分别平均减少70%、22%;在具有平均57000个采样点的自采集点云数据库上与传统ICP算法和改进ICP算法相比,迭代次数分别平均减少94%、75%,算法运行时间分别平均减少92%、69%,欧氏距离误差平方和分别平均减少61.5%、30.6%;实验结果显示使用该方法进行点云配准效率较高且配准误差更小;和KinectFusion算法相比在纹理细节保留上也表现出较好的效果。结论 本文提出的基于旋转平台标定的点云配准算法,利用二分迭代算法能够有效降低算法复杂度。与典型ICP和改进的ICP算法的对比实验也表明了本文算法的有效性。另外,与其他方法在具有纹理的点云配准对比实验中也验证了本文配准方法的优越性。该方法仅采用单个Kinect即可实现对非匀速非固定角度旋转物体的3维建模,方便实用,适用于简单快速的3维重建应用场合。