面向草莓抓取的气动四叶片软体抓手研制

农林业中果蔬的自动化采摘需求日趋强烈,末端抓手是实现无损采摘的关键。传统的末端抓手以刚性结构居多,现有的各种柔性抓手也存在抓取力不足、包覆性不佳等缺点。本文以草莓的无损采摘为研究对象,提出将草莓外部轮廓曲线作为设计曲线,设计了一种新型气动四叶片软体抓手。首先,对软体抓手的结构做仿真优化,提出一种安全地附着在目标物表面的设想。然后,在进行草莓表面的最小破坏应力试验的基础上,测试了软体抓手的末端力,验证了其实现无损抓取的可行性。再次,利用动态捕捉技术,研究了软体抓手叶面的弯曲变形规律。最后,选择使用弧线型气体通道的软体抓手进行了草莓抓取测试,结果证明了气动四叶片软体抓手可以实现草莓的无损抓取,抓取成功率达90%,破损率为2%,表明所研制的四叶片软体抓手用于草莓抓取时具有良好的稳定性和实用性,可用于草莓采摘的末端执行器。本研究也可为其他易损果蔬的采摘技术提供理论基础和技术支撑。     

The visual control of reaching and grasping: Binocular disparity and motion parallax.

The primary visual sources of depth and size information are binocular cues and motion parallax. Here, the authors determine the efficacy of these cues to control prehension by presenting them in isolation from other visual cues. When only binocular cues were available, reaches showed normal scaling of the transport and grasp components with object distance and size. However, when only motion parallax was available, only the transpont component scaled reliably. No additional increase in scaling was found when both cues were available simultaneously. Therefore, although equivalent information is available from binocular and motion parallax information, the latter may be of relatively limited use for the control of the grasp. Binocular disparity appears selectively important for the control of the grasp. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Grasping objects localized from uncertain point cloud data

Robotic grasping is very sensitive to how accurate is the pose estimation of the object to grasp. Even a small error in the estimated pose may cause the planned grasp to fail. Several methods for robust grasp planning exploit the object geometry or tactile sensor feedback. However, object pose range estimation introduces specific uncertainties that can also be exploited to choose more robust grasps. We present a grasp planning method that explicitly considers the uncertainties on the visually-estimated object pose. We assume a known shape (e.g. primitive shape or triangle mesh), observed as a–possibly sparse–point cloud. The measured points are usually not uniformly distributed over the surface as the object is seen from a particular viewpoint; additionally this non-uniformity can be the result of heterogeneous textures over the object surface, when using stereo-vision algorithms based on robust feature-point matching. Consequently the pose estimation may be more accurate in some directions and contain unavoidable ambiguities.The proposed grasp planner is based on a particle filter to estimate the object probability distribution as a discrete set. We show that, for grasping, some ambiguities are less unfavorable so the distribution can be used to select robust grasps. Some experiments are presented with the humanoid robot iCub and its stereo cameras.     

基于虚拟力引导的人机协同目标抓取方法

针对力反馈遥操作中传统人工势场法无法适应于机械臂整体的避障以及在作业过程中操作者难以控制机械臂到达所需位姿的问题,提出了一种基于虚拟力引导的人机协同目标抓取方法。力反馈设备向操作者提供力觉交互。通过结合人工势场法和虚拟夹具,构建管道形虚拟力场,生成实时虚拟力引导,实现协助操作者完成从端机器人的整体避障任务并在完成避障后引导机器人返回预定义路径并趋近目标点。当进行抓取任务时,构建锥形虚拟力场,实现协助操作者操作机械臂到达目标位置和姿态。此外,提出了一种机器人运动限制方法以降低操作者的操作失误对抓取任务的影响。实验证明,该方法能有效提高目标抓取操作的成功率和操作效率。     

南水北调配套工程大宁调蓄水库西堤防渗墙施工技术

北京市南水北调配套工程大宁调蓄水库工程西堤防渗采用塑性混凝土防渗墙布置,＂两站一抓＂法成槽,＂泥浆下直升＂法浇筑,＂接头管＂法槽段连接。大宁水库西堤防渗墙施工投入足够资源,选择实力强大、信誉良好的施工队伍,为类似工程的施工管理提供了较好的实例。     

一种基于非线性弹簧模型的虚拟手交互新方法

基于虚拟手的交互技术在人机交互和人机工程学测试等应用中发挥着重要的作用。为了实现直观自然、实时准确、接近真实世界中的虚拟手与虚拟物体的交互,并计算出反馈作用力,首先提出了用非线性弹簧模型计算抓取作用力,使虚拟手和虚拟环境之间实现了基于物理的交互;然后将计算结果以视觉渲染的形式反馈给用户,并对仿真的速率做了定量分析,以便使仿真速率可以达到屏幕刷新频率和力反馈刷新频率的要求。实验结果表明,虚拟手不仅可以直观自然地抓取3维虚拟物体,而且和3维物体之间能够进行实时交互,同时可计算出反馈作用力。     

Towards robust grasping: An analysis of in-hand object motion with FBG optical fibers as force sensing technology

To enable or allow for advanced manipulation scenarios in the envisaged future in robotic manipulation, the introduction of tactile or force-sensing technology is essential. This work introduces a study on a novel integration of fiber sensing elements inside the fingers of robotics grippers that allow obtaining accurate interaction forces during object manipulation. The study focuses on in-hand object motion and analyzes the objects’ dynamic behaviors during grasping or contact phases. The proposed approach classifies the behavior of the gripper-object interaction in real-time by looking at the measured forces in the orthogonal direction of the gripper’s fingers after a filtering stage. The methodology is validated theoretically and evaluated with real experiments using objects presenting different shapes, sizes, and surface properties.     

Slippage control in soft finger grasping and manipulation

Handling objects with robotic soft fingers without considering the odds of slippage are not realistic. Grasping and manipulation algorithms have to be tested under such conditions for evaluating their robustness. In this paper, a dynamic analysis of rigid object manipulation with slippage control is studied using a two-link finger with soft hemispherical tip. Dependency on contact forces applied by a soft finger while grasping a rigid object is examined experimentally. A power-law model combined with a linear viscous damper is used to model the elastic behavior and damping effect of the soft tip, respectively. In order to obtain precise dynamic equations governing the system, two second-order differential equations with variable coefficients have been designed to describe the different possible states of the contact forces accordingly. A controller is designed based on the rigid fingertip model using the concept of feedback linearization for each phase of the system dynamics. Numerical simulations are used to evaluate the performance of the controller. The results reveal that the designed controller shows acceptable performance for both soft and rigid finger manipulation in reducing and canceling slippage. Furthermore, simulations indicate that the applied force in the soft finger manipulation is considerably less than the rigid “one.”.     

A Theoretical Approach of an Intelligent Robot Gripper to Grasp Polygon Shaped Objects

This paper presents an approach for grasp planning and grasp forces optimization of polygon shaped objects. The proposed approach is an intelligent rule-based method that figures out the minimal number of fingers and minimal values of contact forces. These fingers are required to securely grasp a rigid body in the presence of friction and under the action of some external force. This is accomplished by finding optimal contact points on the object boundary along with minimal number of fingers required for achieving the aforementioned goal. Our system handles every object case independently. It generates a rule base for each object based on adequate values of external forces. The system uses the genetic algorithm as its search mechanism, and a rule evaluation mechanism called bucket brigade for the reinforcement learning of the rules. The process mainly consists of two stages; learning then retrieval. Retrievals act on line utilizing previous knowledge and experience embedded in a rule base. If retrievals fail in some cases, learning is presumed until that case is resolved. The algorithm is very general and can be adapted for interface with any object shape. The resulting rule base varies in size according to the degree of difficulty and dimensionality of the grasping problem.