液压多功能小件落物打捞器的研制与应用

由于侧钻井、水平井特殊井身结构限制，常规的小件落物打捞器打捞成功率低。为此经过认真分析和现场实践，将常规机械操作的一把抓改成液压操作，常规强磁打捞器的圆柱形平面磁铁改成具有伸缩性的磁针，同时结合套铣筒，研制出一种液压多功能小件落物打捞器，克服了侧钻井、水平井井身结构的限制，大大提高了一次打捞成功率，现场应用效果明显。     

四川南桠河一期工程姚河坝水电斜井开挖施工技术浅论

以姚河坝水电站引水隧洞50°倾角、长度为194.734m的斜井开挖为实例,从施工测量、开挖方法、通风防尘、临时支护斜井及安全施工等方面探讨长洞身、大倾角斜井在无爬罐条件下的人工开挖方法.     

Learning-based dynamic coupling of discrete and continuous trackers

We present a data-driven dynamic coupling between discrete and continuous methods for tracking objects of high dofs, which overcomes the limitations of previous techniques. In our approach, two trackers work in parallel, and the coupling between them is based on the tracking error. We use a model-based continuous method to achieve accurate results and, in cases of failure, we re-initialize the model using our discrete tracker. This method maintains the accuracy of a more tightly coupled system, while increasing its efficiency. At any given frame, our discrete tracker uses the current and several previous frames to search into a database for the best matching solution. For improved robustness, object configuration sequences, rather than single configurations, are stored in the database. We apply our framework to the problem of 3D hand tracking from image sequences and the discrimination between fingerspelling and continuous signs in American Sign Language.     

误读与纠正

分析材料试验机度盘偏转角度小 (准确度低 )、试样打滑、定义 (上屈服强度 -下屈服强度 -屈服强度 )模糊、不同应力速率等导致误读 ,并针对误读原因提出纠正方法。     

Real-time accurate hand path tracking and joint trajectory planning for industrial robots (I)

Previously, researchers raised the accuracy for a robot’s hand to track a specified path in Cartesian space mainly through increasing the number of knots on the path and the number of the path’s segments, which results in the heavier online computational burden for the robot controller. Aiming at overcoming this drawback, the authors propose a new kind of real-time accurate hand path tracking and joint trajectory planning method. Through selecting some extra knots on the specified hand path by a certain rule and introducing a sinusoidal function to the joint displacement equation of each segment, this method can greatly raise the path tracking accuracy of robot’s hand and does not change the number of the path’s segments. It also does not increase markedly the computational burden of robot controller. The result of simulation indicates that this method is very effective, and has important value in increasing the application of industrial robots. Foundation item: Foundation of the Robotics Laboratory, Chinese Academy of Sciences (No. RL200002) Biography of the first author: TAN Guan-zheng, Dr., professor, born in Oct. 1962, majoring in artificial intelligence, robotics and automation.     

羊皮服装革的弯曲应力松弛性能及其与(手感)弹性之关系研究

本文研究了羊皮服装革的弯曲应力衰减性能。对应力衰减常数（αｂ）及其与羊皮服装革手感弹性的关系进行了讨论。结果表明：αｂ与革的手感弹性相关。αｂ值定量反映出革的手感弹性优劣。     

基于视觉的多特征手势识别

手势是一种自然直观的交互方式,基于视觉的手势识别是实现新一代人机交互的关键技术。本文在已有的手势识别技术基础上,从手势分割及手势表示两方面着手,提出了一种单目视觉下的手势识别方法。利用颜色特征检测肤色区域,成功分割出人手;利用人手的轮廓及凸缺陷检测指尖,再利用指尖的数目和方位来表示一个手势,进而结合轮廓长度和面积等几何特征完成手势识别。传统的指尖检测方法需要遍历并扫描手掌外轮廓,计算量大,本文通过凸缺陷检测指尖,减少了计算量,提高了指尖检测的速度。实验结果表明,本文的方法具有很好的鲁棒性及实时性,能适应环境的变化。     

Measuring individual corrective reaction time using the intermittent illumination model

The corrective reaction time (t_cr) is an essential motor property when modelling hand control movements. Many studies designed experiments to estimate t_cr, but reported only group means with inconsistent definitions. This study proposes an alternative methodology using Drury's (1994) intermittent illumination model. A total of 24 participants performed circular tracking movements under five levels of visual information delay using a modified monitor in a darkened room. Measured movement speeds and the manipulated delays were used with the model to estimate t_cr of individuals and test effects of gender and path width. The results showed excellent model fits and demonstrated individual differences of t_cr, which was 273 ms on average and ranged from 87 to 441 ms. The wide range of t_cr values was due to significant effects of gender and path width. Male participants required shorter t_cr compared to female participants, especially for narrow path widths.     

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.     

融合Hu矩与BoF-SURF支持向量机的手势识别

基于尺度不变特征变换的特征包（BoF-SIFT）支持向量机的分类方法具有较好的手势识别效果, 但是计算复杂度高、实时性较差。为此, 提出了融合Hu矩与基于快速鲁棒特征的特征包（BoF-SURF）支持向量机（SVM）的手势识别方法。特征包模型中用快速鲁棒性特征（SURF）算法替换尺度不变特征变换（SIFT）算法提取特征, 提高了实时性, 并引入Hu矩描述手势全局特征, 进一步提高识别率。实验结果表明, 算法无论是实时性还是识别率都要高于BoF-SIFT支持向量机方法。