面向冗余机器人实时控制的逆运动学求解有效方法

针对7自由度冗余机器人实时运动控制,对机器人逆运动学提出了一种新的求解方法.采用位姿分解方式,使7自由度冗余机器人逆运动学简化为4自由度位置逆运动学求解.在梯度投影法得到位置优化解的基础上,利用机器人封闭解公式求得一组优化解.通过对7自由度机器人仿真分析,表明了该方法的有效性.     

An admittance based hierarchical control framework for dual-arm cobots

Dual-arm robotic platforms have solid arguments to match the growing need for versatility in the industry. Coupling the control of two manipulators for cooperative purposes enlarges the scope of feasible operations, while adding perception capabilities allows to navigate in dynamic environments. In this respect, we propose a complete online kinematic control framework for dual-arm robots operating in unstructured industrial settings. We base our approach on admittance control in the cooperative task space. Regulating internal and external efforts offer safe bimanual task execution and enables physical interaction. We implement a hierarchical quadratic programming architecture that applies a prioritization of tasks: most efforts are concentrated on the proper tracking of relative motions of the arms, which is the most critical for safety reasons. We demonstrate the performance of our framework through a “teaching-by-demonstration” experiment on the dual-arm mobile cobot BAZAR.     

基于改进型NSCT 变换的灰度可见光与红外图像融合方法

针对灰度可见光与红外图像融合,提出一种基于改进型非下采样轮廓波变换(NSCT)的图像融合方法.不同于经典NSCT模型,改进型NSCT变换摈弃了细节捕捉能力不强的非下采样金字塔分解机制,采用冗余提升不可分离小波变换实现对源图像的多尺度分解;然后,分别采用基于区域平均能量匹配度、邻域系数差和信息熵的融合规则,得到融合图像的低频系数和高频系数;最后,通过改进型NSCT逆变换得到了融合图像.实验结果验证了该方法的有效性.     

基于多线程的音频设备上位机控制系统

设计一种音频设备的上位机控制系统,实现对多台音频设备的集中和远程控制。为了保证上位机在与设备通信的同时,能及时响应前台界面的用户操作,引入了多线程技术。同时,音频设备具有经常被持续、快速调节同一参数的特点,为解决在此情况下由于硬件通信速度有限而导致的设备滞后响应上位机用户操作的问题,设计了一种冗余命令剔除机制。该机制不仅有效地减少了通信占用的资源,而且更好地保证了用户对设备调节控制的实时性。     

基于S7-300／400PLC的冗余控制系统在变压吸附（PSA）制氢装置中的应用

本文介绍了分别基于西门子S7—300PLC和S7—400PLC的两种冗余控制系统在荆门石化和鞍钢变压吸附制氢装置中的应用。概述了工艺过程和工艺对控制系统的要求,着重对上述两套系统的硬件配置和软件编程组态实施方案进行了描述。运行结果表明,上述系统稳定可靠,控制效果良好,能满足工艺要求。     

基于贝叶斯背景模型的免携带设备目标定位算法

免携带设备目标定位不需要目标携带任何电子设备或标签来对人或其他物体进行定位。针对现有射频层析成像算法在多径环境中定位精度不理想的问题,提出了一种基于贝叶斯背景模型的定位算法。该算法首先将斜拉普拉斯分布和贝叶斯理论相结合来建立贝叶斯背景模型,用于排除冗余链路;然后对接收信号强度的变化值进行加权处理,从而减小多径效应对目标定位的干扰;最后引入目标位置的后验估计均值对目标位置进行修正,提高定位精度。实验结果表明,该定位算法具有可行性和有效性。     

Reliable representations for association rules

Association rule mining has contributed to many advances in the area of knowledge discovery. However, the quality of the discovered association rules is a big concern and has drawn more and more attention recently. One problem with the quality of the discovered association rules is the huge size of the extracted rule set. Often for a dataset, a huge number of rules can be extracted, but many of them can be redundant to other rules and thus useless in practice. Mining non-redundant rules is a promising approach to solve this problem. In this paper, we first propose a definition for redundancy, then propose a concise representation, called a Reliable basis, for representing non-redundant association rules. The Reliable basis contains a set of non-redundant rules which are derived using frequent closed itemsets and their generators instead of using frequent itemsets that are usually used by traditional association rule mining approaches. An important contribution of this paper is that we propose to use the certainty factor as the criterion to measure the strength of the discovered association rules. Using this criterion, we can ensure the elimination of as many redundant rules as possible without reducing the inference capacity of the remaining extracted non-redundant rules. We prove that the redundancy elimination, based on the proposed Reliable basis, does not reduce the strength of belief in the extracted rules. We also prove that all association rules, their supports and confidences, can be retrieved from the Reliable basis without accessing the dataset. Therefore the Reliable basis is a lossless representation of association rules. Experimental results show that the proposed Reliable basis can significantly reduce the number of extracted rules. We also conduct experiments on the application of association rules to the area of product recommendation. The experimental results show that the non-redundant association rules extracted using the proposed method retain the same inference capacity as the entire rule set. This result indicates that using non-redundant rules only is sufficient to solve real problems needless using the entire rule set.     

Robot arm reaching through neural inversions and reinforcement learning

We present a neural method that computes the inverse kinematics of any kind of robot manipulators, both redundant and non-redundant. Inverse kinematics solutions are obtained through the inversion of a neural network that has been previously trained to approximate the manipulator forward kinematics. The inversion provides difference vectors in the joint space from difference vectors in the workspace. Our differential inverse kinematics (DIV) approach can be viewed as a neural network implementation of the Jacobian transpose method for arm kinematic control that does not require previous knowledge of the arm forward kinematics. Redundancy can be exploited to obtain a special inverse kinematic solution that meets a particular constraint (e.g. joint limit avoidance) by inverting an additional neural network The usefulness of our DIV approach is further illustrated with sensor-based multilink manipulators that learn collision-free reaching motions in unknown environments. For this task, the neural controller has two modules: a reinforcement-based action generator (AG) and a DIV module that computes goal vectors in the joint space. The actions given by the AG are interpreted with regard to those goal vectors.     

西门子S7—300软冗余PLC在垃圾焚烧发电厂自控系统中的应用

本文主要介绍西门子S7—300软冗余PLC系统在垃圾焚烧发电厂中的应用,以已经竣工的“深圳市医疗废物处置中心”的自动控制系统为例详细描述了软冗余系统的设计选型、编程以及在小型垃圾发电厂中的应用。     

西门子S7系列PLC在成都地铁BAS系统中的应用

本系统以西门子S7-414H冗余控制器为主站,多台S7—200PLC和S7—300PLC为从站,通过光纤链路模块OLM组成冗余光纤环网,实现地铁设备监控系统的完美控制。文章重点介绍了控制工艺背景、主／从网络组成构架、硬件配置及软件编程．要点、系统中包含的PROFIBUS-DP、MODBUS、网络读写等通讯协议出现的问题及解决方法、调试后达到的效果及应用体会。