提出一种基于最优化线性搜索的方法规划人形机器人的步态.通过对脚踝关节末端轨迹运动的规划和参考零力矩点(ZMP:zero moment point)轨迹的规划,将最优化泛函极值问题转换成为基于非线性约束最优化的问题,将连续空间多变量规划问题转换成为离散空间二维变量规划问题,提出了一套能够在线规划步态的控制算法.该算法的收敛性和稳定性在仿真和实物上都得到了验证.
Currently there are two approaches for a multi-class support vector classifier(SVC). One is to construct and combine several binary classifiers while the other is to directly consider all classes of data in one optimization formulation. For a K-class problem(K>2),the first approach has to construct at least K classifiers,and the second approach has to solve a much larger op-timization problem proportional to K by the algorithms developed so far. In this paper,following the second approach,we present a novel multi-class large margin classifier(MLMC). This new machine can solve K-class problems in one optimization formula-tion without increasing the size of the quadratic programming(QP) problem proportional to K. This property allows us to construct just one classifier with as few variables in the QP problem as possible to classify multi-class data,and we can gain the advantage of speed from it especially when K is large. Our experiments indicate that MLMC almost works as well as(sometimes better than) many other multi-class SVCs for some benchmark data classification problems,and obtains a reasonable performance in face recognition application on the AR face database.
