Balance recovery control for biped robot based on reaction null space method

A biped walking robot should be able to keep balance even in the presence of disturbing forces. This paper presents a step strategy concept of biped walking robot that is stabilized by using reaction null space method. The called ＂step strategy＂ can be modeled by means of the reaction null space method that introduced earlier to tackle dynamic interaction problems of free-floating robots, or moving base robots in general. 6-DOF biped robot model simulations are used to confirm the validity.

基于NSMC方法的某核工程场地裂隙地下水数值模拟不确定分析

裂隙介质渗透结构表现为高度的非均质性与各项异性。为了科学有效地预测某核工程场地裂隙地下水的流动规律,揭示裂隙岩体地下水的渗流特性,笔者等采用Pilot Point调参方法与null space Monte Carlo方法(NSMC),开展了裂隙岩体渗透结构的不确定性分析研究,构建了符合实际水文地质条件的多个渗流数值模型集合。结果表明:该方法获得的各个实现地下水位模拟结果能够与实际观测数据较好吻合,可反映工程场地裂隙地下水动力特征与流动趋势;各个实现的参数化渗透结构在空间上存在一定的差异性,但整体变化趋势是保持一致的,渗透参数的不确定性表现为在实测数据分布区域相对较低,钻孔空白区域相对较高;该方法可以弥补单一、确定性模拟结果在表征裂隙介质渗透结构方面的局限性,有效地降低模型参数的不确定性与随机性。此方法对进一步提升裂隙岩体渗流模拟精度与预测能力,深化裂隙地下水迁移规律的认识具有重要的意义。

Sparse recovery in probability via l_q-minimization with Weibull random matrices for 0 < q ≤ 1

Although Gaussian random matrices play an important role of measurement matrices in compressed sensing, one hopes that there exist other random matrices which can also be used to serve as the measurement matrices. Hence, Weibull random matrices induce extensive interest. In this paper, we first propose the lrobust null space property that can weaken the D-RIP, and show that Weibull random matrices satisfy the lrobust null space property with high probability. Besides, we prove that Weibull random matrices also possess the lquotient property with high probability. Finally, with the combination of the above mentioned properties,we give two important approximation characteristics of the solutions to the l-minimization with Weibull random matrices, one is on the stability estimate when the measurement noise e ∈ R~n needs a priori ‖e‖≤ε, the other is on the robustness estimate without needing to estimate the bound of ‖e‖. The results indicate that the performance of Weibull random matrices is similar to that of Gaussian random matrices in sparse recovery.

Pre-impact trajectory planning for minimizing base attitude disturbance in space manipulator systems for a capture task

Aimed at capture task for a free-floating space manipulator, a scheme of pre-impact trajectory planning for minimizing base attitude disturbance caused by impact is proposed in this paper.Firstly, base attitude disturbance is established as a function of joint angles, collision direction and relative velocity between robotic hand and the target.Secondly, on the premise of keeping correct capture pose, a novel optimization factor in null space is designed to minimize base attitude disturbance and ensure that the joint angles do not exceed their limits simultaneously.After reaching the balance state, a desired configuration is achieved at the contact point.Thereafter, particle swarm optimization（PSO） algorithm is employed to solve the pre-impact trajectory planning from its initial configuration to the desired configuration to achieve the minimized base attitude disturbance caused by impact and the correct capture pose simultaneously.Finally, the proposed method is applied to a 7-dof free-floating space manipulator and the simulation results verify the effectiveness.

A Null Space Approach for Solving Nonlinear Complementarity Problems

In this work, null space techniques are employed to tackle nonlinear complementarity problems （NCPs）. NCP conditions are transform into a nonlinear programming problem, which is handled by null space algorithms, The NCP conditions are divided into two groups, Some equalities and inequalities in an NCP are treated as constraints, While other equalities and inequalities in an NCP are to be regarded as objective function. Two groups are all updated in every step. Null space approaches are extended to nonlinear complementarity problems. Two different solvers are employed for all NCP in an algorithm.

Weighted Moore-Penrose inverses and fundamental theorem of even-order tensors with Einstein product

We treat even-order tensors with Einstein product as linear operators from tensor space to tensor space, define the mill spaces and the ranges of tensors, and study their relationship. We extend the fundamental theorem of linear algebra for matrix spaces to tensor spaces. Using the new relationship, we characterize the least-squares （M） solutions to a multilineax system and establish the relationship between the minimum-norm （N） leastsquares （M）solution of a multilinear system and the weighted Moore-Penrose inverse of its coefficient tensor. We also investigate a class of even-order tensors induced by matrices and obtain some interesting properties.

Facial Feature Extraction Method Based on Coefficients of Variances

Principal Component Analysis （PCA） and Linear Discriminant Analysis （LDA） are two popular feature extraction techniques in statistical pattern recognition field. Due to small sample size problem LDA cannot be directly applied to appearance-based face recognition tasks. As a consequence, a lot of LDA-based facial feature extraction techniques are proposed to deal with the problem one after the other. Nullspace Method is one of the most effective methods among them. The Nullspace Method tries to find a set of discriminant vectors which maximize the between-class scatter in the null space of the within-class scatter matrix. The calculation of its discriminant vectors will involve performing singular value decomposition on a high-dimensional matrix. It is generally memory- and time-consuming. Borrowing the key idea in Nullspace method and the concept of coefficient of variance in statistical analysis we present a novel facial feature extraction method, i.e., Discriminant based on Coefficient of Variance （DCV） in this paper. Experimental results performed on the FERET and AR face image databases demonstrate that DCV is a promising technique in comparison with Eigenfaces, Nullspace Method, and other state-of-the-art facial feature extraction methods.

Some Rank Equalities about Combinations of Two Idempotent Matrices

The paper researches the rank of combinations a PA＋bAQ-cPAQ of two idempotent matrices P and Q.Using the properties of the idempotent matrix and elementary block matrix operation,we get some rank equalities for combinations a PA＋bAQ-cPAQ of two idempotent matrices P and Q.These rank equalities generalize the results of Koliha J J,Rakoevi V and Tian Y,and give some applications of the rank equalities.