小型仿人机器人的设计及步态规划

针对现有仿人形机器人造价高的缺点,设计一款低成本的小型双足机器人研究平台.根据人类步行过程及人体生理结构.依据模糊控制与专家控制相结合的理论提出一种简单的双足机器人模型.并根据仿生学原理确定机器人的自由度配置及各关节的比例尺寸.然后,利用目前通用的行为规划软件对双足机器人步态规划进行仿真.并在平坦地面上进行相应行走试验.实验证明.根据人行走模式对机器人进行步态规划的算法稳定可行.为机器人的教学和科研提供了良好的实验平台.     

双足机器人RCG姿态控制算法的研究

稳定步行是仿人双足机器人开展实际作业的基础,也是研究的难点和热点.为了提高对仿人机器人步行失稳的响应速率和控制准确性,克服利用陀螺仪进行姿态测量及控制无法完整表述机器人运动状态,从而造成控制滞后的缺点.本文提出了RCG姿态控制算法,在以角速度和角度作为控制参量的模型基础上,引入机器人运动过程中的加速度作为姿态判断和调整的影响因子,实现对机器人行走过程的反馈控制,提高了双足机器人对失稳状态的响应速率和响应的准确性.通过对自主搭建的机器人样机进行测试,结果表明：当双足机器人步行失稳时,RCG姿态控制算法比以角速度和角度作为参量的控制算法能够更快速、准确的修正姿态偏差,保持姿态稳定.     

A Hybrid CPG–ZMP Controller for the Real-Time Balance of a Simulated Flexible Spine Humanoid Robot

Although a few researchers have started to realize the importance of a flexible spine in generating more natural-looking behaviors for humanoid robots, there has been no work on trying to make a full-body humanoid robot (that has a flexible spine) to maintain balance. The main reason for this is that it is very costly and difficult to develop and control this kind of robot. This paper serves two purposes. First, it proposes the use of a realistic 3-D robot simulator as a platform for costly flexible spine humanoid robotics research. Second, it presents a hybrid CPG-ZMP controller for the simulated robot. The biologically inspired CPG component of our controller allows the mechanical spine and feet to exhibit rhythmic motions using only two control parameters. Through monitoring the measured ZMP location, the engineering component modulates the neural activity of the CPG to allow the robot to maintain balance while it is standing and exhibiting motions on the sagittal and frontal planes in real time. The final postures of the simulated humanoid emerge automatically in real time through dynamic interactions between the neural networks, the robot itself, and its environment. Since experimental results have also demonstrated that our system is robust against disturbances from external pushing forces, our controller has the potential to be applicable to the next generation of humanoid robots.      

小型仿人机器人的设计及步态规划

针对现有仿人形机器人造价高的缺点,设计一款低成本的小型双足机器人研究平台。根据人类步行过程及人体生理结构．依据模糊控制与专家控制相结合的理论提出一种简单的双足机器人模型,并根据仿生学原理确定机器人的自由度配置及各关节的比例尺寸。然后,利用目前通用的行为规划软件对双足机器人步态规划进行仿真,并在平坦地面上进行相应行走试验。实验证明,根据人行走模式对机器人进行步态规划的算法稳定可行,为机器人的教学和科研提供了良好的实验平台。     

基于DSP的双足机器人运动控制系统设计

在仿人机器人研究领域,双足步行控制一直是其难点。主要介绍基于TI的DSP芯片TMS320F2812设计双足机器人的基本运动控制系统,围绕机器人腿部无刷直流电机的驱动进行优化设计。系统采用PWM进行电机调速,辅助以补偿参数,通过步态指令,验证电机运转的精确性、稳定性和系统的可操作性。电机调试为CCS仿真、步态规划和独立行走提供试验平台,使机器人能够实现步行功能。     

Nonlinear support vector machine visualization for risk factor analysis using nomograms and localized radial basis function kernels

Nonlinear classifiers, e.g., support vector machines (SVMs) with radial basis function (RBF) kernels, have been used widely for automatic diagnosis of diseases because of their high accuracies. However, it is difficult to visualize the classifiers, and thus difficult to provide intuitive interpretation of results to physicians. We developed a new nonlinear kernel, the localized radial basis function (LRBF) kernel, and new visualization system visualization for risk factor analysis (VRIFA) that applies a nomogram and LRBF kernel to visualize the results of nonlinear SVMs and improve the interpretability of results while maintaining high prediction accuracy. Three representative medical datasets from the University of California, Irvine repository and Statlog dataset-breast cancer, diabetes, and heart disease datasets-were used to evaluate the system. The results showed that the classification performance of the LRBF is comparable with that of the RBF, and the LRBF is easy to visualize via a nomogram. Our study also showed that the LRBF kernel is less sensitive to noise features than the RBF kernel, whereas the LRBF kernel degrades the prediction accuracy more when important features are eliminated. We demonstrated the VRIFA system, which visualizes the results of linear and nonlinear SVMs with LRBF kernels, on the three datasets.     

基于图像核函数的图像目标识别技术研究

核方法已经广泛应用于模式识别的各个领域,但是传统的核函数仅能接受一维矢量作为输入数据,对二维图像数据需要进行一定的预处理.本文从核函数构造的角度出发,以RBF核函数为结构原型,定义了一种输入量为图像数据的新型核函数.从形式上看,本核函数仅在输人数据的类型上有所不同,其他方面完全一致,经典的基于核方法的分类器算法都可以使用本核函数.对UCI数据实验结果表明,本核函数在多种图像识别应用中均有良好的效果.     

Signal Processing and Application of Six-axis Force/Torque Sensor Integrated in Humanoid Robot Foot

Six-axis force/torque sensor (F/T sensor) that can detect three orthogonal forces and torques has been extensively adopted in humanoid robot foot for stable control. Due to the F/T sensors being seriously influenced by the electromagnetic interference from ankle generator, signal stability of F/T sensor is still one of the main influencing factors for accurately dynamical and stable control. Though the problem with influencing electromagnetic noise can be solved generally and partially by shielding of the sensor and wires, this traditional method will to some extend face implementation difficulty induced by special mounting space and application environment especially in humanoid robot ankle. Therefore, the research on the output signal stability and precision of the F/T sensors has still been one of the most essential subjects for humanoid robot dynamic equilibrium control. In this paper, some various signal processing methods have been adopted and analyzed comparatively with the aim at the output signal anti-interference processing of F/T sensors. And the filtering effect and its feasibility were verified experimentally in the dynamic walking motion of humanoid robot platform BHR-2.     

Gait Synthesis and Sensory Control of Stair Climbing for a Humanoid Robot

Stable and robust walking in various environments is one of the most important abilities for a humanoid robot. This paper addresses walking pattern synthesis and sensory feedback control for humanoid stair climbing. The proposed stair-climbing gait is formulated to satisfy the environmental constraint, the kinematic constraint, and the stability constraint; the selection of the gait parameters is formulated as a constrained nonlinear optimization problem. The sensory feedback controller is phase dependent and consists of the torso attitude controller, zero moment point compensator, and impact reducer. The online learning scheme of the proposed feedback controller is based on a policy gradient reinforcement learning method, and the learned controller is robust against external disturbance. The effectiveness of our proposed method was confirmed by walking experiments on a 32-degree-of-freedom humanoid robot.     

Real-Time Planning of Humanoid Robot's Gait for Force-Controlled Manipulation

This paper proposes a new style of manipulation by a humanoid robot. Focusing on the task of pushing an object, the foot placement is planned in real time according to the result of manipulation of the object. By using the impedance control of the arms, the humanoid robot can stably push the object regardless of the mass of the object. If the object is heavy, the humanoid robot pushes it by walking slowly and vice versa. Also, for planning the gait in real time, we propose a new analytical method where a newly calculated trajectory of the robot motion is smoothly connected to the current one. The effectiveness of the proposed method is confirmed by simulation and experiment     

Real-Time Gait Planning for Pushing Motion of Humanoid Robot

This paper describes real-time gait planning for pushing motion of humanoid robots. This method deals with an object whose mass is not known. In order that a humanoid robot pushes an unknown object in both single support phase and double support phase, real-time gait planning for pushing the unknown object is proposed. Real-time gait planning consists of zero moment point (ZMP) modification and cycle time modification. ZMP modification is the method that modifies the influence of reaction force to ZMP. By cycle time modification, the period in double support phase is modified to avoid a robot tipping over. These modifications are calculated from reaction force on arms in every cycle. With these methods, trajectory planning for pushing an unknown object in both single support phase and double support phase is calculated. Even if parameters of an object and friction coefficient on the floor vary, the robot keeps on walking while pushing an object. The effectiveness of the proposed method is confirmed by a simulation and an experiment.     

采用混合核支持向量机的DOA估计

人工神经网络( ANN)进行建模时通常需要准备大量的数据样本,同时网络结构一般都比较复杂;而采用支持向量机( SVM)进行建模时,不同核函数有不同的效果,各有利弊,且选取SVM模型参数的理论支撑尚不完整。为了解决这些问题,提出了一种基于混合核函数的支持向量机来改善来波到达角( DOA)的估计性能,并结合二进制粒子群算法( PSO)来对混合核函数进行参数寻优。该混合核函数由全局核函数和局部核函数构成,提高了SVM的泛化能力和学习能力。首先通过拟合多项式函数,验证了该混合核SVM的有效性。将该方法用于DOA估计建模,在不同信噪比和快拍数下,通过与径向基函数( RBF)神经网络、基于各单一核函数的SVM和MUSIC算法预测结果对比,混合核SVM均方差有所降低,提高了DOA估计的精度且有更好的稳定性。     

Application of kernel principal component analysis for single-lead-ECG-derived respiration

Recent studies show that principal component analysis (PCA) of heartbeats is a well-performing method to derive a respiratory signal from ECGs. In this study, an improved ECG-derived respiration (EDR) algorithm based on kernel PCA (kPCA) is presented. KPCA can be seen as a generalization of PCA where nonlinearities in the data are taken into account by nonlinear mapping of the data, using a kernel function, into a higher dimensional space in which PCA is carried out. The comparison of several kernels suggests that a radial basis function (RBF) kernel performs the best when deriving EDR signals. Further improvement is carried out by tuning the parameter σ(2) that represents the variance of the RBF kernel. The performance of kPCA is assessed by comparing the EDR signals to a reference respiratory signal, using the correlation and the magnitude squared coherence coefficients. When comparing the coefficients of the tuned EDR signals using kPCA to EDR signals obtained using PCA and the algorithm based on the R peak amplitude, statistically significant differences are found in the correlation and coherence coefficients (both p<0.0001), showing that kPCA outperforms PCA and R peak amplitude in the extraction of a respiratory signal from single-lead ECGs.     

考虑关节角加速度约束的仿人机器人偏摆力矩控制方法

针对仿人机器人步行过程中存在的机器人关节角加速度约束影响控制性能的问题，提出一种考虑关节角加速度约束的仿人机器人偏摆力矩控制方法.该方法充分考虑了双臂在摆动过程中对偏摆力矩的影响，根据力矩平衡条件得到需要抵消的偏摆力矩的大小与方向，将偏摆力矩的控制问题转化为带约束条件的二次规划问题，并设计了一种在线变步长迭代算法计算得到优化后的双臂摆动轨迹.实验表明，该方法能有效抵消机器人步行中产生的偏摆力矩，避免控制过程中的"削峰"现象，有效提高机器人的步行稳定性.     

尺度核函数支撑矢量机

本文提出了一种可容许的支撑矢量机核—尺度核.该尺度核函数可以被看作是一个具有平移因子的多维尺度函数,它能作为平方可积空间的子空间上一组完备的基函数.在此意义上,采用尺度核函数的支撑矢量机,可以认为是在尺度空间中寻找最佳的尺度系数.因此在理论上尺度核函数支撑矢量机能够以零误差逼近某一空间上的任何目标函数,文中给出的仿真实验进一步验证了它的可行性和有效性.     

凸组合核函数的支持向量机高光谱图像分类

支持向量机的高光谱图像分类中,单核函数存在局限性。为了提高分类器的分类精度和支持向量机模型的泛化能力,利用高斯径向基核和多层感知核进行凸组合构造复合核函数支持向量机,证明了该函数满足作为核函数的判决Mercer条件,并进一步将凸组合核函数支持向量机应用到高光谱图像分类中,完成了建模和实验验证。实验结果表明,凸组合核函数具有较好的鲁棒性,且该类支持向量机的分类精度和KAPPA系数较单核SVM均得到了有效的提高,是一种解决多分类问题行之有效的分类器。     

基于EMD-SVM的江水浊度预测方法研究

针对江水浊度序列宽频、非线性、非平稳的特点,将经验模态分解(EMD)和支持向量机(SVM)回归方法引入浊度预测领域,建立了基于EMD-SVM的浊度预测模型.通过EMD分解,将原始非平稳的浊度序列分解为若干固有模态分量(IMF),根据各IMF序列的特点,选择不同的参数对各IMF序列进行预测,最后合成原始序列的预测值.将该方法应用于实际浊度预测,并与径向基神经网络(RBF)预测及单独支持向量机回归预测结果进行比较,仿真结果表明该方法预测精度有明显提高.     

Exoskeletal master device for dual arm robot teaching

Dual arm robots are used as humanoid or industrial robots for assembly work. Each arm of these robots is generally composed of 7-DOF to mimic the human arm. For motion teaching of this 7-DOF robot arm, upper limb exoskeletal master devices can be used, and each arm of the upper limb exoskeletal master device can also be composed of 7-DOF. However, the motions of the human shoulder are complex and the lack of DOF in the exoskeletal master device limits the wearer's motions and makes the wearer feel uncomfortable. We propose a compact-sized exoskeletal master device, each of whose arms are composed of two serially connected parts. One is a 6-DOF shoulder–elbow mechanism and the other is a 3-DOF wrist mechanism. The 6-DOF mechanism serially connects the base frame near the shoulder to a point on the forearm, and the center of rotation of the shoulder joint is shifted to the outside of the human shoulder. In addition, the 6-DOF mechanism includes a long stroke but short reduced-length prismatic joint. This 6-DOF mechanism enables unconstrained and comfortable shoulder motions. The 3-DOF wrist mechanism corresponds to forearm pronation/supination, wrist flexion/extension, and wrist adduction/abduction. The closed-loop inverse kinematic scheme is applied for the dual arm robot control in the task space. The performance of the exoskeletal master devices and control strategies are verified through experiments using a 14-DOF dual arm slave robot.     

System Design and Control of Anthropomorphic Walking Robot LOLA

This paper presents the 25-DOF full-size humanoid robot LOLA . Our goal is to realize fast and human-like walking. Furthermore, we want to increase the robot’s autonomous, vision-guided walking capabilities. LOLA is characterized by a redundant kinematic configuration, an extremely lightweight design, joint actuators with brushless motors and an electronics architecture using decentralized joint control. Special emphasis was put on an improved mass distribution to achieve good dynamic performance. Center of mass trajectories are calculated in real-time from footstep locations using a spline collocation method. Reference trajectories are modified by a stabilizing control system based on hybrid force/position control with an inner joint position control loop.