具有7自由度和双球型髋关节的仿人机器人下肢运动分析与规划

提出了具有7自由度和双球型髋关节的仿人机器人下肢机构．它和传统的6自由度双足步行机构相比，具有下述两大优点．首先双球型髋关节使机器人在不增加腰部关节的情况下实现腰部的基本运动功能，使机器人能够直立行走；其次在给定腰和足部位置时，它也能和人类一样实现转动双腿的动作．本文还对该复杂机构进行了运动特性分析、运动学求解、运动规划和实验验证．     

基于弹簧—质量模型的仿袋鼠跳跃机器人步态稳定性研究

根据袋鼠跳跃运动的特点，建立了仿袋鼠机器人的弹簧—质量模型．采用拉格朗日法通过线性化近似分析建立了其动力学方程，并对其进行了着地阶段径向与横向运动分析．根据步态运动稳定性的条件，研究了仿袋鼠跳跃机器人顶点反射映像函数、固定点的存在性及其稳定性，获得了仿袋鼠机器人在跳跃过程中满足步态稳定性的各个参数的取值范围，以及各参数之间关系的显式表达式．最后，通过实例证明了采用线性化近似分析的有效性，并获得了仿袋鼠机器人稳定跳跃时各参数之间的变化关系．     

一种仿婴儿欠自由度四足爬行机器人

受婴儿爬行时独特的躯体形态的启发,设计了具有柔性脊柱和弹性膝关节的欠自由度四足爬行机器人BabyBot,其脊柱为变截面通体柔顺结构,膝关节为无自由度可变形被动关节.利用伪刚体法对柔性脊柱和弹性膝关节的结构参数进行设计,采用中枢模式发生器(CPG)运动控制模型生成对角爬行步态轨迹,柔顺机构与仿生控制有机结合形成了BabyBot机器人"以膝着地、腰髋耦合"的仿婴儿爬行步态.对欠自由度仿婴儿机器人的机构可行性,以及柔性脊柱对机器人运动性能的影响进行仿真及实验,结果表明,具有弹性膝关节的欠自由度四足机器人可以实现平稳的爬行运动,变截面柔性脊柱能够减小机器人行走时躯干在横滚及偏转方向的姿态波动程度,提高了机器人运动的协调性和轨迹准确性,并揭示出婴儿爬行时脊柱的柔顺运动对稳定视觉的潜在作用.     

基于模糊优化的仿人机器人运动规划研究

文章主要是对仿人型两足机器人(简称仿人机器人)的运动稳定性进行分析、研究,建立了一套动力学模型,并运用模糊优化的方法分析其运动参数.实验表明:该方法在很大程度上提高了仿人两足机器人的运动稳定性.     

一种基于气动柔性驱动器的仿人机械手设计

为了提高柔性机械手结构刚性，保证其柔顺性以及实现灵巧运动，本文基于柔性驱动器技术，分析人手的外形、结构和运动特点，采取以气动柔性驱动器作为驱动关节的驱动方式，再结合刚性机械结构，设计出一种具有气动柔性手指、拇指对掌关节、刚性掌部的仿人机械手，并对其进行了姿态及抓握实验。实验结果表明，该仿人机械手具有良好的灵巧性和稳定的抓取能力。     

仿人机器人复杂动作设计中人体运动数据提取及分析方法

提出了仿人机器人复杂动作设计中人体运动数据提取及分析方法. 首先, 通过运动捕捉系统获取人体运动数据, 并采用运动重定向技术, 输出人--机简化模型的数据; 然后, 对运动数据进行分析和运动学解算, 给出基于人体运动数据的仿人机器人逆运动学求解方法, 得到仿人机器人模型的关节角数据; 再经过运动学约束和稳定性调节后, 生成能够应用于仿人机器人的运动轨迹. 最终, 通过在仿人机器人BHR-2上进行刀术实验验证了该方法的有效性.     

基于CPG的沙漠蜘蛛机器人多模式运动控制方法

模仿具有多种运动模式的沙漠蜘蛛,设计了本体为双层六杆5R闭链机构的仿蜘蛛机器人,其中16个主动关节由直流伺服电机控制．提出了基于Hopf振荡器的中枢模式发生器（CPG）运动控制模型,用于实现仿蜘蛛机器人的翻滚、爬行、侧滚等多种运动模式以及步态切换．利用Matlab和ADAMS对仿蜘蛛机器人的多模式运动进行动力学仿真,结果表明机器人可实现连续平稳的翻滚、爬行、侧滚运动,验证了CPG仿生控制方法应用于闭链机器人多模式运动的可行性．     

仿人机器人两步步态规划算法研究

为了进一步提高仿人机器人步行时的稳定性,通过对人类步行的研究,并从两足步行机的两步步态规划方法中得到启发,对仿人机器人步行也进行类似的两步规划,但由于结构上的不同,仿人机器人中采用加入上肢运动补偿的方式实现平衡.规划仿人机器人的运动姿态,然后根据零力矩点必须落在稳定区域的原则,对仿人机器人的上肢运动轨迹进行求解,通过这种加入上肢补偿的两步规划来实现仿人机器人的稳定步行.从实验结果可以看出,采用这种两足步态规划方法,在仿人机器人两足步行时,可以使机器人上肢与下肢协调运动,从而提高了步行的稳定性.     

面向仿人机器人自然步态规划的人体步行实验分析

自然步态规划方法是实现仿人机器人步态柔顺和能量优化的可行方法,该方法要求对人体步行及其平衡策略进行定量研究．本文分析自然步态规划方法的原理,建立了一套快捷有效的人体步态测试系统,并通过实验建立了人体步行的参数化数据库．实验结果揭示了人体步行的参数化特征及其平衡策略,对于仿人机器人的自然步态规划及控制提供了理论指导．结论特别指出,仅仅通过规划的方式实现仿人机器人的自然步态是不完备的,自然步态的实现必须同仿生控制策略相结合．同时实验结论对于仿人机器人的本体优化设计也提供了参考．     

仿人机器人复杂动态动作设计及相似性研究

提出了一种基于人体运动的考虑节奏相似性的仿人机器人复杂动态动作设计方法. 首先, 把人体的运动分割成基本动作段, 给出了运动学约束, 讨论了复杂动态动作的稳定性调节方法. 然后, 提出了考虑运动节奏的仿人机器人模仿人体动作的相似性函数, 并给出了满足运动学约束和动力学稳定性、具有高相似性的运动轨迹求解方法. 最后, 通过在仿人机器人 BHR-2 上进行中国功夫``刀术'实验验证了该方法的有效性.     

Fuzzy control-based real-time robust balance for a humanoid robot

This paper studies and implements a real-time robust balance control for a humanoid robot under three environment disturbances which are an external thrust, an inclinable platform, and a see-saw. More precisely to say, the robot with robust control can resist an external thrust, stand on a two-axis inclinable platform, or walk on a see-saw successfully. The main feature of the robot is that it has a waist joint which has three degrees of freedom. With the aids of the proposed fuzzy controllers, the robot can change the posture of the body nimbly by adjusting the waist joint and two ankle joints to strengthen the stabilization capacity. The sensory system of the robot includes eight force sensors and one inertial measurement unit sensor in order to measure the center of pressure and the slant angle of the robot’s body. According to the measured data from the sensors and by imitating human reflex actions, the proposed fuzzy controllers perform real-time balance control for the robot under three environment disturbances. According to the experiment results, the stability of the robot is increased at least 32.2 and 61.7% under the first two environment disturbances, respectively. In addition, the robot walking on a see-saw has a success rate of about 95%.     

五指形仿人机械手的数学模型研究

以人手的解剖学研究成果为基础，对具有五个手指和手掌的仿人机械手（以下简称仿人机械手）的数学模型进行研究。首先，以现有的工业机器人研究成果为基础，提出了仿人机械手的坐标系建立方法。然后，从人手的解剖学特点出发，采用D—H变换矩阵建立了仿人机械手运动学模型。     

基于Kinect的仿人机器人控制系统

为实现对具有16个自由度仿人机器人的姿态控制,采用Kinect传感器对人体姿态的坐标数据进行采集,根据坐标信息利用Processing软件开发基于SimpleOpenNI库的上位机软件,建立人体关节模型,并利用空间向量法对仿人机器人的步态规划以及重心控制算法分析,解析各关节的转动角度,经由无线WiFi模块向仿人机器人发送指令以控制舵机的运动,最终实现对机器人的控制,搭建了基于Kinect传感器的测试平台.测试结果表明:仿人机器人上肢在运动范围内无死角,通过对重心的控制,下肢可实现简单的步行,符合预期效果.     

仿人机器人控制系统的研究与实现

根据仿人机器人控制性能的要求,设计开发了关节控制器,并通过CAN总线把各个关节控制器、力传感器及上位机连接在一起,构成了分布式控制系统．利用无线局域网技术,实现了语音、视频等多媒体信息的传输,把监控台、头部、上身和移动平台连接在一起,构成了仿人机器人完整的控制系统．最后提出了一些设想以提高系统的性能．     

Development of flexible joints for a humanoid robot that walks on an oscillating plane

In this study, we develop flexible joints for a humanoid robot that walks on an oscillating plane and discuss their effectiveness in compensating disturbances. Conventional robots have a rigid frame and are composed of rigid joints driven by geared motors. Therefore, disturbances, which may be caused by external forces from other robots, obstacles, vibration and oscillation of the surface upon which the robot is walking, and so on, are transmitted directly to the robot body, causing the robot to fall. To address this problem, we focus on a flexible mechanism. We develop flexible joints and incorporate them in the waist of a humanoid robot; the experimental task of the robot is to walk on a horizontally oscillating plane until it reaches the desired position. The robot with the proposed flexible joints, reached the goal position despite the fact that the controller was the same as that used for a conventional robot walking on a static plane. From these results, we conclude that our proposed mechanism is effective for humanoid robots that walk on an oscillating plane.     

Stepping over obstacles with humanoid robots

The wide potential applications of humanoid robots require that the robots can walk in complex environments and overcome various obstacles. To this end, we address the problem of humanoid robots stepping over obstacles in this paper. We focus on two aspects, which are feasibility analysis and motion planning. The former determines whether a robot can step over a given obstacle, and the latter discusses how to step over, if feasible, by planning appropriate motions for the robot. We systematically examine both of these aspects. In the feasibility analysis, using an optimization technique, we cast the problem into global optimization models with nonlinear constraints, including collision-free and balance constraints. The solutions to the optimization models yield answers to the possibility of stepping over obstacles under some assumptions. The presented approach for feasibility provides not only a priori knowledge and a database to implement stepping over obstacles, but also a tool to evaluate and compare the mobility of humanoid robots. In motion planning, we present an algorithm to generate suitable trajectories of the feet and the waist of the robot using heuristic methodology, based on the results of the feasibility analysis. We decompose the body motion of the robot into two parts, corresponding to the lower body and upper body of the robot, to meet the collision-free and balance constraints. This novel planning method is adaptive to obstacle sizes, and is, hence, oriented to autonomous stepping over by humanoid robots guided by vision or other range finders. Its effectiveness is verified by simulations and experiments on our humanoid platform HRP-2.     

Bilateral remote teaching and autonomous task execution with task progress feedback

This paper describes an approach to estimating the progress in a task executed by a humanoid robot and to synthesizing motion based on the current progress so that the robot can achieve the task. The robot observes a human performing whole body motion for a specific task, and encodes these motions into a hidden Markov model (HMM). The current observation is compared with the motion generated by the HMM, and the task progress can be estimated during the robot performing the motion. The robot subsequently uses the estimate of the task progress to generate a motion appropriate to the current situation with the feedback rule. We constructed a bilateral remote control system with humanoid robot HRP-4 and haptic device Novint Falcon, and we made the humanoid robot push a button. Ten trial motions of pushing a button were recorded for the training data. We tested our proposed approach on the autonomous execution of the pushing motion by the humanoid robot, and confirmed the effectiveness of our task progress feedback method.     

Wheeled inverted pendulum type assistant robot: design concept and mobile control

This paper describes the design concept of the human assistant robot I-PENTAR (Inverted PENdulum Type Assistant Robot) aiming at the coexistence of safety and work capability and its mobile control strategy. I-PENTAR is a humanoid type robot which consists of a body with a waist joint, arms designed for safety, and a wheeled inverted pendulum mobile platform. Although the arms are designed low-power and lightweight for safety, it is able to perform tasks that require high power by utilizing its self-weight, which is the feature of a wheeled inverted pendulum mobile platform. I-PENTAR is modeled as a three dimensional robot; with controls of inclination angle, horizontal position, and steering angle to achieve high mobile capability. The motion equation is derived considering the non-holonomic constraint of the two-wheeled mobile robot, and a state feedback control method is applied for basic mobile controls wherein the control gain is calculated by the LQR method. Through several experiments of balancing, linear running, and steering, it was confirmed that the robot could realize stable mobile motion in a real environment by the proposed controller.     

Realizing whole-body tactile interactions with a self-organizing,multi-modal artificial skin on a humanoid robot

In this paper, we present a new approach to realize whole-body tactile interactions with a self-organizing, multi-modal artificial skin on a humanoid robot. We, therefore, equipped the whole upper body of the humanoid HRP-2 with various patches of CellulARSkin – a modular artificial skin. In order to automatically handle a potentially high number of tactile sensor cells and motors units, the robot uses open-loop exploration motions, and distributed accelerometers in the artificial skin cells, to acquire its self-centered sensory-motor knowledge. This body self-knowledge is then utilized to transfer multi-modal tactile stimulations into reactive body motions. Tactile events provide feedback on changes of contact on the whole-body surface. We demonstrate the feasibility of our approach on a humanoid, here HRP-2, grasping large and unknown objects only via tactile feedback. Kinesthetically taught grasping trajectories, are reactively adapted to the size and stiffness of different test objects. Our paper contributes the first realization of a self-organizing tactile sensor-behavior mapping on a full-sized humanoid robot, enabling a position controlled robot to compliantly handle objects.