Fuzzy sliding mode control of a finger of a humanoid robot hand

Abstract: The motion control problem for the finger of a humanoid robot hand is investigated. First, the index finger of the human hand is dynamically modelled as a kinematic chain of cylindrical links. During construction of the model, special attention is given to determining bone dimensions and masses that are similar to the real human hand. After the kinematic and dynamic analysis of the model, in order to ensure that the finger model tracks its desired trajectory during a closing motion, a fuzzy sliding mode controller is applied to the finger model. In this controller, a fuzzy logic algorithm is used in order to tune the control gain of the sliding mode controller; thus, an adaptive controller is obtained. Finally, numerical results, which include a performance comparison of the proposed fuzzy sliding mode controller and a conventional sliding mode controller, are presented. The results demonstrate that the proposed control method can be used to perform the desired motion task for humanoid robot hands efficiently.     

MAHRU-M: A mobile humanoid robot platform based on a dual-network control system and coordinated task execution

This paper introduces a mobile humanoid robot platform able to execute various services for humans in their everyday environments. For service in more intelligent and varied environments, the control system of a robot must operate efficiently to ensure a coordinated robot system. We enhanced the efficiency of the control system by developing a dual-network control system. The network system consists of two communication protocols: high-speed IEEE 1394, and a highly stable Controller Area Network (CAN). A service framework is also introduced for the coordinated task execution by a humanoid robot. To execute given tasks, various sub-systems of the robot were coordinated effectively by this system. Performance assessments of the presented framework and the proposed control system are experimentally conducted. MAHRU-M, as a platform for a mobile humanoid robot, recognizes the designated object. The object’s pose is calculated by performing model-based object tracking using a particle filter with back projection-based sampling. A unique approach is used to solve the human-like arm inverse kinematics, allowing the control system to generate smooth trajectories for each joint of the humanoid robot. A mean-shift algorithm using bilateral filtering is also used for real-time and robust object tracking. The results of the experiment show that a robot can execute its services efficiently in human workspaces such as an office or a home.     

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。     

RUNA: a multimodal command language for home robot users

This article describes a multimodal command language for home robot users, and a robot system which interprets users’ messages in the language through microphones, visual and tactile sensors, and control buttons. The command language comprises a set of grammar rules, a lexicon, and nonverbal events detected in hand gestures, readings of tactile sensors attached to the robots, and buttons on the controllers in the users’ hands. Prototype humanoid systems which immediately execute commands in the language are also presented, along with preliminary experiments of faceto-face interactions and teleoperations. Subjects unfamiliar with the language were able to command humanoids and complete their tasks with brief documents at hand, given a short demonstration beforehand. The command understanding system operating on PCs responded to multimodal commands without significant delay. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

A human-like real-time grasp synthesis method for humanoid robot hands

This paper addresses a real-time grasp synthesis of multi-fingered robot hands to find grasp configurations which satisfy the force closure condition of arbitrary shaped objects. We propose a fast and efficient grasp synthesis algorithm for planar polygonal objects, which yields the contact locations on a given polygonal object to obtain a force closure grasp by a multi-fingered robot hand. For an optimum grasp and real-time computation, we develop the preference and the hibernation process and assign the physical constraints of a humanoid hand to the motion of each finger. The preferences consist of each sublayer reflecting the primitive preference similar to the conditional behaviors of humans for given objectives and their arrangements are adjusted by the heuristics of human grasping. The proposed method reduces the computational time significantly at the sacrifice of global optimality, and enables grasp posture to be changeable within 2-finger and 3-finger grasp. The performance of the presented algorithm is evaluated via simulation studies to obtain the force-closure grasps of polygonal objects with fingertip grasps. The architecture suggested is verified through experimental implementation to our developed robot hand system by solving 2- or 3-finger grasp synthesis.     

Grasp capability analysis of multifingered robot hands

This paper addresses the problem of grasp capability analysis of multifingered robot hands. The aim of the grasp capability analysis is to find the maximum external wrench that the multifingered robot hands can withstand, which is an important criterion in the evaluation of robotic systems. The study of grasp capability provides a basis for the task planning of force control of multifingered robot hands. For a given multifingered hand geometry, the grasp capability depends on the joint driving torque limits, grasp configuration, contact model and so on. A systematic method of the grasp capability analysis, which is in fact a constrained optimization algorithm, is presented. In this optimization, the optimality criterion is the maximum external wrench, and the constraints include the equality constraints and the inequality constraints. The equality constraints are for the grasp to balance the given external wrench, and the inequality constraints are to prevent the slippage of fingertips, the overload of joint actuators, the excessive forces over the physical limits of the object, etc. The advantages of this method are the ability to accomodate diverse areas such as multiple robot arms, intelligent fixtures and so on. The effectiveness of the proposed method is confirmed with a numerical example of a trifingered grasp.     

仿人机器人上楼梯行走稳定性研究

针对仿人机器人上楼梯行走稳定性问题,进行了步态规划、稳定区域和稳定裕量的计算、控制策略等关键技术的研究。通过仿真,得到稳定行走姿态变化情况及各关节参数,为研究实时控制提供了依据。     

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.     

A bio-inspired predictive sensory-motor coordination scheme for robot reaching and preshaping

This paper presents a sensory-motor coordination scheme for a robot hand-arm-head system that provides the robot with the capability to reach an object while pre-shaping the fingers to the required grasp configuration and while predicting the tactile image that will be perceived after grasping. A model for sensory-motor coordination derived from studies in humans inspired the development of this scheme. A peculiar feature of this model is the prediction of the tactile image. The implementation of the proposed scheme is based on a neuro-fuzzy module that, after a learning phase, starting from visual data, calculates the position and orientation of the hand for reaching, selects the best-suited hand configuration, and predicts the tactile feedback. The implementation of the scheme on a humanoid robot allowed experimental validation of its effectiveness in robotics and provided perspectives on applications of sensory predictions in robot motor control.     

五指形仿人机械手的设计与实现及示教

设计和实现了一个具有五个手指和手掌的仿人机械手（以下简称仿人机械手）,并利用数据手套对其进行了示教,使其能有效地完成复杂的作业。首先,以现有的仿人机械手研究成果为基础,对仿人机械手进行了优化设计和实现;然后,以数据手套为示教源,对仿人机械手示教模型进行了研究,采用D-H变换矩阵建立了仿人机械手的逆向运动学,解决了示教运动映射问题。最后,利用仿人机械手进行了若干作业实验,实验结果证明了仿人机械手及其示教模型的正确性。     

分布式电源在仿人机器人控制系统中的应用

为实现仿人机器人无缆工作的时间更长,设计了采用分布式电源的控制系统.仿人机器人关节电机驱动器采用动力电和数字电分开供电的方式,动力电主要为电机供电,而数字电主要为控制芯片供电,因而采用分布式电源方案,即由一个电池组对多个关节驱动器的数字电部分供电,而对单个关节驱动器的动力电部分由一个电池组供电.该方案在搭建的多电机控制...     

e-Robot网格-仿人机器人研究与应用基础设施

目前仿人机器人的研究与应用碰到了诸如:大计算量、海量存储的需求、实验设备投入大、科研人员的协作和成果融合困难等问题。而网格具有超级计算能力、海量的存储容量、能做到网格中所有软件资源、硬件资源、人力资源的协同工作和全面共享。若能基于网格技术构建一个仿人机器人研究与应用的平台,就能解决仿人机器人研究和应用中的众多障碍。这里提出的e-Robot网格就是这样一个仿人机器人研究与应用的基础设施,它采用OGSA体系结构的基础网格,并在其基础上构建庞大的仿人机器人研究与应用服务软件集合。e-Robot网格将给仿人机器人领域的研究、应用带来变革性的进步。     

不同个性的情感机器人表情研究

在情感机器人研究中,不同个性的面部表情是情感机器人增强真实感的重要基础。为实现情感机器人更加丰富细腻的表情,将人类的个性特征引入情感机器人,分析个性理论和情感模型理论,得知不同个性机器人的情感强度。结合面部动作编码系统中面部表情与机器人控制点之间的映射关系,得到情感机器人不同个性的基本表情实现方法。利用Solidworks建立情感机器人脸部模型,在ANSYS工程软件中将SHFR-Ⅲ情感机器人脸部模型设置为弹性体,通过有限元仿真计算方法,对表情的有限元仿真方法进行了探究,得到实现SHFR-Ⅲ不同个性基本表情的控制区域载荷大小和仿真结果。最后,根据仿真结果,进行SHFR-Ⅲ情感机器人不同个性的表情动作实验。实验结果表明,有限元表情仿真可以指导SHFR-Ⅲ情感机器人实现近似人类的不同个性的基本面部表情。     

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.     

SMA丝驱动的手指康复机器人设计及实验研究

为了研制一种轻便、可穿戴的仿生手指康复机器人,在分析手指肌肉骨骼生物参数及致动机理的基础上,设计了一种形状记忆合金丝(Shape Memory Alloy, SMA)驱动的软体柔性手指康复机器人,建立了其运动学和力学模型。以手套为结构设计的原型,通过控制SMA丝的收缩来模拟手指肌肉肌腱的收缩,从而实现辅助手指屈曲伸展运动的功能。试验研究了手指康复机器人的运动性能和抓握性能。试验结果表明,手指柔性康复机器人最大弯曲角度接近正常人手关节角度,最大指尖力可达18N,能完成日常所需的屈曲伸展以及抓握功能。     

多用途欠驱动手爪的自主抓取研究

对欠驱动手爪自主抓取进行了研究,将其分为自主决策和抓取控制两个过程．首先分析了欠驱动手爪的特点、主要的抓取模式,并借鉴人的抓取经验,采用模糊输入方法,综合考虑抓取任务要求和物体本身的特征属性,利用模糊神经网络良好的分类特性选择合适的抓取模式．在此基础上,完成手指姿势调整,采用基于传感器反馈的控制策略,在被抓物体上形成的合适的力分布以获得稳定抓取,并通过抓取实例验证了抓取决策和控制的正确性,提高了欠驱动手爪抓取的自动化水平．     

Biologically inspired design of a parallel actuated humanoid robot

This paper presents the comparison for the role of bi-articular and mono-articular actuators in human and bipedal robot legs, in particular the hip and knee joint, for driving the design of a humanoid robot with inspirations from the biological system. The various constraints driving the design of both systems are also compared. Additional factors particular to robotic system are identified and incorporated in the design process. To do this, a dynamic simulation is used to determine loading conditions and the forces and power produced by each actuator under various arrangements. It is shown that while the design principles of humans and humanoids are similar, other constraints ensure that robots are still merely inspired by humans, and not direct copies. A simple design methodology that captures the complexity and constraints of such a system in this paper is proposed. Finally, a full-size humanoid robot that demonstrates the newfound principle is highlighted.     

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%.     

Multi-level offloading decision on efficient object tracking for humanoid robot

Energy efficiency is an important issue for a humanoid robot to track an object, especially robot using battery packs with limited capacity. The tracking algorithm is implemented by many functions, such as walking patterns, balance control, object recognition, path planning, etc. Performing these functions on a remote server can offload the computation on robot for energy efficiency. The offloading needs a proper decision rule in respect to the efficiency and performance. This paper presents a multi-level decision method by classifying the functions into three levels related to stability, real-time performance, and energy efficiency. Based on these levels, the decision rules are designed to offload these functions to track the object efficiently. They are validated by a humanoid robot platform called DARwIn-OP. This method could also be used to minimize the energy consumption on other tasks of a humanoid robot.     

基于接触力信息的仿人机器人ZMP测量系统

针对现有仿人机器人零力矩点(ZMP)测量系统的力/力矩传感器不直接触地导致不能充分反映脚底各部位受力的问题,设计了一种基于地面接触力信息的具有传感器阵列的ZMP测量系统。介绍了传感器信号多级放大、采集及处理的软硬件系统,应用CAN总线接口实现了与外部上位机的通信。所设计的系统已应用于实际仿人机器人。步行实验表明:该系统能有效完成步行中ZMP的实时测量和脚底各部位受力信息的实时采集、计算与通信,简单易实现。