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．     

Near Optimal PID Controllers for the Biped Robot While Walking on Uneven Terrains

The execution of the gaits generated with the help of a gait planner is a crucial task in biped locomotion. This task is to be achieved with the help of a suitable torque based controller to ensure smooth walk of the biped robot. It is important to note that the success of the developed proportion integration differentiation (PID) controller depends on the selected gains of the controller. In the present study, an attempt is made to tune the gains of the PID controller for the biped robot ascending and descending the stair case and sloping surface with the help of two non-traditional optimization algorithms, namely modified chaotic invasive weed optimization (MCIWO) and particle swarm optimization (PSO) algorithms. Once the optimal PID controllers are developed, a simulation study has been conducted in computer for obtaining the optimal tuning parameters of the controller of the biped robot. Finally, the optimal gait angles obtained by using the best controller are fed to the real biped robot and found that the biped robot has successfully negotiated the said terrains.     

Fractional Control of a Humanoid Robot Reduced Model with Model Disturbances

There is an open discussion between those who defend mass-distributed models for humanoid robots and those in favor of simple concentrated models. Even though each of them has its advantages and disadvantages, little research has been conducted analyzing the control performance due to the mismatch between the model and the real robot, and how the simplifications affect the controller’s output. In this article we address this problem by combining a reduced model of the humanoid robot, which has an easier mathematical formulation and implementation, with a fractional order controller, which is robust to changes in the model parameters. This controller is a generalization of the well-known proportional–integral–derivative (PID) structure obtained from the application of Fractional Calculus for control, as will be discussed in this article. This control strategy guarantees the robustness of the system, minimizing the effects from the assumption that the robot has a simple mass distribution. The humanoid robot is modeled and identified as a triple inverted pendulum and, using a gain scheduling strategy, the performances of a classical PID controller and a fractional order PID controller are compared, tuning the controller parameters with a genetic algorithm.     

Hybrid control of a pneumatic artificial muscle (PAM) robot arm using an inverse NARX fuzzy model

We investigated the possibility of applying a hybrid feed-forward inverse nonlinear autoregressive with exogenous input (NARX) fuzzy model-PID controller to a nonlinear pneumatic artificial muscle (PAM) robot arm to improve its joint angle position output performance. The proposed hybrid inverse NARX fuzzy-PID controller is implemented to control a PAM robot arm that is subjected to nonlinear systematic features and load variations in real time. First the inverse NARX fuzzy model is modeled and identified by a modified genetic algorithm (MGA) based on input/output training data gathered experimentally from the PAM system. Second the performance of the optimized inverse NARX fuzzy model is experimentally demonstrated in a novel hybrid inverse NARX fuzzy-PID position controller of the PAM robot arm. The results of these experiments demonstrate the feasibility and benefits of the proposed control approach compared to traditional PID control strategies. Consequently, the good performance of the MGA-based inverse NARX fuzzy model in the proposed hybrid inverse NARX fuzzy-PID position control of the PAM robot arm is demonstrated. These results are also applied to model and to control other highly nonlinear systems.     

Adequate motion simulation and collision detection for soccer playing humanoid robots

In this paper a humanoid robot simulator based on the multi-robot simulation framework (MuRoSimF) is presented. Among the unique features of this simulator is the scalability in the level of physical detail in both the robot’s motion and sensing systems. It facilitates the development of control software for humanoid robots which is demonstrated for several scenarios from the RoboCup Humanoid Robot League.Different requirements exist for a humanoid robot simulator. E.g., testing of algorithms for motion control and postural stability require high fidelity of physical motion properties whereas testing of behavior control and role distribution for a robot team requires only a moderate level of detail for real-time simulation of multiple robots. To meet such very different requirements often different simulators are used which makes it necessary to model a robot multiple times and to integrate different simulations with high-level robot control software.MuRoSimF provides the capability of exchanging the simulation algorithms used for each robot transparently, thus allowing a trade-off between computational performance and fidelity of the simulation. It is therefore possible to choose different simulation algorithms which are adequate for the needs of a given simulation experiment, for example, motion simulation of humanoid robots based on kinematical, simplified dynamics or full multi-body system dynamics algorithms. In this paper also the sensor simulation capabilities of MuRoSimF are revised. The methods for motion simulation and collision detection and handling are presented in detail including an algorithm which allows the real-time simulation of the full dynamics of a 21 DOF humanoid robot. Merits and drawbacks of the different algorithms are discussed in the light of different simulation purposes. The simulator performance is measured and illustrated in various examples, including comparison with experiments of a physical humanoid robot.     

Symmetry position/force hybrid control for cooperative object transportation using multiple humanoid robots

A symmetry position/force hybrid control framework for cooperative object transportation tasks with multiple humanoid robots is proposed in this paper. In a leader-follower type cooperation, follower robots plan their biped gaits based on the forces generated at their hands after a leader robot moves. Therefore, if the leader robot moves fast (rapidly pulls or pushes the carried object), some of the follower humanoid robots may lose their balance and fall down. The symmetry type cooperation discussed in this paper solves this problem because it enables all humanoid robots to move synchronously. The proposed framework is verified by dynamic simulations.     

液压驱动型四足机器人电液伺服控制系统仿真建模与实验分析

复杂的作业环境和艰巨的作业任务使液压驱动型四足机器人对其伺服系统的精度、速度和力量均比一般机器人在普通情况下有更高的要求。为掌握液压驱动型四足机器人在多种路况下行走时各液压缸的受力情况以及液压系统内流量、压力的变化情况,需要对其虚拟样机进行机械动力系统和液压伺服系统的联合仿真,定性分析电液伺服系统位置、速度等被控对象的特性,并分析PID控制器在四足机器人伺服控制方面的特性与不足。针对传统控制算法在四足机器人控制存在的短板问题,设计了一种非对称前馈补偿模糊自适应PID算法,并利用物理样机进行了实际验证。实验结果为四足机器人电液伺服控制系统硬件、软件和控制算法的设计与优化指明了方向,还为研究四足机器人平稳步态控制策略提供了决策依据和数据支持。     

Realization of Dynamic Stair Climbing for Biped Humanoid Robot Using Force/Torque Sensors

This paper proposes a control algorithm for the dynamic stair climbing of a human-sized biped humanoid robot. Dynamic stair climbing can cause more instability than dynamic biped walking on the ground because stair climbing requires an additional vertical motions and a large step length. We assume that stair configuration is already known and only use force/torque sensors at ankle joint to achieve a control algorithm for a stable dynamic stair climbing. We describe a stair climbing pattern generation and stair climbing stages, and then propose a real-time balance control algorithm which is composed of several online controllers. Each online controller is addressed in detail with experimental results. Finally, the effectiveness and performance of the proposed control algorithm are verified through a dynamic stair climbing experiment of KHR-2.     

基于神经网络的仿人跑步机器人步态规划

为了快速生成仿人机器人跑步运动轨迹,研究了一种用于仿人机器人跑步步态生成的步态规划器。采用三维弹簧倒立摆模型描述跑步过程中仿人机器人质心运动规律,奔跑时机器人质心轨迹及落脚点位置可以由四个步态参数来确定,从而将步态规划问题转化成步态参数优化问题,求解了500余种不同运动状态下的步态参数。建立了基于三层BP神经网络的步态规划器,将优化结果作为训练样本训练神经网络。用上述规划器实现了仿人机器人跑步步态规划并对规划结果进行了仿真验证。研究结果表明,基于BP神经网络的步态规划器可以实现步态参数的快速计算,生成的跑步步态逼真;提出的跑步运动步态规划方法可行,为仿人机器人实时轨迹生成提供了一种解决方法。     

Gait design for an ice skating humanoid robot

Basic walking gaits are a common building block for many activities in humanoid robotics, such as robotic soccer. The nature of the walking surface itself also has a strong affect on an appropriate gait. Much work is currently underway in improving humanoid walking gaits by dealing with sloping, debris-filled, or otherwise unstable surfaces. Travel on slippery surfaces such as ice, for example, greatly increases the potential speed of a human, but reduces stability. Humans can compensate for this lack of stability through the adaptation of footwear such as skates, and the development of gaits that allow fast but controlled travel on such footwear.This paper describes the development of a gait to allow a small humanoid robot to propel itself on ice skates across a smooth surface, and includes work with both ice skates and inline skates. The new gait described in this paper relies entirely on motion in the frontal plane to propel the robot, and allows the robot to traverse indoor and outdoor ice surfaces more stably than a classic inverted pendulum-based walking gait when using the same skates. This work is demonstrated using Jennifer, a modified Robotis DARwIn-OP humanoid robot with 20 degrees of freedom.     

变论域自适应模糊PID控制系统仿真与应用

由于电液伺服系统具有非线性、时滞性等问题,自适应模糊PID控制方式仍难以精确控制马达转速,故设计了变论域自适应模糊PID控制算法。通过设计合适的伸缩因子,将变论域与自适应模糊PID控制相结合,该算法可以精确控制电磁比例阀的流量进而控制马达的转速。将PID控制、模糊控制、自适应模糊PID控制和变论域自适应模糊PID控制算法进行Matlab仿真,结果表明：变论域自适应模糊PID控制具有响应快、无超调、稳态误差基本为零的特点。通过可编程控制器实现了模糊PID控制与变论域自适应模糊PID控制算法在电液伺服系统中的应用,实验数据表明,变论域自适应模糊PID控制更加精确,符合工业控制要求。     

基于图像视觉伺服的模糊比例积分微分控制系统

针对传统比例积分微分(PID)参数难整定、控制性能不理想等问题,将模糊控制理论与PID控制器相结合,构成模糊PID控制器。采用Eye-to-Hand视觉模型,引入图像视觉伺服机制,通过图像获取误差信号来实现对PID控制器三个参数K_p、T_i和T_d的实时在线自适应调整。最后在以PC机、CompactRIO、NI-9401、互补金属氧化物半导体(CMOS)摄像头、电机驱动器及无刷直流(DC)电机组成的打孔机视觉伺服运动控制系统上完成了实验。结果表明,基于图像的视觉伺服模糊PID控制器相对于传统PID控制器响应速度提高了60%,超调量降低了80%,鲁棒性也更好;不仅能提高孔的定位精度,还能边加工边检测。     

Policy gradient learning for a humanoid soccer robot

In humanoid robotic soccer, many factors, both at low-level (e.g., vision and motion control) and at high-level (e.g., behaviors and game strategies), determine the quality of the robot performance. In particular, the speed of individual robots, the precision of the trajectory, and the stability of the walking gaits, have a high impact on the success of a team. Consequently, humanoid soccer robots require fine tuning, especially for the basic behaviors. In recent years, machine learning techniques have been used to find optimal parameter sets for various humanoid robot behaviors. However, a drawback of learning techniques is time consumption: a practical learning method for robotic applications must be effective with a small amount of data. In this article, we compare two learning methods for humanoid walking gaits based on the Policy Gradient algorithm. We demonstrate that an extension of the classic Policy Gradient algorithm that takes into account parameter relevance allows for better solutions when only a few experiments are available. The results of our experimental work show the effectiveness of the policy gradient learning method, as well as its higher convergence rate, when the relevance of parameters is taken into account during learning.     

模糊PID控制的柴油机调速系统仿真

PID控制是生产过程中应用最广泛的控制方式,但它对含变化参数的模型控制效果不理想。为了使舰船柴油机适应不断变化的工作环境,在传统PID控制的柴油机调速系统的基础上加入模糊控制环节,对PID参数进行在线整定。详细介绍了模糊PID算法在柴油机调速系统中的应用以及模糊控制器的设计,并通过MATLAB对模糊PID柴油机调速系统进行仿真,仿真结果表明,模糊自整定PID控制系统可以改善系统的动态特性,减小系统的振荡,提高系统的响应速度。     

Emergent synthesis of motion patterns for locomotion robots

Emergence of stable gaits in locomotion robots is studied in this paper. A classifier system, implementing an instance-based reinforcement-learning scheme, is used for the sensory-motor control of an eight-legged mobile robot and for the synthesis of the robot gaits. The robot does not have a priori knowledge of the environment and its own internal model. It is only assumed that the robot can acquire stable gaits by learning how to reach a goal area. During the learning process the control system is self-organized by reinforcement signals. Reaching the goal area defines a global reward. Forward motion gets a local reward, while stepping back and falling down get a local punishment. As learning progresses, the number of the action rules in the classifier systems is stabilized to a certain level, corresponding to the acquired gait patterns. Feasibility of the proposed self-organized system is tested under simulation and experiment. A minimal simulation model that does not require sophisticated computational schemes is constructed and used in simulations. The simulation data, evolved on the minimal model of the robot, is downloaded to the control system of the real robot. Overall, of 10 simulation data seven are successful in running the real robot.     

具有颜色识别功能的类人机器人设计

针对传统类人机器人在控制系统实时性和视觉识别方面的不足,以S3C6410作为主控芯片,设计了具有视觉识别功能的类人机器人控制系统,通过改进和简化视频识别算法取得了良好的目标识别效果。实验表明,基于本控制系统设计而成的类人机器人实时性好,目标识别准确,通过调整运动路径能够快速找到目标。     

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.     

Incremental motion learning through kinesthetic teachings and new motion production from learned motions by a humanoid robot

This work presents a new incremental motion learning algorithm through kinesthetic teachings and a new motion production algorithm by combining learned motions in a humanoid robot. The proposed algorithms are useful for improving the motions that a humanoid robot can produce. The learning algorithm consists of data encoding, time alignment, dimensional reduction, parameter estimation in the Gaussian mixture model (GMM) of motions, GMM refinement, and motion generation steps. The overall procedure is built to be incremental. No historic data memorization is required in any step, and model parameters are enough information to generate motions. The motion production algorithm allows a robot to extract new motions simply from learned motions without requiring teaching sessions. A series of experiments with a humanoid robot serves to validate the performance of the proposed algorithms.     

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。     

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.