基于CPG模型的仿生机器鱼运动控制

仿生机器鱼的研究已经成为一个富有挑战性的热点问题.为了控制机器鱼自身的运动和姿态,本文研究了胸鳍对机器鱼运动的影响,并且基于CPG模型,提出了一种运动控制方法.采用的控制模型由4个振荡器构成,可根据反馈的信息产生节律信号以控制机器鱼胸鳍和尾鳍的运动.根据CPG模型参数与反馈输入之间的关系,设计了机器鱼俯仰和转弯反馈控制方法,利用反馈的信息自主调节CPG参数,达到控制胸鳍运动模式的目的.仿真实验验证了控制模型和反馈策略的有效性.     

机器海豚多模态游动CPG控制

受自然界海豚超凡的水中游动技能启发,机器海豚在军事和民用上具有潜在的广泛应用前景,因此受到研究人员的极大关注. 然而,要实现机器海豚在水中自如地机动游动,必须为机器海豚设计一个具有丰富游动技能的多模态控制器. 为此,通过振荡器建模与分析、中枢模式发生器（Central pattern generation,CPG）与机器海豚关节配对、CPG单元间耦合等环节建立了机器海豚的链式弱耦合CPG运动控制模型,提出一种基于CPG激发产生多模态振荡波形控制机器海豚运动的方法. 详细阐述了机器海豚样机研制、控制器设计、运动控制实现与实验测试等内容. 向前直游、转弯、浮潜等游动实验结果验证了所提出的机器海豚CPG运动控制方法的有效性和实用性.     

两类仿鲹科机器鱼倒游运动控制方法的对比研究

给出并比较了两类分别采用鱼体波动方程和中枢模式发生器（Central pattern generator,CPG）控制仿鲹科机器鱼倒游运动的方法.前者主要通过修改鱼体波动方程、颠倒机器鱼各个关节的控制规律来实现 鱼体倒游;后者则基于CPG模型,产生各个关节的节律控制信号.基于CPG的倒游方法可进一步细分为两种：1） 相位颠倒的CPG控制方法,即通过逆转CPG控制机器鱼直游的相位关系;2） 相位-幅值颠倒的CPG控制方法,即通过逆转鱼体波的传播方向和摆动幅值来实现机器鱼倒游.文中针对这两大类、三种机器鱼倒游运动控制方法 进行了分析、仿真和实验.实验结果表明：在相同参数配置下,采用相位颠倒的CPG控制方法产生的倒游速度最大,但游动对水的扰动也最大;而采用鱼体波倒游和相位-幅值颠倒的CPG控制方法时,两者产生的最大倒游速度相差不大,扰动较小.此外,采用鱼体波倒游方法在频率切换时会有抖动现象,需要设计专门的过渡函数来消除;而采用CPG模型的方法 则可以实现平滑过渡.上述结果对提高水下游动机器人的机动性能具有重要的指导意义.     

仿生机器鱼胸/尾鳍协同推进闭环深度控制

为改善机器鱼定深控制过程中的动态性能与稳态性能,根据深度误差的大小将定深控制过程分解为趋近阶段与巡游阶段,给出了一种基于中枢模式发生器(central pattern generator,CPG)与模糊控制相结合的闭环运动控制方法.为此,首先建立了以压力传感器信号为反馈输入,通过模糊控制器调节控制参数的CPG运动控制模型.在此基础上,针对误差较大的趋近阶段,采用胸/尾鳍协同方式,通过趋近模糊控制器改变摇翼关节的偏置量与幅值来使机器鱼快速到达期望深度;针对误差较小的巡游阶段,采用改变攻角方式,通过巡游模糊控制器改变胸鳍攻角来使机器鱼保持在期望深度.两阶段之间通过胸鳍CPG的启停实现切换.模糊控制器设计时利用了基于最小二乘法对实验数据拟合而得出的俯仰角变化率与控制参数的近似关系,提高了机器鱼趋向期望深度的速度并减小了在期望深度巡游时的稳态误差.仿真与实验结果验证了所提控制方法的有效性.     

基于CPG的仿生环形长鳍波动推进器运动控制

为了克服一般仿生水下机器人稳定性与机动性的不足,提出一种仿生环形长鳍波动推进器及其控制方法.根据环形长鳍波动推进器的结构特征和推进机理,提出了基于中枢模式发生器(CPG)的运动控制方法.该方法通过相邻耦合的方式,对波动推进器中20个频率和幅值可独立控制的神经元振荡器进行了建模,构建了一种用于该推进器的CPG网络模型.仿真分析了对称波形、非对称波形和环形波形推进控制方式下控制模型中的各振荡子输出信号,以及各参数对输出信号的影响,并试验研究了波形参数对样机游动速度和转弯速度的影响.试验结果显示样机具有一定的稳定性与机动性,直线游动速度和原地转弯速度随波动频率和波动幅值的增大而增大,最大直线游动速度可达109 mm/s,最大原地转弯速度可达93?/s.仿真及试验结果证明了此CPG控制模型的可行性和有效性.     

动物运动指令的中枢模式发生器对机器人运动控制的启示

动物运动指令的中枢模式发生器（central pattern generator, CPG）在动物的节律运动中发挥着重要的作用,对机器人的仿生控制方法研究具有借鉴意义.首先介绍了CPG的神经环路和控制机制,然后分析了组成CPG的非线性振荡器的典型数学模型,接着介绍了利用CPG进行机器人运动控制在国内外的发展现状,最后展望了其应用前景.     

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．     

仿生机器鱼运动控制方法综述

运动控制是仿生机器鱼研究的核心问题,为此,依据解决运动控制问题的不同思路,总结了仿生机器鱼运动控制常见的几种研究方法:基于杆系结构的鱼体波曲线拟合法、正弦控制器方法和基于中枢模式发生器模型的方法,分别对3种运动控制方法的基本原理和特点进行了总结和归纳,分析了3种方法在可靠性、稳定性和实时性等方面的优缺点,最后指出了仿生机器鱼运动控制方法的发展趋势。     

基于CPG的六足机器人运动步态控制方法

中枢模式发生器（CPG）在六足机器人的运动步态控制中起着至关重要的作用。为了研究六足机器人的运动控制方法,首先基于仿生学原理设计了六足机器人的机械结构,并在虚拟样机软件ADAMS中搭建其三维模型;其次选择Hopf振荡器作为CPG单元,并改进了振荡器模型;然后设计了六足机器人的CPG网络拓扑结构,包含单腿关节映射函数方案和腿间CPG环形耦合网络方案,并对其进行了改进;最后通过ADAMS和MATLAB联合仿真实验,验证了所设计六足机器人的运动稳定性和CPG控制方案的可行性与有效性。仿真结果表明,该方法能够满足六足机器人不同运动步态的控制需求,对六足机器人的运动控制具有一定的实际应用价值。     

基于双层级CPG的3维蛇形机器人运动控制方法

为实现3维蛇形机器人多模式运动控制,提出了一种基于双层级中枢模式发生器(CPG)的运动控制方法.该双层级CPG网络包含节律层和模式层,节律层的CPG神经元用于控制3维蛇形机器人的俯仰关节组和偏转关节组的相位关系,模式层的CPG神经元用于控制3维蛇形机器人关节组内各个关节的相位差及关节轨迹.首先,利用Kuramoto振荡器对CPG神经元进行建模,并确定CPG网络的层级结构和耦合拓扑;然后,基于蛇形约束曲线计算3维蛇形机器人侧滚运动、侧移运动、滑行运动及转向运动4种典型运动步态的控制参数;最后,通过联合仿真和实验验证该双层级CPG网络的控制性能.由实验结果可知,3维蛇形机器人的侧滚运动、侧移运动、滑行运动以及转向运动的实际速度分别能够达到3.9 cm/s、9.0 cm/s、2.1 cm/s和10.8°/s.因此,该方法能够有效地、灵活地控制3维蛇形机器人的多模式运动.     

A Simplified CPGs Network with Phase Oscillator Model for Locomotion Control of a Snake-like Robot

CPG (Central pattern generator) is a dynamical system of coupled nonlinear oscillators or neural networks inspired by a control mechanism in animal bodies. Without any rhythmic inputs, the CPG has the ability to produce oscillatory patterns. This paper presents a novel structure of a CPG network which can produce rhythmic motion that imitates movement of animals such as snake and lamprey. The focus is on the locomotion control of a snake-like robot, where phase oscillator has been adopted as the dynamical model to control the harmonic motion of the CPG network. There are two main points addressed in this paper: (1) simple network structure of unidirectional coupling oscillators, and (2) a single parameter to control the body shape and to control the forward and backward movement of the snake-like robot. The proposed CPG network is designed to have a simple structure with less complexity, less mathematical computation, fast convergence speed and exhibit limit cycle behavior. In addition, a new parameter, τ is introduced to control the smoothness of the CPG output as well as the speed of the snake-like robot. Simulation and experimental results show that the proposed CPG network can be used to control the serpentine locomotion of a snake-like robot.     

Central pattern generators based on Matsuoka oscillators for the locomotion of biped robots

Biologically inspired control approaches based on central pattern generators (CPGs) with neural oscillators have been drawing much attention for the purpose of generating rhythmic motion for biped robots with human-like locomotion. This article describes the design of a neural-oscillator-based gait-rhythm generator using a network of Matsuoka oscillators to generate a walking pattern for biped robots. This includes the proper consideration of the oscillator’s parameters, such as a time constant for the adaptation rate, coupling factors for mutual inhibitory connections, etc., to obtain a stable and desirable response from the network. The article examines the characteristics of a CPG network with six oscillators, and the effect of assigning symmetrical and asymmetrical coupling coefficients among oscillators within the network structure under different possible inhibitions and excitations. The kinematics and dynamics of a five-link biped robot have been modeled, and its joints are actuated through simulation by the torques output from the neural rhythm generator to generate the trajectories for hip, knee, and ankle joints. The parameters of the neural oscillators are tuned to achieve flexible trajectories. The CPG-based control strategy is implemented and tested through a simulation. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

刺激下可变耦合神经振子群活动的非线性随机演化模型

考虑耦合强度随时间变化,提出在外刺激及噪声共同作用下神经振子群活动的动力学模型,并引入平均耦合对数密度作为神经振子群分布式时空编码模式.通过数值分析表明,一阶弱谐波刺激对神经振子群体编码没有显著的影响;强刺激或高阶谐波刺激加强了神经振子群的同步化活动,并增强了神经振子之间的耦合;不同频率谐波的组合刺激对神经编码的影响并不是相互独立的,而是具有某种非线性关系,且刺激强度较大的谐波主导神经编码.     

基于中枢神经模式产生器机器人的控制网络

分析了基于中枢神经模式产生器(Central Pattern Generator,CPG)的仿人机器人控制网络系统结构的特点,介绍了振荡器的数学模型。研究了CPG网络中各神经元的刺激方式,采用Hopf非线性振荡器构造神经元,模仿人类的行走步态,设计一种6关节控制网络。计算仿真中该网络输出信号稳定,运动节奏符合设计要求。最后,应用一仿人机器人完成了实验,提高了其行走的速度和稳定性,验证了该网络的有效性。     

Wave CPG model for autonomous decentralized multi-legged robot: Gait generation and walking speed control

In this paper, we propose a method to control gait generation and walking speed control for an autonomous decentralized multi-legged robot by using a wave Central Pattern Generator (CPG) model. The wave CPG model is a mathematical model of nonlinear oscillators and generates rhythmic movements of the legs. The gait generation and the walking speed control are achieved by controlling the virtual energy of the oscillators (Hamiltonian). A real robot experiment showed the relationship to the Hamiltonian, the actual energy consumption and the walking speed, and the effectiveness of the proposed method was verified.     

CPG-based control of a turtle-like underwater vehicle

This paper presents biologically inspired control strategies for an autonomous underwater vehicle (AUV) propelled by flapping fins that resemble the paddle-like forelimbs of a sea turtle. Our proposed framework exploits limit cycle oscillators and diffusive couplings, thereby constructing coupled nonlinear oscillators, similar to the central pattern generators (CPGs) in animal spinal cords. This paper first presents rigorous stability analyses and experimental results of CPG-based control methods with and without actuator feedback to the CPG. In these methods, the CPG module generates synchronized oscillation patterns, which are sent to position-servoed flapping fin actuators as a reference input. In order to overcome the limitation of the open-loop CPG that the synchronization is occurring only between the reference signals, this paper introduces a new single-layered CPG method, where the CPG and the physical layers are combined as a single layer, to ensure the synchronization of the physical actuators in the presence of external disturbances. The key idea is to replace nonlinear oscillators in the conventional CPG models with physical actuators that oscillate due to nonlinear state feedback of the actuator states. Using contraction theory, a relatively new nonlinear stability tool, we show that coupled nonlinear oscillators globally synchronize to a specific pattern that can be stereotyped by an outer-loop controller. Results of experimentation with a turtle-like AUV show the feasibility of the proposed control laws.     

Construction and Central Pattern Generator-Based Control of a Flipper-Actuated Turtle-Like Underwater Robot

This paper deals with the construction and control of a turtle-like underwater robot with four mechanical flippers. Each flipper consists of two joints generating a rowing motion by a combination of lead-lag and feathering motions. With cooperative movements of four flippers, the robot can propel and maneuver in any direction without rotation of its main body and execute complicated three-dimensional movements, including ascending, submerging, rolling and hovering. The control architecture is constructed based on a central pattern generator (CPG). A model for a system of coupled nonlinear oscillators is established to construct a CPG and has been successfully applied to the eight-joint turtle-like robot. The CPGs are modeled as nonlinear oscillators for joints and inter-joint coordination is achieved by altering the connection weights between joints. Rowing action can be produced by modulating the control parameters in the CPG model. The CPG-based method performs elegant and smooth transitions between swimming gaits, and enhanced adaptation to the transient perturbations due to nonlinear characteristics. The effectiveness of the proposed method is confirmed via simulations and experimental results.     

Smooth transition between different gaits of a hexapod robot via a central pattern generators algorithm

This paper focuses on the topic of smooth gait transition of a hexapod robot by a proposed central pattern generator (CPG) algorithm. Through analyzing the movement characteristics of the real insects, it is easy to generate kinds of gait patterns and achieve their smooth transition if we employ a series of oscillations with adjustable phase lag. Based on this concept, a CPG model is proposed, which is constructed by an isochronous oscillators and several first-order low-pass filters. As an application, a hexapod robot and its locomotion control are introduced by converting the CPG signal to robot’s joint space. Simulation and real world experiment are completed to demonstrate the validity of the proposed CPG model. Through measuring the position of the body center and the distance between footpoints and ground, the smooth gait transition can be achieved so that the effectiveness of the proposed method is verified.     

Learning model for coupled neural oscillators.

Neurophysiological experiments have shown that many motor commands in living systems are generated by coupled neural oscillators. To coordinate the oscillators and achieve a desired phase relation with desired frequency, the intrinsic frequencies of component oscillators and coupling strengths between them must be chosen appropriately. In this paper we propose learning models for coupled neural oscillators to acquire the desired intrinsic frequencies and coupling weights based on the instruction of the desired phase pattern or an evaluation function. The abilities of the learning rules were examined by computer simulations including adaptive control of the hopping height of a hopping robot. The proposed learning rule takes a simple form like a Hebbian rule. Studies on such learning models for neural oscillators will aid in the understanding of the learning mechanism of motor commands in living bodies.