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.     

基于改进智能算法的多机器人舞蹈表演避障系统设计

多机器人舞蹈表演中受到场地大小、角度变化、外部光照等因素影响,导致机器人避障可控性不好,为了提高多机器人舞蹈表演避障能力,提出基于改进智能算法的多机器人舞蹈表演避障系统设计方法。构建多机器人舞蹈表演的步态运动学模型,采用多模态振荡稳定性调节的方法实现机器人舞蹈表演过程耦合动态调节,在翻滚、侧滚、爬行等形态模式下,采用人工智能学习算法进行机器人舞蹈表演避障学习训练,建立双层六杆闭链机构实现多机器人舞蹈表演避障姿态稳定性控制,在末端跟随运动模式下实现多机器人舞蹈表演避障系统设计。仿真测试结果表明,采用该方法进行多机器人舞蹈表演避障设计的鲁棒性较好,路径偏移量减小,能够实现机器人实时控制。     

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.     

基于自学习中枢模式发生器的仿人机器人适应性行走控制

为了克服传统中枢模式发生器(Central pattern generator, CPG)关节空间控制方法的复杂性和局限性, 本文基于自学习中枢模式发生器模型, 提出了一套在线调制和融合多传感器信息的仿人机器人环境自适应行走控制方法.算法难点在于如何在机器人的工作空间将自学习CPG用于工作空间轨迹生成, 并使CPG参数直接和步态模式相关联.本文提出了利用自学习CPG来学习和实时生成机器人质心轨迹和脚掌轨迹的方法, 在线调节机器人步长、抬腿高度和步行速度等关键参数.参考生物反射行为, 利用传感反馈信息激发CPG以产生具有环境适应性的工作空间轨迹, 提升行走质量. 控制系统的参数通过优化算法来进一步改善行走性能.相比于传统的CPG关节空间法, 本文所采用的自学习CPG工作空间法不仅极大简化了CPG网络结构而且提高了仿人机器人行走的适应性.最后, 通过仿人机器人坡面适应性行走的仿真和实验, 验证了所提出控制策略的可行性和有效性.     

一种多模型融合的仿猎豹四足机器人复杂运动控制方法

针对具有2自由度主动脊柱关节的仿猎豹四足机器人,基于任务分解思想和生物神经系统机理,提出多模型融合的控制方法。该方法以弹簧负载倒立摆模型实现单腿跳跃控制,通过中枢模式发生器（CPG）实现4条腿之间以及脊柱―腿之间的协调控制,利用虚拟模型控制实现机器人与环境交互,采用基于CPG输出的有限状态机来融合3个控制模型,构建仿猎豹四足机器人的多模型分层运动控制器。参考猎豹脊柱运动特征,设计了机器人脊柱关节运动模式,给出脊柱与腿的协调控制策略。最后,在Webots仿真环境中搭建了仿猎豹四足机器人虚拟样机,实现了不同步态下的脊柱―腿的协调控制、在崎岖地形上稳定奔跑,以及平滑的对角―疾驰―对角步态转换,仿真结果验证了所提出的多模型融合的四足机器人运动控制方法的有效性。     

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。     

基于Hopf振荡器实现的蛇形机器人的步态控制

针对生物蛇不同步态的运动特点,提出了一种基于Hopf振荡器实现的蛇形机器人的中枢模式发生器(CPG)运动控制方法.首先,利用具有非线性极限环特性的耦合的Hopf振荡器构建出能够实现蜿蜒运动和侧向蜿蜒运动两种步态的链式网络模型.然后,根据动力学仿真软件建立机器人的虚拟样机,利用模型中振荡器的输出作为蛇形机器人分布式多冗余度关节的控制信号来驱动前进,成功实现了以上两种运动方式,并讨论了CPG的模型参数与机器人前进速度的关系.最后,在实物样机上的实验进一步验证了所提出的方法在实现蛇形机器人多种步态控制方面的有效性.     

基于Fuzzy–CPG的双足机器人适应性行走控制

为提高双足机器人的环境适应性, 本文提出了一种基于模糊控制与中枢模式发生器(CPG)的混合控制策 略, 称之为Fuzzy–CPG算法. 高层控制中枢串联模糊控制系统, 将环境反馈信息映射为行走步态信息和CPG幅值参 数. 低层控制中枢CPG根据高层输出命令产生节律性信号, 作为机器人的关节控制信号. 通过机器人运动, 获取环境 信息并反馈给高层控制中枢, 产生下一步的运动命令. 在坡度和凹凸程度可变的仿真环境中进行混合控制策略的 实验验证, 结果表明, 本文提出的Fuzzy–CPG控制方法可以使机器人根据环境的变化产生适应的行走步态, 提高了 双足机器人的环境适应性行走能力.     

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。     

Development of adaptive locomotion of a caterpillar-like robot based on a sensory feedback CPG model

This paper presents a novel control mechanism for generating adaptive locomotion of a caterpillar-like robot in complex terrain. Inspired by biological findings in studies of the locomotion of the lamprey, we employ sensory feedback integration for online modulation of the control parameters of a new proposed central pattern generator (CPG). This closed-loop control scheme consists of the following stages: First, touch sensor information is processed and transformed into module states. Then, reactive strategies that determine the mapping between module states and sensory inputs are generated according to an analysis of the module states. Finally, by means of a genetic algorithm, adaptive locomotion is achieved by optimising the amount and speed of sensory input that is fed back to the CPG model. Incorporating the closed-loop controller in a caterpillar-like robot, both simulation and real on-site experiments are carried out. The results confirm the effectiveness of the control system, based on which the robot flexibly adapts to, and manages to crawl across the complex terrain.     

An adaptive locomotion controller for a hexapod robot: CPG,kinematics and force feedback

Insects can perform versatile locomotion behaviors such as multiple gaits, adapting to different terrains, fast escaping, etc. However, most of the existing bio-inspired legged robots do not possess such walking ability, especially when they walk on irregular terrains. To tackle this challenge, a central pattern generator （CPG）-based locomotion control methodology is proposed, integrated with a contact force feedback function. In this approach, multiple gaits are produced by the CFG module. After passing through a post-processing circuit and a delay-line, the control signal is fed into six trajectory generators to generate predefined feet trajectories for the six legs. Then, force feedback is employed to adjust these trajectories so as to adapt the robot to rough terrains. Finally the regulated trajectories are sent to inverse kinematics modules such that the position control instructions are generated to control the actuators. In both simulations and real robot experiments, we consistently show that the robot can perform sophisticated walking patterns. What is more, the robot can use the force feedback mechanism to deal with the irregularity in rough terrain. With this mechanism, the stability and adaptability of the robot are enhanced. In conclusion, the CPG-base control is an effective approach for legged robots and the force feedback approach is able to improve walking ability of the robots, especially when they walk on irregular terrains.     

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.     

基于循环抑制CPG 模型控制的蛇形机器人三维运动

具有三维运动能力和独特的节律运动方式，使生物蛇能在复杂的地形环境中生存. 大多数动物节律运动是由中央模式发生器(Central pattern generator, CPG) 控制的. 以此为理论依据, 首次以循环抑制建模机理构建蛇形机器人组合关节运动控制的CPG 模型. 证明该模型是节律输出型CPG 中微分方程维数最少的. 采用单向激励方式连接该类CPG 构建蛇形机器人三维运动神经网络控制体系，给出该CPG 网络产生振荡输出的必要条件. 应用蛇形机器人动力学模型仿真得到控制三维运动的CPG 神经网络参数，利用该CPG 网络的输出使\勘查者"成功实现三维运动. 该结果为建立未探明的生物蛇神经网络模型提供了一种全新的方法.     

Gaits-transferable CPG controller for a snake-like robot

With slim and legless body, particular ball articulation, and rhythmic locomotion, a nature snake adapted itself to many terrains under the control of a neuron system. Based on analyzing the locomotion mechanism, the main functional features of the motor system in snakes are specified in detail. Furthermore, a bidirectional cyclic inhibitory （BCl） CPG model is applied for the first time to imitate the pattern generation for the locomotion control of the snake-like robot, and its characteristics are discussed, particularly for the generation of three kinds of rhythmic locomotion. Moreover, we introduce the neuron network organized by the BCI-CPGs connected in line with unilateral excitation to switch automatically locomotion pattern of a snake-like robot under different commands from the higher level control neuron and present a necessary condition for the CPG neuron network to sustain a rhythmic output. The validity for the generation of different kinds of rhythmic locomotion modes by the CPG network are verified by the dynamic simulations and experiments. This research provided a new method to model the generation mechanism of the rhythmic pattern of the snake.     

Adaptive creeping locomotion of a CPG-controlled snake-like robot to environment change

In this paper, we present a biomimetic approach which is based on Central Pattern Generator (CPG) to solve the difficulty in control of a snake-like robot with a large number of degrees of freedom. A new network with a feedback connection is proposed, which can generate uniform outputs without any additional adjustment. The relations between the CPG parameters and the characteristics of output are also investigated. A simulation platform is also established for the analysis of the CPG-based locomotion control of a snake-like robot. To figure out adaptive creeping locomotion of the robot to the environment with changed friction or the given slope, the relations of CPG parameters and locomotion efficiency by the proposed curvature adaptive principle have been discussed.     

Adaptive walking control of quadruped robots based on central pattern generator （CPG） and reflex

This paper presents a central pattern generator (CPG) and vestibular reflex combined control strategy for a quadruped robot. An oscillator network and a knee-to-hip mapping function are presented to realize the rhythmic motion for the quadruped robot. A two-phase parameter tuning method is designed to adjust the parameters of oscillator network. First, based on the numerical simulation, the influences of the parameters on the output signals are analyzed, then the genetic algorithm (GA) is used to evolve the phase relationships of the oscillators to realize the basic animal-like walking pattern. Moreover, the animal’s vestibular reflex mechanism is mimicked to realize the adaptive walking of the quadruped robot on a slope terrain. Coupled with the sensory feedback information, the robot can walk up and down the slope smoothly. The presented bio-inspired control method is validated through simulations and experiments with AIBO. Under the control of the presented CPG and vestibular reflex combined control method, AIBO can cope with slipping, falling down and walk on a slope successfully, which demonstrates the effectiveness of the proposed walking control method.