基于CPG控制的六足爬壁机器人自主越障研究

针对基于混沌CPG(central pattern generation)模型控制的六足爬壁机器人在幕墙上自主越障爬行的问题,本文提出了一种融合障碍探测模块、位姿调整模块以及简单神经环路混沌CPG的运动控制方法,并根据幕墙障碍特点和由传感器采集到的机器人方位信息及障碍距离信息,设计了合理的越障策略。实验显示该机器人可据环境自主切换三足行进步态、四足越障步态和五足原地旋转步态,并能够调整重心与幕墙间的距离以增加稳定性,最后完成了机器人在幕墙上自主爬壁和越障。     

仿生六足机器人传感信息处理及全方向运动控制

针对仿生六足机器人的智能运动控制问题,提出一种基于中枢神经模式发生器(CPG)的控制方法.传统CPG控制方法无法很好地控制机器人足端轨迹,而本CPG控制方法中加入了用于足端轨迹控制的轨迹发生器模块,通过调节参数值可以实现机器人的全方向运动控制.为降低传统CPG控制中传感信息反馈以及参数调整的复杂程度,设计2种用于传感信号处理的神经网络.该模块实现多传感信号的融合,并生成用以控制机器人运动行为的各个参数值,实现机器人的自主避障.设计一个仿生六足机器人样机,将其分别放置在两种不同的情境下进行自主避障行走实验,结果证明了机器人全方向运动控制算法和自主避障算法的可行性.     

模块化机器人的双输出CPG网络调相运动控制

为了增强模块化机器人的灵活性,以及对其多模式运动进行更简单有效地控制,结合阵列式和串联式自重构机器人的特点,设计基于万向式关节的双自由度UBot模块,并根据两个关节之间的相互抑制关系,结合生物学的中枢模式发生器(CPG)运动控制原理,设计具有双输出的CPG网络调相运动控制器.通过调整CPG间的连接权重以及每个CPG中两组输出的关系,调解运动控制信号间的相位差值,有效地控制UBot模块化机器人的整体协调运动.应用这种控制方法控制了模块化机器人的3种运动模式:四足运动、蠕动和多节虫运动,在ADAMS仿真环境下进行了运动仿真实验,并在UBot模块化自重构平台上分别进行了3种构型的实体运动试验.实验结果验证了双输出CPG调相网络对模块化机器人运动控制的有效性和实用性.     

仿生六足机器人传感信息处理及全方向运动控制

针对传统CPG控制方法无法很好地控制机器人足端轨迹的问题,提出基于中枢神经模式发生器(CPG)的控制方法.在CPG控制方法中加入用于足端轨迹控制的轨迹发生器模块,通过调节参数值可以实现机器人的全方向运动控制.为降低传统CPG控制中传感信息反馈以及参数调整的复杂程度,设计2种用于躲避前方和两侧障碍物的传感信号处理的神经网络.该模块实现多传感信号的融合,生成用以控制机器人运动行为的各个参数值,实现机器人的自主避障.设计一个仿生六足机器人样机,将其分别放置在墙角和狭窄空间中进行自主避障行走实验,结果证明了机器人全方向运动控制算法和自主避障算法的可行性.     

基于中枢模式发生器的步态控制策略的研究

针对基于仿生学原理的足式机器人步态控制方法,探讨了基于细胞神经网络（CNN）的中枢模式发生器（CPG）实现机器人运动控制的基本思想及不足.提出了基于模糊神经网络（FNN）和CNN的CPG步态控制模型,给出了CNN中自动波稳定性的数值判别方法.FNN与遗传算法（GA）相结合形成的调整机制的引入,增强了系统的抗干扰能力.仿真结果表明,此控制策略具有较理想的效果,并且原理简单,易于实现.     

六足步行机器人定半径转弯步态

将三角步态应用于六足步行机器人定半径转弯步态中,提出基于三角步态的定半径转弯步态规划方法,简化了六足步行机器人转弯步态.将利用重心直线段轨迹跟踪定半径圆轨迹的方法运用到六足步行机器人的定半径转弯步态中,提出基于稳定性约束和腿运动约束条件下的六足步行机器人最大转弯角度的求解方法.在六足步行机器人定半径转弯步态中,对于2组腿分别作为支撑腿转弯时,采用不同的转弯角度从而有效地利用机器人转弯过程中每步的转弯能力.利用MATLAB和ADAMS软件对基于三角步态的六足步行机器人定半径转弯步态进行仿真,仿真结果验证了提出的转弯步态规划方法的正确性.     

有并联脊柱的四足机器人步态规划

为了增加足式机器人的腿部运动范围和吸收地面冲击力,在刚性躯体四足机器人的基础上,设计3-RPS并联机构作为机器人的脊柱.建立有脊柱四足机器人的运动学模型,得到脊柱关节的周期性与质心位置的关系.在对角步态的基础上,利用脊柱偏航方向的自由度,规划了脊柱扭转对角步态.采用Hopf振荡器,建立耦合中枢模式发生器（CPG）网络输出步态曲线.通过与刚性躯干四足机器人的对比仿真和实验可知,主动脊柱的加入使机器人运行过程中的俯仰波动降低45.79%,矫正了偏航位置. 脊柱关节的周期性转动不会引起质心位置的突变.脊柱波动与肢体摆动间的协调,使得有脊柱四足机器人具有更优的运动性能.     

下肢康复机器人轨迹自适应滑模阻抗控制

针对下肢康复机器人训练任务规划和主动康复控制问题,提出了一种能够根据受损患肢病况进行步态轨迹规划的策略,并设计了可实现主动康复训练的控制算法。步态轨迹规划能够根据不同患肢按需自适应调整训练任务轨迹与正常步态轨迹的偏离程度,阻抗控制可以提高训练中受损患肢的主动参与力,自适应滑膜控制可以消除机器人结构模型的参数不确定性和训练过程中因肌肉痉挛等造成的外力干扰,并且对系统的非线性有一定的鲁棒作用。实验结果表明,该方法能够有效地解决不同受损患肢步态康复训练的自适应任务规划和主动康复训练过程中的柔顺运动控制,任务轨迹跟踪精度较高。     

三肢体机器人运动分析及规划

针对一种腿臂机构功能融合设计的三肢体机器人,将其肢体操作模式作为移动模式下的特殊状态进行分析,将机器人整体的运动学分析分解为各肢体分别作为站立腿和摆动腿的运动学组合问题,实现了机器人2种工作模式运动学模型的统一,并分别进行了规划. 通过机器人步态运动仿真验证了理论分析的正确性,为机器人控制器的设计提供了理论基础.     

双下肢建模与运动控制研究

人体下肢行走为周期性规律运动,研究行走的步态特征对双足行走机器人步态规划以及动力型假肢运动控制都有很大的借鉴意义.利用VICON人体三维运动捕捉系统采集平地行走髋关节与膝关节的运动信息并进行分析.将人体简化为刚体模型,借助CAD软件Solidworks进行人体建模,利用COSMOS Motion插件进行下肢运动学分析.搭建双下肢运动平台,直流伺服电机作为驱动髋关节与膝关节运动的动力装置,通过对人体膝关节和髋关节角度信号的控制,最终达到模拟人腿的步态行走.     

Slope Terrain Locomotion Control of a Quadruped Robot Based on Biological Reflex CPG Model

Inspired by the neuronal principles underlying the tetrapod locomotion,this paper proposed a biomimetic vestibular reflex central pattern generator(CPG) model to improve motion performance and terrain adaptive ability of a quadruped robot in complex situations,which is on the basis of central pattern generator (CPG) model constructed by modified Hopf oscillators.The presented reflex model was modified in the light of the particular joint configuration of the quadruped robot and the trot gait pattern.Focusing on slop locomotion of the quadruped robot with trot gaits,the cosimulations of the ADAMS virtual prototype,CPG mathematical expressions with vestibular reflex and Simulink control model were conducted.The simulation results demonstrated that the presented CPG controller with vestibular reflex was more efficient and stable for the quadruped robot trotting on slopes,compared with the different trotting control models.     

Inter-limb and intra-limb coordination control of quadruped robots

To realize the coordinated and stable rhythmic motion of quadruped robots (QRs), the locomotion control method of QRs based on central pattern generator (CPG) was explored. In traditional control strategies based on CPG, few CPG models care about the intra-limb coordination of QRs, and the durations of stance phase and swing phase are always equal. In view of these deficiencies, a new and simpler multi-joint coordinated control method for both inter-limb and intra-limb was proposed in this paper. A layered CPG control network to realize the locomotion control of QRs was constructed by using modified Hopf oscillators. The coupled relationships among hip joints of all limbs and between hip joint and knee joint within a limb were established. Using the co-simulation method of ADAMS and MATLAB/Simulink, various gait simulation experiments were carried out and the effectiveness of the designed control network was tested. Simulation results show that the proposed control method is effective for QRs and can meet the control requirements of QRs' gaits with different duty factors.     

Orderly delay control technique for a new type of arthropod robot

The orderly delay control technique for a new type of arthropod robot is studied in this paper. The orderly delay controller is composed of three parts. The first part is a central pattern generator( CPG) with periodical output. The second part is a neural pathway( NP) that generates the time delay characteristic of various gait patterns. The last part is a locomotion nerve center( LNC) that decides the frequency of the CPG output and generates orderly phase delay by changing the parameters of NP. And then signals that fit for different gaits can be obtained through the regulation of LNC.Experiments are implemented with a robot following mathematical simulation of the controller. The experimental results show that various gait patterns can be realized successfully with the method proposed in this paper.     

Stable Transition of Quadruped Rhythmic Motion Using the Tracking Differentiator

Since the quadruped robot possesses predominant environmental adaptability, it is expected to be employed in nature environments. In some situations, such as ice surface and tight space, the quadruped robot is required to lower the height of center of gravity (COG) to enhance the stability and maneuverability. To properly handle these situations, a quadruped controller based on the central pattern generator (CPG) model, the discrete tracking differentiator (TD) and proportional-derivative (PD) sub-controllers is presented. The CPG is used to generate basic rhythmic motion for the quadruped robot. The discrete TD is not only creatively employed to implement the transition between two different rhythmic medium values of the CPG which results in the adjustment of the height of COG of the quadruped robot, but also modified to control the transition duration which enables the quadruped robot to achieve the stable transition. Additionally, two specific PD sub-controllers are constructed to adjust the oscillation amplitude of the CPG, so as to avoid the severe deviation in the transverse direction during transition locomotion. Finally, the controller is validated on a quadruped model. A tunnel with variable height is built for the quadruped model to travel through. The simulation demonstrates the severe deviation without the PD sub-controllers, and the reduced deviation with the PD sub-controllers.     

Continuous and smooth gait transition in a quadruped robot based on CPG

To improve the smoothness of motion control in a quadruped robot, a continuous and smooth gait transition method based on central pattern generator (CPG) was presented to solve the unsmooth or failed problem which may result in phase-locked or sharp point with direct replacement of the gait matrix. Through improving conventional weight matrix, a CPG network and a MATLAB/Simulink model were constructed based on the Hopf oscillator for gait generation and transition in the quadruped robot. A co-simulation was performed using ADAMS/MATLAB for the gait transition between walk and trot to verify the correctness and effectiveness of the proposed CPG gait generation and transition algorithms. Related methods and conclusions can technically support the motion control technology of the quadruped robot.     

Multi-level gait transition model for a new type of arthropod robot

To realize the continuous and variable gait transition for a new type of arthropod robot, a multi-level gait transition model is studied in this paper. The model is composed of central pattern generator （CPG） and saturation function. The CPG consists of four pairs of oscillators which can ex- hibit rhythmic activity when given stimulation signal S that lies in the range of saturation function. All oscillators receive the same S, but each pair of oscillators has different saturation functions. Multi- level gait transition can be realized when S changes regularly, as the oscillators start or stop oscilla- ting at different times. After computer simulation, the gait transition model is implemented in the ar- thropod robot. The experimental results show that ideal gait transition for the arthropod robot can be realized with the multi-level gait transition model.     

Interaction between the chaotic neural network and the CPG

The cerebral cortex is a chaotic nonlinear system. The Central Pattern Generator(CPG) can generate a rhythmic movement. According to biological knowledge, the CPG is controlled by the central nervous. But the study of the mechanism for biological motion control is still an open question. In this paper, we establish the model for depicting the interaction between the chaotic neural network and CPG. Bifurcation analysis and phase are used to describe changes in system behavior and show the interaction mechanism. In addition, the influences of CPG parameters on the model are discussed. Many modes described at state equilibrium points in the cerebral cortex correspond to gait patterns, and the change of state equilibrium points in the cerebral cortex leads to the change of gait patterns. At the same time, the results show that the brain cortex patterns can be changed by adjusting the value of the brain cortex' external input and CPG's feedback to the cerebral cortex.     

基于Hopf振荡器的仿生机器魟鱼胸鳍波形控制算法

为了获得与生物魟鱼胸鳍相近的推进波形和游动性能,提出基于Hopf振荡器的仿生中枢模式发生器（CPG）胸鳍波形控制策略. 针对仿生机器魟鱼的结构与游动特征,利用20个Hopf振荡器耦合构建中心式CPG拓扑网络模型;通过输入参数幅值、频率和波数,控制该拓扑网络模型输出仿生机器魟鱼定常巡游、加速游动和机动转弯3种游动模式下胸鳍波形的动态位置信号. 通过仿真验证了该拓扑网络模型能够快速响应输入参数的变化,稳定输出平滑、连续的动态位置信号. 通过试验研究该拓扑网络模型控制仿生机器魟鱼胸鳍波动的可行性以及网络的输入参数对仿生机器魟鱼游动性能的影响. 试验结果表明,该模型能够稳定地输出耦合的波形信号,控制仿生机器魟鱼鳍面形成与生物鱼相似的推进波形,实现各游动模式以及各游动模式间灵活平滑地切换.     

复合控制拦截弹的神经网络姿态控制

为了有效地调整复合控制拦截弹的姿态运动,提出了一个姿态控制方法。首先基于动量矩定理建立了拦截弹姿态运动的数学模型,然后针对数学模型和推力器点火逻辑的特点,设计了拦截弹姿态运动系统的神经网络控制器。使用常规方法获取基本的控制器,通过神经网络对常规控制器的控制参数进行调节,最后利用最优原理求出了拦截弹期望的俯仰控制力矩和偏航控制力矩。仿真结果表明,该姿态控制方法能够有效地实现拦截弹的精确姿态控制,并且具有较好的动态性能。     

机器人打磨自适应变阻抗主动柔顺恒力控制

为解决工业机器人打磨过程中存在复杂时变非线性耦合与不确定性扰动导致机器人柔顺恒力打磨自适应调节能力不足的问题，首先给出了一种可实现沿轴向平移与旋转运动解耦的力控末端执行器，其次设计了一种自抗扰控制器和一种粒子群神经网络变阻抗控制器分别作为内环控制和外环控制，在此基础上，提出了一种机器人自适应变阻抗主动柔顺恒力控制方法，用于在线自适应优化阻抗参数，动态调节打磨力修正量，实现机器人打磨作业自适应主动柔顺恒力控制。最后采用Lyapunov稳定性理论分析证明了所提出方法的闭环稳定性。通过机器人打磨系统虚拟样机联合仿真实验和机器人平台实物实验，验证了所提出方法的有效性。实验结果表明，所提方法能够较好实现静态与动态期望打磨力跟踪，减小了打磨力波动、力超调量以及打磨初期打磨工具处的冲击力，提高了打磨力控制系统抗扰动稳定性、恒力跟踪性能和动态响应能力，对复杂多变工况机器人打磨作业具有较强的适应性与鲁棒性。