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。     

在线考虑运动学限制的最小加速度的轨迹规划

提出了一种基于简化运动规划的机器人轨迹规划新方法,可用于多自由度的机器人操作臂系统。关键问题是找到最小加速度的轨迹规划,来优化操作臂的运动以减少抖动。此外,给出了轨迹规划的解存在的充分必要条件,并考虑了所有的关节位置、角速度、加速度、加加速度等运动学限制。而且这种方法能够在线应用,适合任意非零的关节初始状态和目标状态,以便使机器人能够在运动过程中进行实时路径修正。最后提出的方法应用于一个七自由度的仿人机器人手臂来验证方法的有效性。     

基于三维线性倒立摆的仿人机器人步态规划

为了实时调整仿人机器人的步态,提出一种仿人机器人的步态生成方法。把机器人运动简化为三维线性倒立摆运动模式,通过预先规划好的零力矩点(ZMP)轨迹,根据质心(CoM)和ZMP的关系,求出CoM轨迹;再将前向步态和侧向步态简化为七连杆结构和五连杆结构,利用三角定理求出各个关节的角度,结合ZMP方程讨论了行走过程中的稳定性。利用给定的条件进行了系统的仿真,并结合实际系统及其运行状况进行分析,验证了所提出规划方法的有效性。     

快速连续反应-避障作业环境下的七自由度灵巧臂轨迹规划

人类经长期学习训练后能对高速物体 (如棒球、乒乓球等)具有快速连续反应作业的运动技能, 从深层次上揭示是由于人体在其训练过程中不断学习优选了相应手臂的动作轨迹, 并储存了丰富的经验和知识. 受人体手臂动作此行为机制启发, 本文提出一种 7-DOF灵巧臂快速连续反应-避障作业的轨迹规划方法. 该方法将灵巧臂对高速物体目标作业的轨迹规划问题转化为动作轨迹参数化优选问题, 考虑作业过程中灵巧臂的机构物理约束和障碍约束条件, 以灵巧臂目标可作业度指标构建适应度函数, 采用粒子群优化 (Particle swarm optimization, PSO)方法优选作业轨迹中的冗余参数; 在此基础上 利用灵巧臂动作轨迹参数化优选方法构建相应作业环境下的知识数据库, 实现灵巧臂对高速物体目标的快速连续反应作业. 以仿人机器人乒乓球对弈作业为例, 将该方法应用于 7-DOF灵巧臂乒乓球作业的轨迹规划中. 数值实验及实际对弈试验结果表明, 该方法不仅能使灵巧臂所规划的轨迹 满足灵巧臂机构物理约束与障碍约束条件, 同时能实现灵巧臂对乒乓球体的快速连续反应作业, 验证了该方法的有效性.     

基于三质心模型的类人机器人射门算法

为了实现快速稳定的射门动作,提出了一种基于三质心模型的类人机器人射门轨迹规划方法。首先,根据三质心模型,得到包含游动腿轨迹和躯干轨迹的零力矩点(ZMP)方程, 采用三次贝塞尔曲线规划游动腿轨迹和ZMP轨迹,根据ZMP方程求解出类人机器人的躯干轨迹;其次,在双腿支撑相根据线性摆模型计算类人机器人的质心轨迹,实现射门姿态的快速调整;最后,在RoboCup 3D仿真平台中应用此算法实现了类人机器人的快速射门动作,并与其他球队的射门动作进行了对比。实验结果表明：应用该算法仅需手工调试即可快速实现稳定的射门动作,射门动作时间有很大减少,可增强机器人足球队的竞争力。     

An efficient motion generation method for redundant humanoid robot arms based on motion continuity

This paper proposes a novel method of motion generation for redundant humanoid robot arms, which can efficiently generate continuous collision-free arm motion for the preplanned hand trajectory. The proposed method generates the whole arm motion first and then computes the actuators’ motion, which is different from IK (inverse kinematics)-based motion generation methods. Based on the geometric constraints of the preplanned trajectory and the geometric structure of humanoid robot arms, the wrist trajectory and elbow trajectory can be got first without solving inverse kinematics and forward kinematics. Meanwhile, the constraints restrict all feasible arm configurations to an elbow-circle and reduce the arm configuration space to a two-dimension space. By combining the configuration space and collision distribution of arm motion, collision-free arm configurations can be identified and be used to generate collision-free arm motion, which can avoid unnecessary forward and inverse kinematics. The experiments show that the proposed method can generate continuous and collision-free arm motion for preplanned hand trajectories.     

A Fast Dynamically Equilibrated Walking Trajectory Generation Method of Humanoid Robot

This paper describes a fast dynamically equilibrated trajectory generation method for a humanoid robot. From a given input motion and the desired ZMP trajectory, the algorithm generates a dynamically equilibrated trajectory using the relationship between the robot's center of gravity and the ZMP. Three key issues are denoted: 1) an enhanced ZMP constraint which enables the calculation of robot stability even if several limbs are contacting the environment, 2) a simplified robot model is introduced that represents the relationship between its center of gravity and ZMP, 3) a convergence method is adopted to eliminate approximation errors arising from the simplified model. Combining these three key issues together with online ZMP compensation method, humanoid robot H5 have succeeded to walk, step down and so on. Experimental results using humanoid robot H5 are described.     

基于实时ZMP检测的类人足球机器人步态规划

为了保证类人机器人行走的稳定性,合理的步态规划和误差补偿是最为关键的两个方面。针对研究新一代的类人足球机器人AFU2008,在步态规划方面,根据ZMP(零力矩点)稳定性原理,首先用参考轨迹法进行关节轨迹规划,然后由运动学逆解出的关节转角值对机器人舵机进行实际控制;在误差补偿方面,采用对ZMP影响较大的上体运动进行误差补偿,并针对传统的上体补偿方法的局限性,提出了允许上体高度作匀速运动的改进方法。最后通过仿真和实际实验表明:相对于传统补偿方法,新方法能够更加明显减小机器人的ZMP误差,提高机器人ZMP的稳定裕度,使得类人机器人可以稳定快速的行走。     

仿人机器人复杂动作设计中人体运动数据提取及分析方法

提出了仿人机器人复杂动作设计中人体运动数据提取及分析方法. 首先, 通过运动捕捉系统获取人体运动数据, 并采用运动重定向技术, 输出人--机简化模型的数据; 然后, 对运动数据进行分析和运动学解算, 给出基于人体运动数据的仿人机器人逆运动学求解方法, 得到仿人机器人模型的关节角数据; 再经过运动学约束和稳定性调节后, 生成能够应用于仿人机器人的运动轨迹. 最终, 通过在仿人机器人BHR-2上进行刀术实验验证了该方法的有效性.     

A walking pattern generation method of humanoid robot MAHRU-R

This paper proposes an omni-directional walking pattern generation method for a humanoid robot MAHRU-R. To walk stably without falling down, a humanoid robot needs the walking pattern. Our previous walking pattern method generated the walking pattern with linear polynomials of the zero moment point (ZMP). It implemented the simple walking like forward/backward walking, side step walking and turning. However, this method was not sufficient to satisfy the various walking which is combined by forward/backward walking, side step walking and turning. We needed to upgrade the walking pattern generation method to implement an omni-directional walking. We use the linear inverted pendulum model consisted of ZMP and center of mass in order to simplify the computation of walking pattern. The proposed method assumes that the state of the following stride is same to the state of the current stride. Using this assumption of walking pattern, the proposed method generates the stable walking pattern for various walking. And the proposed scheme generates the ZMP trajectory with the quartic polynomials in order to reduce the fluctuation of ZMP trajectory by various walking. To implement the efficient walking pattern, this method proposes three walking modules: periodic step module, transient step module and steady step module. Each step module utilizes weighted least square method with future ZMP position information. The effectiveness of the proposed method is verified by simulations of various walking. And the proposed method is confirmed by the experiment of real humanoid robot MAHRU-R.     

Landing Force Control for Humanoid Robot by Time-Domain Passivity Approach

This paper proposes a control method to absorb the landing force or the ground reaction force for a stable dynamic walking of a humanoid robot. Humanoid robot may become unstable during walking due to the impulsive contact force of the sudden landing of its foot. Therefore, a control method to decrease the landing force is required. In this paper, time-domain passivity control approach is applied for this purpose. Ground and the foot of the robot are modeled as two one-port network systems that are connected, and exchange energy with each other. The time-domain passivity controller with admittance causality is implemented, which has the landing force as input and foot's position to trim off the force as output. The proposed landing force controller can enhance the stability of the walking robot from simple computation. The small-sized humanoid robot, HanSaRam-VII that has 27 DOFs, is developed to verify the proposed scheme through dynamic walking experiments.     

基于最优控制的仿人机器人行走振动抑制

针对仿人机器人行走过程中由腿部非刚性特性引起的振动,提出了一种基于最优控制的行走振动抑制方法．首先对振动进行建模,并将这一模型加入到原有机器人动力学模型中去．然后基于拓展的动力学模型,使用预观控制方法求取一条符合质心加速度约束的控制轨迹,作为求解最优控制问题的初始解．进而由此初始解出发,迭代求解此带约束的最优控制问题,利用得到的最优控制轨迹即能以前馈方式抑制行走过程中的振动．最后,使用预观控制方法和本文方法在仿人机器人“空”上进行行走对比实验．实验结果表明提出的方法显著减小了机器人行走过程中零力矩点（ZMP）的振荡和躯干的晃动．该方法对行走振动抑制的有效性得到了验证．     

Incremental kinesthetic teaching of motion primitives using the motion refinement tube

We present an approach for kinesthetic teaching of motion primitives for a humanoid robot. The proposed teaching method starts with observational learning and applies iterative kinesthetic motion refinement using a forgetting factor. Kinesthetic teaching is supported by introducing the motion refinement tube, which represents an area of allowed motion refinement around the nominal trajectory. On the realtime control level, the kinesthetic teaching is handled by a customized impedance controller, which combines tracking performance with compliant physical interaction and allows to implement soft boundaries for the motion refinement. A novel method for continuous generation of motions from a hidden Markov model (HMM) representation of motion primitives is proposed, which incorporates time information for each state. The proposed methods were implemented and tested using DLR??s humanoid upper-body robot Justin.     

双足机器人动态步态规划

针对目前仿人机器人动态步行在样机上实现较少的情况,将多项式插值方法运用于机器人踝关节轨迹规划,结合已知髋关节运动轨迹,利用几何约束的方法求取膝关节运动轨迹,得到完整步态周期内各关节运动规律,最终实现NAO机器人的动态步行。实验结果证实了基于多项式插值的几何约束规划方法是可行且有效的。     

仿人机器人轻型高刚性手臂设计及运动学分析

重点研究了7自由度轻型高刚性作业型仿人机器人手臂的机构设计和运动学分析方法.首先使用动力学仿真、有限元分析与实验测试相结合的方法,设计了仿人机器人手臂,该机械臂结构紧凑、质量轻、刚度高.同时,提出结合查询数据库和逆运动学计算去模仿人类手臂姿态,从而获得逆运动学最优解的方法.该方法不仅解决了冗余自由度带来的逆运动学多解问...     

Dynamically-Stable Motion Planning for Humanoid Robots

We present an approach to path planning for humanoid robots that computes dynamically-stable, collision-free trajectories from full-body posture goals. Given a geometric model of the environment and a statically-stable desired posture, we search the configuration space of the robot for a collision-free path that simultaneously satisfies dynamic balance constraints. We adapt existing randomized path planning techniques by imposing balance constraints on incremental search motions in order to maintain the overall dynamic stability of the final path. A dynamics filtering function that constrains the ZMP (zero moment point) trajectory is used as a post-processing step to transform statically-stable, collision-free paths into dynamically-stable, collision-free trajectories for the entire body. Although we have focused our experiments on biped robots with a humanoid shape, the method generally applies to any robot subject to balance constraints (legged or not). The algorithm is presented along with computed examples using both simulated and real humanoid robots.     

基于零力矩点预观控制的在线步态规划

提出了一种基于零力矩点(ZMP)预观控制的仿人机器人在线步态规划方法。参照仿人机器人的ZMP表达式,将水平角动量引入机器人的桌子-小车模型表达式,对其进行了扩展;基于扩展桌子-小车模型并结合预观控制理论,在线完成机器人的步态规划。为了提高机器人步行的稳定性,将髋关节轨迹的优化问题转化为质心轨迹的调节问题,构造预观控制器对机器人的质心轨迹进行校正,实现机器人的步态优化。仿真结果表明了所用方法的有效性。     

表征空间中的机器人分层运动规划

由于对机器人的任务要求日趋复杂和多变,如何使机器人具备灵活的配置和运动规划能力,以适应复杂任务的需求,成为了目前运动规划领域所研究的核心问题.传统的基于任务空间和配置空间的建模方法虽然在机器人运动规划领域得到了非常广泛的应用,但在用于解决复杂规划任务时无法对不可行任务进行进一步地处理.本文在表征空间模型的基础上,提出了一种分层的运动规划算法,一方面借助于表征空间维度的扩展,使对运动规划任务的描述更为灵活;另一方面通过任务层与运动层的循环交互,使生成的路径满足更高层次和更丰富的任务要求.在仿人机器人和多机器人系统上的应用结果表明了本文所提算法的有效性.     

欠驱动冗余度空间机器人优化控制

欠驱动控制是空间技术中容错技术的重要方面.本文研究了被动关节中有制动器的欠驱动冗余度空间机器人系统的运动优化控制问题.从系统动力学方程出发,分析了欠驱动冗余度空间机器人的优化能力和控制方法;给出了主、被动关节间的耦合度指标;提出了欠驱动冗余度空间机器人系统的“虚拟模型引导控制”方法,在这种方法中采用与欠驱动机器人机构等价的全驱动机器人作为模型来规划机器人的运动,使欠驱动系统在关节空间中逼近给出的规划轨迹,实现了机器人末端运动的连续轨迹运动优化控制;通过末关节为被动关节的平面三连杆机器人进行了仿真,仿真的结果证明了提出算法的有效性.