Stability and control of dynamic walking for a five-link planar biped robot with feet

During dynamic walking of biped robots, the underactuated rotating degree of freedom (DOF) emerges between the support foot and the ground, which makes the biped model hybrid and dimension-variant.This paper addresses the asymptotic orbit stability for criterion for DVHS is also presented, and the result is then used to study dynamic walking for a five-link planar biped robot with feet. Time-invariant gait planning and nonlinear control strategy for dynamic walking with flat feet is also introduced. Simulation results indicate that an asymptotically stable limit cycle of dynamic walking is achieved by the proposed method.     

Stability and control of dynamic walking for a five-link planar biped robot with feet

During dynamic walking of biped robots, the underactuated rotating degree of freedom （DOF） emerges between the support foot and the ground, which makes the biped model hybrid and dimension-variant. This paper addresses the asymptotic orbit stability for dimension-variant hybrid systems （DVHS）. Based on the generalized Poincare map, the stability criterion for DVHS is also presented, and the result is then used to study dynamic walking for a five-link planar biped robot with feet. Time-invariant gait planning and nonlinear control strategy for dynamic walking with fiat feet is also introduced. Simulation results indicate that an asymptotically stable limit cycle of dynamic walking is achieved by the proposed method.     

面向离散地形的欠驱动双足机器人平衡控制方法

欠驱动双足机器人在行走中为保持自身的平衡, 双脚需要不间断运动. 但在仅有特定立足点的离散地形上很难实现调整后的落脚点, 从而导致欠驱动双足机器人在复杂环境中的适应能力下降. 提出了基于虚拟约束(Virtual constraint, VC)的变步长调节与控制方法, 根据欠驱动双足机器人当前状态与参考落脚点设计了非时变尺度缩放因子, 能够实时重构适应当前环境的步态轨迹; 同时构建了全身动力学模型, 采用反馈线性化的模型预测控制 (Model predictive control, MPC) 滚动优化产生力矩控制量, 实现准确的轨迹跟踪控制. 最终进行了欠驱动双足机器人的随机离散地形稳定行走的仿真实验, 验证了所提方法的有效性与鲁棒性.     

Reflex control of bipedal locomotion on a slippery surface

Biped robots are expected to walk on many different and previously unknown terrains including slippery surfaces on which no prior information is available. It is very important that biped robots have an ability to walk on a slippery surface which it meets so suddenly, since any damage to biped robots will be very costly. In order to prevent falling down on a suddenly encountered slippery surface, this paper proposes a reflex control method for biped robots to quickly recover their posture from a foot slip upon its detection. Computer simulations were performed with a 12-d.o.f. biped robot model and a 6-d.o.f. elastic pad model, the latter of which consists of nonlinear dampers, and linear and nonlinear springs. Simulation results show that the proposed method is very effective in preventing biped robots falling down when walking on a slippery surface.     

Overview of the Lucy Project: Dynamic Stabilization of a Biped Powered by Pneumatic Artificial Muscles

This paper gives an overview of the Lucy project. What is special is that the biped is not actuated with the classical electrical drives, but with pleated pneumatic artificial muscles. In an antagonistic setup of such muscles both the torque and the compliance are controllable. From human walking there is evidence that joint compliance plays an important role in energy-efficient walking and running. To be able to walk at different walking speeds and step lengths, a trajectory generator and joint trajectory tracking controller are combined. The first generates dynamically stable trajectories based on the objective locomotion parameters which can be changed from step to step. The joint trajectory tracking unit controls the pressure inside the muscles so the desired motion is followed. It is based on a computed torque model and takes the torque–angle relation of the antagonistic muscle setup into account. With this strategy the robot is able to walk at a speed up to 0.15 m/s. A compliance controller is developed to reduce the energy consumption by combining active trajectory control with the exploitation of the natural dynamics. A mathematical formulation was developed to find an optimal compliance setting depending on the desired trajectory and physical properties of the system. This strategy is experimentally evaluated on a single pendulum structure and not implemented on the real robot because the walking speed of the robot is currently too slow. At the end a discussion is given about the pros and cons of building a pneumatic biped, and the control architecture used.     

双足机器人动态步行实时步态生成

针对双足机器人动态步行生成关节运动轨迹复杂问题,提出了一种简单直观的实时步态生成方案。建立了平面五杆双足机器人动力学模型,通过模仿人类步行主要运动特征并根据双足机器人动态步行双腿姿态变化的要求,将动态步行复杂任务分解为顺序执行的四个过程,在关节空间相对坐标系下设计了躯干运动模式、摆动腿和支撑腿动作及步行速度调整模式,结合当前步行控制结果反馈实时产生稳定的关节运动轨迹。仿真实验验证了该方法的有效性,简单易实现。     

基于GCIPM行走的步行机器人路径规划

介绍了利用重力补偿倒立摆方式（GCIPM）提高步行机器人行走的稳定性。该方法与以往利用线性倒立摆方式（IPM）控制的机器人相似，但是考虑了期望轨迹上机器人的迈步腿力。当基于IPM的路径规划应用到实际的步行机器人上，依据ZMP控制理论从预固定点移动时，被忽略的迈步腿力的变化在实际中使稳定性得不到保证。由于GCIPM考虑了迈步腿力的影响，仿真表明，应用GCIPM的步行机器人，稳定性得到优化提高。     

基于deep Q-network双足机器人非平整地面行走稳定性控制方法

针对双足机器人在非平整地面行走时容易失去运动稳定性的问题,提出一种基于一种基于价值的深度强化学习算法DQN（Deep Q-Network）的步态控制方法。首先通过机器人步态规划得到针对平整地面环境的离线步态,然后将双足机器人视为一个智能体,建立机器人环境空间、状态空间、动作空间及奖惩机制,该过程与传统控制方法相比无需复杂的动力学建模过程,最后经过多回合训练使双足机器人学会在不平整地面进行姿态调整,保证行走稳定性。在V-Rep仿真环境中进行了算法验证,双足机器人在非平整地面行走过程中,通过DQN步态调整学习算法,姿态角度波动范围在3°以内,结果表明双足机器人行走稳定性得到明显改善,实现了机器人的姿态调整行为学习,证明了该方法的有效性。     

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.     

一种小样本支持向量机控制器在两足机器人步态控制的研究

神经网络等传统的机器学习方法是基于样本数目无穷大的经验风险最小化原则,这对非确定环境下有限样本的步态学习控制非常不利．针对两足机器人面临的非确定环境适应性难题,提出了一种基于支持向量机（SVM）的两足机器人步态控制方法,解决了小样本条件下的步态学习控制问题．提出了一种基于混合核的步态回归方法,仿真研究表明了这种方法比全局核和局部核分别单独用于步态学习时有优越性．SVM以踝关节及髋关节的轨迹作为输入,相应的满足ZMP判据的上体轨迹作为输出,利用有限的理想步态样本对机器人上体轨迹与腿部轨迹之间的动态运动关系进行学习,然后将训练好的SVM置入机器人控制系统,从而增强了步态控制的鲁棒性,有利于实现两足机器人在非结构环境下的稳定步行．仿真结果表明了所提方法的优越性．