基于虚拟黏弹性肌肉模型的仿人机器人不平整地面稳定行走

Virtual Viscoelastic Muscle Model Based Stable Walking on Uneven Terrain for Humanoid Robots

复杂未知环境下,仿人机器人在行走过程中难以获取精确的地面信息,导致规划的落脚点与实际落脚位置之间存在误差,这会对机器人的平衡造成严重的干扰.针对该问题,提出了模拟人体肌肉黏弹性的虚拟肌肉模型,并基于该模型设计了仿人机器人在不平整地面上的稳定行走控制方法.首先,从仿生角度出发,扩展传统肌肉模型,构建了具有伸缩功能的虚拟肌肉模型,并对其黏弹性进行了分析.然后,基于该模型,采用LQR(线性二次型调节器)方法设计了虚拟肌肉伸缩长度与伸缩力的控制方法.最后,基于足部力传感器信息反馈,将该模型应用于仿人机器人行走过程中抬脚高度的调节,使仿人机器人能够适应未知复杂环境中地面高度的突变,或在机器人传感器系统获取的地面信息与实际情况相差很大的情况下实现稳定行走.结果表明,该算法可以使仿人机器人在高度差为6 cm以内的不平整地面上实现1.8 km/h的稳定行走.基于BHR-6P平台的行走仿真验证了该算法的有效性.

In complex unknown environments, it is difficult for humanoid robots to obtain exact terrain information during walking, which leads to the errors between the planned footholds and the actual foot landing positions. It brings great disturbance to the balance of robots. To address this problem, a virtual muscle model imitating the viscoelasticity of human muscle is proposed, and a stable walking control method for humanoid robots on uneven terrain is designed. Firstly, from the perspective of bionics, a virtual muscle model with ability of extension and shrink is established by extending the traditional muscle model, and its viscoelasticity is analyzed. Then, a muscle length and force controller is designed with LQR(linear quadratic regulator) method based on this model. Finally, combining the feedback information of force sensors on the feet,the proposed model is applied to adjusting the height of feet during walking for humanoid robots, which guarantees robots to adapt to the abrupt change of terrain height in complex unknown environments, or to accomplish stable walking when the terrain information obtained by the sensor system seriously differs from the actual condition. The result shows that the stable walking of humanoid robots can be realized on an uneven terrain within height errors of 6 cm and at speed of 1.8 km/h via the proposed method. Its effectiveness is validated by walking simulation on BH6-6P platform.