基于分级规划策略的拟人机械臂仿人运动规划算法研究

为使拟人机械臂具有高精度的仿人运动，提出一种通过触发条件和分级规划策略 的仿人运动新方法。将人臂运动过程离散为不同运动阶段，在每一个运动阶段都有与之对应的 规划层，在不同的规划层中，拟人机械臂的运动特点不同。利用各自的特点建立不同规划层下 的运动模型及臂姿预测指标，对拟人机械臂臂姿进行预测。最后，以NAO 机器人为实验平台， 比较所提方法与最小势能法(MTPE)的静态臂姿与动态臂姿预测，并与运动捕捉系统(OptiTrack) 采集的真实人臂运动数据进行比较。实验表明，该方法具有较小的静态臂姿和动态臂姿预测误 差，能使拟人机械臂产生高度逼真的仿人运动。

On Human-Like Motion Planning Algorithm of Anthropomorphic Mechanical Arms Based on Hierarchical Planning Strategy

In order to make anthropomorphic mechanical arms generate human-like movements accurately, a novel human-like motion planning method is proposed, which combines the trigger conditions and hierarchical planning strategy (HPS). The method decomposes the complete arm movements into a set of different motion processes, each of which has corresponding planning hierarchies. The anthropomorphic mechanical arms reveal different characteristics in different planning hierarchies. The motion models and posture prediction indicators in varying planning hierarchies are built based on the respective characteristics to predict the postures of anthropomorphic mechanical arms. The experiment is acted on humanoid robot NAO as the platform, and then the prediction results of static and dynamic arm postures is performed by the proposed method and the minimum total potential energy (MTPE) are compared. In addition, the prediction results are compared with the real arm motion data collected by motion capture system (OptiTrack). The experimental results show that the errors of static and dynamic posture prediction of proposed method could be reduced, and the anthropomorphic mechanical arms can generate the human-like movements accurately through the proposed method.