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 movementsaccurately, a novel human-like motion planning method is proposed, which combines the triggerconditions and hierarchical planning strategy (HPS). The method decomposes the complete armmovements into a set of different motion processes, each of which has corresponding planninghierarchies. The anthropomorphic mechanical arms reveal different characteristics in differentplanning hierarchies. The motion models and posture prediction indicators in varying planninghierarchies are built based on the respective characteristics to predict the postures of anthropomorphicmechanical arms. The experiment is acted on humanoid robot NAO as the platform, and then theprediction results of static and dynamic arm postures is performed by the proposed method and theminimum 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). Theexperimental results show that the errors of static and dynamic posture prediction of proposed methodcould be reduced, and the anthropomorphic mechanical arms can generate the human-like movementsaccurately through the proposed method.