基于动作基元的拟人机械臂仿人运动规划

拟人机械臂的仿人运动作为仿人机器人的运动基础,一直都是研究的热点及难点。针对拟人机械臂仿人运动的模型多样化及运动相似性特点,本文提出了一种基于动作基元的拟人臂仿人运动规划算法,对模型建立和逆运动学求解两方面展开研究。首先对手臂结构进行解耦,通过引入动作基元的概念,将动作基元作为连接手臂模型及拟人臂模型之间的桥梁,从而满足模型多样化的要求。提出动作基元的提取法则,确定不同动作基元之间的连接方式,建立动作基元与拟人臂反解算法之间的映射关系。同时提出了一种基于动作基元的运动框架,以满足不同拟人臂平台的仿人运动任务,实现方法的通用性。其次,针对多模型状态下的逆运动学求解问题,根据动作基元的运动特点,建立了不同动作基元模型下的拟人臂臂姿预测指标,实现拟人臂逆运动学的求解。最后,在仿人机器人NAO平台上分别进行了相似性实验验证和仿人运动实验验证。在相似性实验中,NAO采用该方法产生仿人运动并与实际人臂运动进行对比,运动过程中的肘部误差能够控制在1cm以内,满足仿人运动的精度要求。在仿人运动实验中,该方法分别与最小势能法和最小二乘法进行了一组仿人运动对比,在相似度上分别提高了7%和58%。该方法的提出在保证拟人臂仿人运动精度的基础上,使复杂的仿人运动模块化、简单化。

Human-like Motion Planning of Anthropomorphic Arm Based on Movement Primitive

As the basic part of motion of humanoid robots, human-like motion planning of the anthropomorphic arm was always one of the research hotspots and difficulties. In this paper, a novel human-like motion planning method based on movement primitives was proposed. This method can satisfy the feature of arm motion and improve the accuracy. Firstly, the arm structure was decoupled and the arm model was built to express different arm movements. The methods of extraction and connection about movement primitives were established. The mapping relations between the arm models and inverse kinematic (IK) solutions were established. Meanwhile, a motion framework was proposed. The joint trajectories of a certain platform can be generated to accomplish required tasks with this motion framework. Secondly, according to the motion features of different movement primitives, the associated Human Performance Measures for different movement primitives were constructed to solve the IK problem. Finally, the proposed method was verified by the similarity experiment and the human-like movement experiment for the general motion of humanoid robot NAO. In the similarity experiment, the robot NAO generated the human-like movements with the proposed method. The motion data were compared with the real data generated by humans. All the errors were less than 1cm, which satisfied the accuracy requirements of human-like movements. In the human-like movement experiment, the robot NAO performed a human-like arm movement with the proposed method. The proposed method was also compared with the Minimum Total Potential Energy Method and the last norm algorithm. Using the proposed method, 7 percent and 58 percent increase in similarity were achieved respectively, compared with those two methods. With the proposed method, the complex motion models were decoupled into different simple sub-movements and the classification of the movements reduced the calculation amount. The experiments proved that the anthropomorphic arm can generate human-like movements accurately.