How to generate anthropomorphic reaching movement remains a challenging problem in service robots and human motor function repair/reconstruction equipment. However, there is no universally accepted computational model in the literature for reproducing the motion of the human upper limb. In response to the problem, this article presents a computational framework for generating reaching movement endowed with human motion characteristics that imitated the mechanism in the control and realization of human upper limb motions. This article first establishes the experimental paradigm of human upper limb functional movements and proposes the characterization of human upper limb movement characteristics and feature movement clustering methods in the joint space. Then, according to the specific task requirements of the upper limb, combined with the human sensorimotor model, the estimation method of the human upper limb natural postures was established. Next, a continuous task parametric model matching the characteristic motion class is established by using the Gaussian mixture regression method. The anthropomorphic motion generation method with the characteristics of the smooth trajectory and the ability of natural obstacle avoidance is proposed. Finally, the anthropomorphic motion generation method proposed in this article is verified by a human-like robot. The measurement index of the human-likeness degree of the trajectory is given. The experimental results show that for all four tested tasks, the human-likeness degrees were greater than 90.8%, and the trajectories’ jerk generated by this method is very similar to the trajectories’ jerk of humans, which validates the proposed method.

中文翻译：

---

类人上肢机器人拟人伸展运动生成方法


如何产生拟人化的到达运动仍然是服务机器人和人类运动功能修复/重建设备中的一个具有挑战性的问题。然而，文献中没有普遍接受的计算模型来再现人类上肢的运动。针对这一问题，本文提出了一种模仿人类上肢运动控制和实现机制的生成具有人体运动特征的伸手运动的计算框架。本文首先建立了人体上肢功能运动的实验范式，提出了表征人体上肢运动特征和关节空间中的特征运动聚类方法。然后，根据上肢的具体任务要求，结合人体感觉运动模型，建立了人体上肢自然姿势的估计方法。接下来，利用高斯混合回归方法建立与特征运动类匹配的连续任务参数模型。提出了一种具有平滑轨迹和自然避障能力的拟人运动生成方法。最后，通过类人机器人对本文提出的拟人运动生成方法进行了验证。给出了轨迹拟人程度的衡量指标。实验结果表明，对于所有四个测试任务，人类相似度均大于90.8%，并且该方法生成的轨迹的急动度与人类的轨迹急动度非常相似，这验证了所提出的方法。