提出机器人轨迹人感度与舒适度新指标，提升人机交互体验
A New Human-Likeness and Comfort Index for Robot Movements Along Prescribed Paths
- 基于人类运动的对数正态规律建模轨迹时间规律
- 68人实验验证指标与主观舒适度高度相关
- 可用于提前评估轨迹生成算法的人性化程度
随着人机交互在各领域的普及，机器人接受度日益重要。仅外观像人不足以保证物理交互中的接受度，接受度直接关联舒适性与人体工学，体现在人类对机器人运动质量的感知上。本文探讨了运动舒适性与人类运动相似性之间的关系。通过分析人类运动的运动学特征，聚焦于末端执行器路径已知时的运动时间规律。基于人类运动的对数正态性原理，定义了一个人感度指数，用于在轨迹执行前进行人形运动的先验表征。该指数可评估轨迹生成算法产生类人运动的能力。为验证，68名受试者参与了三次涉及与机器人物理交互的实验，结果表明感知舒适度偏好与所提人感度指数分布呈全局一致趋势。
原文摘要 · Abstract (English)
As human-robot interaction rapidly spreads in numerous fields, the subject of robot acceptance gains increasing importance. Visual similarity to the human body, as occurs for humanoids, is generally not enough to ensure acceptance in physical interaction, as acceptance directly links to comfort and ergonomics, which are measured in terms of the quality of the robot movement perceived by the human. This paper discusses the connection between comfort and similarity of the robot movement to the human one. By considering the kinematic characterization of human movement, this paper focuses on the time laws of such movements, wherein the end-effector path is prescribed. Based on the lognormality principle for modeling human movements, a human-likeness index is defined and used to provide an a priori characterization of trajectories. Such an index can be used to evaluate the performance of trajectory generation algorithms in producing human-like movements before they are actually executed. For validation purposes, 68 subjects are required to judge their comfort. The results of three experimental campaigns involving a physical interaction with a robot demonstrate a globally consistent trend between the preference in terms of perceived comfort and the distribution of the suggested human-likeness index.
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