一种用于沿规定路径运动的机器人的新的类人性与舒适度指数
A New Human-Likeness and Comfort Index for Robot Movements Along Prescribed Paths
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中文总结 AI 辅助
探讨人机交互中机器人运动舒适度与类人性联系，基于人类运动建模定义类人性指数，用于评估轨迹生成算法，通过68名受试者参与的三次实验验证，发现舒适度偏好与类人性指数分布有一致趋势。
中文摘要 AI 辅助
随着人机交互在众多领域迅速普及，机器人的可接受性问题愈发重要。类人机器人在视觉上与人体相似，但在物理交互中，这通常不足以确保被接受，因为接受度直接与舒适度和人体工程学相关，而这是通过人类感知的机器人运动质量来衡量的。本文探讨了机器人运动的舒适度与类人性之间的联系。通过考虑人类运动的运动学特征，聚焦于末端执行器路径规定时的运动时间规律。基于人类运动建模的对数正态性原理，定义了一个类人性指数，并用于对轨迹进行先验表征。该指数可用于在轨迹生成算法实际执行之前评估其产生类人运动的性能。为进行验证，要求68名受试者判断他们的舒适度。涉及与机器人进行物理交互的三次实验活动结果表明，在感知舒适度方面的偏好与所建议的类人性指数分布之间存在全球一致的趋势。
英文摘要
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.
发表机构
- Department of Computer Engineering, Electrical Engineering and Applied Mathematics (DIEM), University of Salerno（萨勒诺大学计算机工程、电气工程与应用数学系）
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