arXiv:2605.26710cs.RO2026-05被引 4

让机器人提前8米主动变道,提升住户对导航行为的满意度。

Look Further: Socially-Compliant Navigation System in Residential Buildings

论文配图:Look Further: Socially-Compliant Navigation System in Residential Buildings
图 1 · 摘自论文原文
  • 机器人在8米外提前侧移变道,主动避让来人。
  • 用户实验显示三类体验(安全、顺滑、礼貌)均显著优于传统方法。
  • 适合住宅场景下注重人机交互体验的配送机器人设计。

移动机器人在与人交互时的反应距离直接影响人机交互质量。本文聚焦于住宅室内走廊环境中移动配送机器人的导航问题。社交导航通常关注避免侵犯个人空间等近距离不适互动,而个人空间范围仅为数米,因此现有方法多在短距离内进行避让。本文提出将反应距离扩展至8米以上,显著超出典型交互距离,从而改善人类对机器人运动的感知。为此,我们引入主动车道变换(Proactive Lane-Changing, PLC)运动模式,即机器人在距迎面而来的人8米处开始从走廊中心向一侧偏移。通过42名参与者组成的用户研究,评估机器人在安全、顺滑、礼貌三个服务目标上的表现。在直线走廊场景(正面接近)中,PLC模式在三项指标上均显著优于文献中常见的减速、停止及临近时的回避策略;而在交叉口(盲角)场景中,各运动模式无显著差异,参与者偏好多样。

原文摘要 · Abstract (English)

The distance at which a mobile robot reacts to a person strongly impacts various qualities of the human-robot interaction. In this paper, we focus on the navigation of a mobile delivery robot platform in a residential indoor hallway environment. Social navigation methods typically focus on avoiding uncomfortable human-robot interactions, such as when a robot encroaches on someone's personal space. Since personal space has been shown to be in the range of just a few meters, social navigation methods typically focus on deconflicting and resolving these short-range interactions. In this work, however, we demonstrate that by extending the reaction distance to over eight meters, far beyond the typical interaction distance, we can improve the human's perception of the robot's motion. We introduce the Proactive Lane-Changing (PLC) motion pattern and a navigation system that leverages it to react to people at an increased distance. This pattern consists of changing the robot's lateral position as it navigates down the hallway from the center to the side at an eight-meter distance from an oncoming person. We conducted a user study with 42 participants to assess their impressions of the delivery robot based on three service objectives: safety, smoothness, and politeness. In the straight hallway scenario (Frontal Approach), results showed significant improvement in each of these three objectives compared to typical motion patterns found in the literature: slowing down, stopping, and reactive collision avoidance in the proximity of a person. In contrast, in the intersection (Blind Corner) scenarios, none of the approaches performed significantly better than any other, with participants having a diverse range of preferences among robot motion patterns.

社交导航人机交互机器人路径规划

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