发表机构
National Cheng Kung University（国立成功大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究移动机器人与行人共享人行道时的安全评估，通过整合预测社会力向量引入PSFM和PTSFM，在非完整移动机器人上实验，证实PTTC集成可提升安全指标，预测贡献有限，单行人场景中预测带来的额外好处不多。
AI 中文摘要
移动机器人将与行人共享人行道，需确保客观安全并尊重行人主观安全/舒适度。计算效率高的社会力模型（SFM）为动态人群中的实时机器人导航提供了可解释的解决方案。将预计碰撞时间（PTTC）集成到SFM变体中可改善安全指标，但预测变体的效果尚不清楚。本文通过在有限时间范围内整合预测社会力向量引入了预测性SFM（PSFM）和预测性TSFM（PTSFM）。在非完整移动机器人上实现了SFM、TSFM、PSFM和PTSFM，并与志愿者进行了面对面场景的实验。系统研究了各变体的客观和主观安全性。通过最小PTTC、平均速度、最小距离、横向距离和最大轨迹曲率来衡量客观安全，通过李克特量表后交互调查评估主观安全。结果证实PTTC集成改善了安全指标，预测贡献有限，在某些子指标中偶尔可见。一些参与者认为预测方法的运动更平滑、速度行为更安全，但曼-惠特尼检验显示主观评分无显著差异。因此，基于PTTC的导航提高了安全性，而在单行人场景中，制定的预测提供的额外好处有限。
英文摘要
Mobile robots are going to share the sidewalks with pedestrians. They must ensure their objective safety and respect the walkers' subjective safety/comfort. Computationally efficient Social Force Models (SFM) present interpretable solutions for real-time robot navigation in dynamic crowds. Recent explorations of Projected Time-to-collision (PTTC) integration into SFM variants, for example, PTTC-based SFM (TSFM), improve safety metrics. But the effect of predictive variants is unclear. We introduce Predictive SFM (PSFM) and Predictive TSFM (PTSFM) by integrating predicted social force vectors over a finite time horizon. The paper implements SFM, TSFM, PSFM, and PTSFM on a nonholonomic mobile robot and performs experimental trials with volunteers attending a facing scenario. We systematically study objective and subjective safety across the variants. Minimum PTTC, average speed, minimum distance, lateral distance, and the maximum trajectory curvature benchmark the objective safety. Likert scale post-interaction surveys assess subjective safety by marking comfort, smoothness, distance appropriateness, and speed suitability. We confirm that PTTC integration improves safety metrics. The prediction contribution is limited and occasionally visible in some of the sub-metrics. Some participants perceive smoother movements and safer speed behavior with predictive methods, but Mann-Whitney tests reveal no significant differences in subjective ratings. Therefore, PTTC-based navigation enhances safety, whereas the formulated prediction offers limited additional benefits in single-pedestrian scenarios.
Comments8 pages, 5 figures, 2 Tables, IEEE/ASME International Conference on Advanced Intelligent Mechatronics
