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
研究针对非完整约束移动机器人，利用社会力模型等设计算法，通过仿真校准、实验比较及统计分析，证明算法稳定性，突出其在提高行人舒适性方面的优势。
AI 中文摘要
在公共场所的移动机器人必须确保行人的舒适性，然而对行人主观安全性的实证研究却很少。许多经典导航算法无法区分行人和动态障碍物，也未明确建模主观人为因素。此外，多数研究聚焦于完整约束移动机器人，而实际应用需要非完整约束移动机器人（NMR）。本文为NMR开发了具有社会意识的算法，证明了稳定性，通过实验验证了性能，并对报告的舒适性进行了统计分析。设计了使用社会力模型（SFM）和预测碰撞时间社会力模型（TSFM）的框架，形式化了NMR与行人、NMR与障碍物的交互并证明系统稳定性。通过最大化舒适性和速度的混合成本函数对模型进行仿真校准。行人-机器人交互实验将SFM和TSFM与两个遥控基线进行比较并收集行人报告的舒适性。统计工具分析实验中收集的调查结果。与先前研究的算法进行基准测试突出了所提方法在研究指标方面的优势。总体而言，这些模型是稳定的，并且在NMR在行人人群中导航时提高了行人的舒适性。
英文摘要
Mobile robots in public spaces must ensure pedestrians' comfort, and yet empirical studies of walkers' subjective safety are rare. Many classical navigation algorithms do not distinguish the walkers from dynamic obstacles and do not explicitly model subjective human factors. Moreover, most studies focus on holonomic mobile robots, whereas applications demand Nonholonomic Mobile Robots (NMR). This paper develops socially aware algorithms for NMRs, proves the stability, verifies the performance experimentally, and statistically analyzes the reported comfort. We design a framework for NMRs using Social Force Model (SFM) and the projected Time-to-collision Social Force Model (TSFM). We formalize the NMR-pedestrians' and NMR-obstacles' interactions and prove the system's stability, assuming boundedly nonpassive pedestrians. Simulations calibrate the models by maximizing a hybrid cost function of comfort and speed. Pedestrian-robot interaction experiments compare SFM and TSFM to two remote-controlled baselines and collect walkers' reported comfort. Statistical tools analyze survey results collected during the experiments. Benchmarking the algorithms against previous studies highlights the proposed methods' advantage with respect to the studied metrics. Overall, the models are stable and improve pedestrian comfort when an NMR navigates through a pedestrian crowd.
Comments21 pages, 9 figures, 12 tables, submitted for publication
