In mobile robot navigation, despite advancements, the generation of optimal paths often disrupts pedestrian areas. To tackle this, we propose three key contributions to improve human-robot coexistence in shared spaces. Firstly, we have established a comprehensive framework to understand disturbances at individual and flow levels. Our framework provides specialized computational strategies for in-depth studies of human-robot interactions from both micro and macro perspectives. By employing novel penalty terms, namely Flow Disturbance Penalty (FDP) and Individual Disturbance Penalty (IDP), our framework facilitates a more nuanced assessment and analysis of the robot navigation's impact on pedestrians. Secondly, we introduce an innovative sampling-based navigation system that adeptly integrates a suite of safety measures with the predictability of robotic movements. This system not only accounts for traditional factors such as trajectory length and travel time but also actively incorporates pedestrian awareness. Our navigation system aims to minimize disturbances and promote harmonious coexistence by considering safety protocols, trajectory clarity, and pedestrian engagement. Lastly, we validate our algorithm's effectiveness and real-time performance through simulations and real-world tests, demonstrating its ability to navigate with minimal pedestrian disturbance in various environments.

翻译：在移动机器人导航领域，尽管技术不断进步，但最优路径的生成往往会干扰行人区域。为解决这一问题，我们提出三项关键贡献以改善共享空间中的人机共存。首先，我们建立了一个综合框架，从个体和流层面理解干扰。该框架通过微观与宏观双重视角，为深入探究人机交互提供了专门的计算机策略。通过引入新型惩罚项——流干扰惩罚（FDP）与个体干扰惩罚（IDP），该框架能够对机器人导航对行人的影响进行更精细的评估与分析。其次，我们提出了一种创新的基于采样的导航系统，该系统巧妙融合了多重安全措施与机器人运动的可预测性。该系统不仅考虑轨迹长度、行驶时间等传统因素，还主动纳入行人意识。通过整合安全协议、轨迹清晰度与行人参与度，我们的导航系统旨在最小化干扰并促进和谐共存。最后，我们通过仿真与实际环境测试验证了算法的有效性与实时性能，证明其能在多种环境中以最小程度干扰行人完成导航任务。