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In mobile robot navigation, despite advancements, the generation of optimalpaths often disrupts pedestrian areas. To tackle this, we propose three keycontributions to improve human-robot coexistence in shared spaces. Firstly, wehave established a comprehensive framework to understand disturbances atindividual and flow levels. Our framework provides specialized computationalstrategies for in-depth studies of human-robot interactions from both micro andmacro perspectives. By employing novel penalty terms, namely Flow DisturbancePenalty (FDP) and Individual Disturbance Penalty (IDP), our frameworkfacilitates a more nuanced assessment and analysis of the robot navigation'simpact on pedestrians. Secondly, we introduce an innovative sampling-basednavigation system that adeptly integrates a suite of safety measures with thepredictability of robotic movements. This system not only accounts fortraditional factors such as trajectory length and travel time but also activelyincorporates pedestrian awareness. Our navigation system aims to minimizedisturbances and promote harmonious coexistence by considering safetyprotocols, trajectory clarity, and pedestrian engagement. Lastly, we validateour algorithm's effectiveness and real-time performance through simulations andreal-world tests, demonstrating its ability to navigate with minimal pedestriandisturbance in various environments.

Minimally-intrusive Navigation in Dense Crowds with Integrated Macro and Micro-level Dynamics