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

TL;DR

This paper presents a comprehensive framework and a novel navigation system for mobile robots that minimizes disturbance to pedestrians in dense crowds by integrating macro and micro-level dynamics, validated through simulations and real-world tests.

Contribution

It introduces a new framework with penalty terms for analyzing disturbances and a sampling-based navigation system that enhances safety and pedestrian awareness.

Findings

Effective reduction of pedestrian disturbance in simulations

Real-time navigation performance demonstrated in tests

Enhanced safety and predictability in robot movements

Abstract

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.