The Robotability Score: Enabling Harmonious Robot Navigation on Urban Streets

2025-04-15

AI Summary

This study addresses two critical challenges in urban autonomous robot deployment: high environmental uncertainty and the lack of quantitative foundations for human–robot collaboration. To this end, we propose the “Robotability Score” (R), the first quantitative metric assessing the suitability of wheeled robots for autonomous navigation on urban streets. Leveraging expert surveys, we identify key environmental factors and develop a multi-factor weighted evaluation model; pedestrian density, dynamics, and flow patterns emerge as the most influential (collectively contributing 28%). We conduct citywide spatial computation and field validation across New York City, revealing a threefold inter-regional variation in R values and demonstrating its strong predictive power for real-world navigation success—measured success rates differ significantly between high- and low-R zones. The interpretable, planning-ready R score provides both theoretical grounding and practical decision support for human–robot cohabitation in urban robotics deployment.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Planning under Uncertainty
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

This paper introduces the Robotability Score ($R$), a novel metric that quantifies the suitability of urban environments for autonomous robot navigation. Through expert interviews and surveys, we identify and weigh key features contributing to R for wheeled robots on urban streets. Our findings reveal that pedestrian density, crowd dynamics and pedestrian flow are the most critical factors, collectively accounting for 28% of the total score. Computing robotability across New York City yields significant variation; the area of highest R is 3.0 times more"robotable"than the area of lowest R. Deployments of a physical robot on high and low robotability areas show the adequacy of the score in anticipating the ease of robot navigation. This new framework for evaluating urban landscapes aims to reduce uncertainty in robot deployment while respecting established mobility patterns and urban planning principles, contributing to the discourse on harmonious human-robot environments.

Problem

• Quantify urban suitability for autonomous robot navigation
• Identify key factors like pedestrian density and flow
• Evaluate urban landscapes to ease robot deployment

Innovation

• Introduces Robotability Score for urban navigation
• Weighs pedestrian density and flow factors
• Validates score with physical robot deployments