提出机器人宜居度评分，量化城市街道适合机器人通行的程度。
The Robotability Score: Enabling Harmonious Robot Navigation on Urban Streets
- 基于专家调研构建多维度评分体系，聚焦人流密度与动态
- 纽约城区机器人宜居度差异达3倍，高分区通行更顺畅
- 适用于城市规划与机器人部署前评估，助力人机共处
本文提出机器人宜居度评分（R），一种量化城市环境适配自主机器人导航能力的新指标。通过专家访谈与问卷调查，识别并加权影响轮式机器人在城市街道通行的关键特征。研究发现，行人密度、人群动态及行进流是决定因素，合计占总评分的28%。在纽约市范围计算机器人宜居度显示显著差异：最高分区域比最低分区域高出3.0倍。在高低宜居度区域部署实体机器人进行测试，验证了该评分对导航难易程度的预测能力。该评估框架旨在降低机器人部署的不确定性，同时尊重既有交通模式与城市规划原则，推动和谐人机共存环境的讨论。
原文摘要 · Abstract (English)
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.
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