From HRI to CRI: Crowd Robot Interaction—Understanding the Effect of Robots on Crowd Motion

Abstract

How does the presence of a robot affect pedestrians and crowd dynamics, and does this influence vary across robot type? In this paper, we took the first step towards answering this question by performing a crowd-robot gate-crossing experiment. The study involved 28 participants and two distinct robot representatives: A smart wheelchair and a Pepper humanoid robot. Collected data includes: video recordings; robot and participant trajectories; and participants’ responses to post-interaction questionnaires. Quantitative analysis on the trajectories suggests the robot affects crowd dynamics in terms of trajectory regularity and interaction complexity. Qualitative results indicate that pedestrians tend to be more conservative and follow “social rules” while passing a wheelchair compared to a humanoid robot. These insights can be used to design a social navigation strategy that allows more natural interaction by considering the robot effect on the crowd dynamics.

A Human Error Analysis in Human–Robot Interaction Contexts: Evidence from an Empirical Study

“…This is in line with a part of the current literature, such as works by Xie et al [43] and Vassallo et al [44], who noticed no difference in HRI and non-HRI activities. Most of the literature argued that an influence is created by the presence of the robot in a working environment [40][41][42]. The difference between the results of the present study and the other contrasting studies can be explained by the monitored parameters in the papers.…”

“…Huber et al [40] demonstrated that robots influence the behavior of humans in simple tasks as well, such as the ones implying hand movements. Similar results were found by Zhang et al [41], whose empirical study demonstrated that robots affect humans in some actions, influencing the completion times and trajectories followed. The same results are confirmed by Chen et al [42], who observed that people changed behavior according to their anticipation of a potential collision with robots during pedestrian motion.…”

Application of Motion Effect Evaluation Algorithm Based on Random Forest

“…It can be seen from the above that the random forest algorithm has many improvement ways and a wide range of application fields, and it has its unique advantages compared with other algorithms in data classification and prediction. Therefore, random forest is a relatively mature and excellent machine learning algorithm in machine learning [ 6 ].…”

AssistMe: Policy iteration for the longitudinal control of a non-holonomic vehicle

“…-see the standardized circuits in [1]. Follow-up studies are necessary to address these situations, including avoiding dynamic obstacles like pedestrians walking [41].…”