Chao Jiang

Zhuo Chen, Chao Jiang, Yi Guo

The study of human-robot interaction (HRI) has received increasing research attention for robot navigation in pedestrian crowds. In this paper, we present empirical study of pedestrian-robot interaction in an uni-directional exit corridor. We deploy a mobile robot moving in a direction perpendicular to that of the pedestrian flow, and install a pedestrian motion tracking system to record the collective motion. We analyze both individual and collective motion of pedestrians, and measure the effect of the robot motion on the overall pedestrian flow. The experimental results show the effect of passive HRI, where the pedestrians' overall speed is slowed down in the presence of the robot, and the faster the robot moves, the lower the average pedestrian velocity becomes. Experiment results show qualitative consistency of the collective HRI effect with simulation results that was previously reported. The study can be used to guide future design of robot-assisted pedestrian evacuation algorithms.

Bo Tang, Chao Jiang, Haibo He et al.

Understanding human mobility is important for the development of intelligent mobile service robots as it can provide prior knowledge and predictions of human distribution for robot-assisted activities. In this paper, we propose a probabilistic method to model human motion behaviors which is determined by both internal and external factors in an indoor environment. While the internal factors are represented by the individual preferences, aims and interests, the external factors are indicated by the stimulation of the environment. We model the randomness of human macro-level movement, e.g., the probability of visiting a specific place and staying time, under the Bayesian framework, considering the influence of both internal and external variables. We use two case studies in a shopping mall and in a college student dorm building to show the effectiveness of our proposed probabilistic human mobility model. Real surveillance camera data are used to validate the proposed model together with survey data in the case study of student dorm.