Social Navigation Planning Based on People's Awareness of Robots

TL;DR

This paper presents a novel robot navigation method that predicts human awareness to generate socially acceptable paths, using sensor fusion and POMDP modeling to adapt to dynamic environments.

Contribution

It introduces a new approach that models human awareness within a POMDP framework for real-time social navigation planning.

Findings

The framework effectively predicts human awareness and adjusts robot paths accordingly.

The method operates in real-time during experiments with the Toyota HSR.

Simulation results show improved social acceptability of robot navigation.

Abstract

When mobile robots maneuver near people, they run the risk of rudely blocking their paths; but not all people behave the same around robots. People that have not noticed the robot are the most difficult to predict. This paper investigates how mobile robots can generate acceptable paths in dynamic environments by predicting human behavior. Here, human behavior may include both physical and mental behavior, we focus on the latter. We introduce a simple safe interaction model: when a human seems unaware of the robot, it should avoid going too close. In this study, people around robots are detected and tracked using sensor fusion and filtering techniques. To handle uncertainties in the dynamic environment, a Partially-Observable Markov Decision Process Model (POMDP) is used to formulate a navigation planning problem in the shared environment. People's awareness of robots is inferred and included as a state and reward model in the POMDP. The proposed planner enables a robot to change its navigation plan based on its perception of each person's robot-awareness. As far as we can tell, this is a new capability. We conduct simulation and experiments using the Toyota Human Support Robot (HSR) to validate our approach. We demonstrate that the proposed framework is capable of running in real-time.