An Approach of Social Navigation Based on Proxemics for Crowded Environments of Humans and Robots

Abstract

Nowadays, mobile robots are playing an important role in different areas of science, industry, academia and even in everyday life. In this sense, their abilities and behaviours become increasingly complex. In particular, in indoor environments, such as hospitals, schools, banks and museums, where the robot coincides with people and other robots, its movement and navigation must be programmed and adapted to robot–robot and human–robot interactions. However, existing approaches are focused either on multi-robot navigation (robot–robot interaction) or social navigation with human presence (human–robot interaction), neglecting the integration of both approaches. Proxemic interaction is recently being used in this domain of research, to improve Human–Robot Interaction (HRI). In this context, we propose an autonomous navigation approach for mobile robots in indoor environments, based on the principles of proxemic theory, integrated with classical navigation algorithms, such as ORCA, Social Momentum, and A*. With this novel approach, the mobile robot adapts its behaviour, by analysing the proximity of people to each other, with respect to it, and with respect to other robots to decide and plan its respective navigation, while showing acceptable social behaviours in presence of humans. We describe our proposed approach and show how proxemics and the classical navigation algorithms are combined to provide an effective navigation, while respecting social human distances. To show the suitability of our approach, we simulate several situations of coexistence of robots and humans, demonstrating an effective social navigation.

Evaluation of Socially-Aware Robot Navigation

“…However, this type of evaluation makes strong assumptions about human and robot behaviors. For example, in simulation experiments, researchers typically rely on pedestrian behavior models such as Optimal Reciprocal Collision Avoidance (ORCA) ( Van Den Berg et al, 2011 ) (e.g., Chen et al, 2017b ; Daza et al, 2021 ) and the Social Force Model (SFM) ( Helbing and Molnár, 1995 ) (e.g., Katyal et al, 2021 ). Reciprocal behavior models such as ORCA impose the assumption that each agent is fully aware of its surroundings and the position and velocity of the other agents; this assumption of omniscience does not hold true for a real robot or person ( Fraichard and Levesy, 2020 ).…”

A New Approach for Including Social Conventions into Social Robots Navigation by Using Polygonal Triangulation and Group Asymmetric Gaussian Functions

“…Finally, the proposed approach was tested in path planning and navigation by using the approach proposed by Daza et al [22], which is a method based on A* and social momentum algorithms for avoiding the personal space of persons. It demonstrated the applicability of the presented approach for path planning and navigation with optimal results and also it is recommended to use it in other approaches for path planning and navigation.…”

“…In simulations, the approach proposed by Daza et al [22], which is a method based on A* and social momentum algorithms for avoiding the personal space of persons, was used. Simulation results for the proposed method are presented in the Figures 16 and 17.…”

“…These dynamic obstacles, as people, have properties: velocity or directions. Other works developed different social features that could be considered for path planning such as [20][21][22][23].…”

Almost Alive: Robots and Androids

“…The movements and body language communicate intentions and goals. We see if a person we meet in the corridor has noticed us and that it will not collide with us (Pacchierotti et al, 2006;Daza et al, 2021). This is a useful feature also in a robot that is not primarily social.…”