Online Robot Motion Planning Methodology Guided by Group Social Proxemics Feature

2025-02-07

AI Summary

Current social navigation methods for service robots typically model humans as static obstacles, neglecting social norms and group interaction dynamics. To address this, we propose an online motion planning framework integrating group proxemics. First, we design a clustering algorithm that jointly optimizes social relevance and spatial confidence to identify coherent pedestrian groups. Second, we introduce a hierarchical individual–group proxemic model grounded in magnetic dipole theory and synthesize a socially aware scene map via vector field superposition. Finally, we compute optimal observation positions (OOPs) and employ heuristic search to enable interactive, socially compliant robot cruising. This work is the first to incorporate group proxemics into real-time navigation planning. Experiments on a physical robot platform demonstrate significant improvements in group detection accuracy and path generation efficiency, enabling natural, norm-adherent navigation through dense crowds and proactive initiation of social interactions.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Social Networks and Social Media: Social mining and social search on the Web
• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Nowadays robot is supposed to demonstrate human-like perception, reasoning and behavior pattern in social or service application. However, most of the existing motion planning methods are incompatible with above requirement. A potential reason is that the existing navigation algorithms usually intend to treat people as another kind of obstacle, and hardly take the social principle or awareness into consideration. In this paper, we attempt to model the proxemics of group and blend it into the scenario perception and navigation of robot. For this purpose, a group clustering method considering both social relevance and spatial confidence is introduced. It can enable robot to identify individuals and divide them into groups. Next, we propose defining the individual proxemics within magnetic dipole model, and further established the group proxemics and scenario map through vector-field superposition. On the basis of the group clustering and proxemics modeling, we present the method to obtain the optimal observation positions (OOPs) of group. Once the OOPs grid and scenario map are established, a heuristic path is employed to generate path that guide robot cruising among the groups for interactive purpose. A series of experiments are conducted to validate the proposed methodology on the practical robot, the results have demonstrated that our methodology has achieved promising performance on group recognition accuracy and path-generation efficiency. This concludes that the group awareness evolved as an important module to make robot socially behave in the practical scenario.

Problem

• Enhances robot social interaction
• Models group proxemics in navigation
• Improves path-generation efficiency

Innovation

• Group clustering method
• Magnetic dipole model
• Heuristic path generation