A Lightweight Crowd Model for Robot Social Navigation

2025-08-27

AI Summary

Addressing the challenge of real-time socially compliant navigation for robots in dense pedestrian environments—where conventional microscopic models incur excessive computational cost and macroscopic models suffer from low accuracy or poor efficiency—this paper proposes a lightweight macroscopic crowd motion prediction model. Leveraging spatiotemporal dynamics of pedestrian flow, the model simplifies spatiotemporal modeling mechanisms, significantly reducing computational complexity. It is tightly integrated with a socially aware path planning framework to enable safe, efficient, and socially normative navigation decisions. Experimental results demonstrate that, compared to baseline methods, the proposed model achieves a 3.6× speedup in inference latency and a 3.1% improvement in prediction accuracy. Crucially, it maintains strong generalization capability while satisfying real-time constraints. This work provides a scalable, perception-decision co-designed solution for deploying service robots in dynamic, crowded urban settings.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Robots operating in human-populated environments must navigate safely and efficiently while minimizing social disruption. Achieving this requires estimating crowd movement to avoid congested areas in real-time. Traditional microscopic models struggle to scale in dense crowds due to high computational cost, while existing macroscopic crowd prediction models tend to be either overly simplistic or computationally intensive. In this work, we propose a lightweight, real-time macroscopic crowd prediction model tailored for human motion, which balances prediction accuracy and computational efficiency. Our approach simplifies both spatial and temporal processing based on the inherent characteristics of pedestrian flow, enabling robust generalization without the overhead of complex architectures. We demonstrate a 3.6 times reduction in inference time, while improving prediction accuracy by 3.1 %. Integrated into a socially aware planning framework, the model enables efficient and socially compliant robot navigation in dynamic environments. This work highlights that efficient human crowd modeling enables robots to navigate dense environments without costly computations.

Problem

• Real-time crowd movement estimation for robot navigation
• Overcoming computational limitations in dense crowd modeling
• Balancing prediction accuracy with computational efficiency

Innovation

• Lightweight macroscopic crowd prediction model
• Simplifies spatial and temporal pedestrian flow processing
• Reduces inference time while improving accuracy