A Lightweight Crowd Model for Robot Social Navigation
This work addresses the challenge of efficient and socially compliant robot navigation in dense human-populated environments, representing an incremental improvement over existing methods.
The paper tackles the problem of real-time crowd movement prediction for robot social navigation by proposing a lightweight macroscopic model that balances accuracy and efficiency, achieving a 3.6 times reduction in inference time and a 3.1% improvement in prediction accuracy.
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.