SANGO: Socially Aware Navigation through Grouped Obstacles

TL;DR

SANGO is a deep reinforcement learning-based navigation method that groups obstacles and follows social norms to improve safety and comfort in crowded environments.

Contribution

It introduces a novel obstacle grouping and social norm adherence framework using deep RL for socially aware navigation.

Findings

Reduces discomfort by up to 83.5%

Decreases collision rates by up to 29.4%

Achieves higher success in dynamic scenarios

Abstract

This paper introduces SANGO (Socially Aware Navigation through Grouped Obstacles), a novel method that ensures socially appropriate behavior by dynamically grouping obstacles and adhering to social norms. Using deep reinforcement learning, SANGO trains agents to navigate complex environments leveraging the DBSCAN algorithm for obstacle clustering and Proximal Policy Optimization (PPO) for path planning. The proposed approach improves safety and social compliance by maintaining appropriate distances and reducing collision rates. Extensive experiments conducted in custom simulation environments demonstrate SANGO's superior performance in significantly reducing discomfort (by up to 83.5%), reducing collision rates (by up to 29.4%) and achieving higher successful navigation in dynamic and crowded scenarios. These findings highlight the potential of SANGO for real-world applications, paving the way for advanced socially adept robotic navigation systems.