2024/11/29 by Rahath Malladi, Amol Harsh, Malladi, Rahath +7
Engineering · Social Sciences · #FOS: Computer and information sciences #Geographic Information Systems Studies #Machine Learning (cs.LG) #Robotics (cs.RO) #Robotics and Automated Systems #Underwater Vehicles and Communication Systems
paper · pdf · doi:10.48550/arxiv.2411.19497
openalex publication_date 2024/11/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.