Autonomous Multi-Agent Navigation in Crowded Environments: A Comprehensive Survey and Analysis

Abstract

This paper presents a comprehensive survey and analysis of autonomous multi-agent navigation in crowded environments, addressing the fundamental challenge of coordinating multiple mobile agents to achieve collision-free, efficient, and socially-compliant motion in dynamic spaces shared with humans. We examine the theoretical foundations spanning collision avoidance algorithms, social force models, and machine learning approaches. Through systematic analysis of velocity obstacles, reciprocal velocity obstacles, optimal reciprocal collision avoidance, and deep reinforcement learning methods, we identify key advantages and limitations of current approaches. The paper critically evaluates computational complexity, scalability constraints, safety guarantees, and real-world deployment challenges. We present comparative performance metrics across simulation and physical implementations, demonstrating that hybrid approaches combining classical geometric methods with learned policies achieve superior performance in dense crowds. Our analysis reveals that while reinforcement learning methods show promise for social compliance, they face challenges in safety certification and sim-to-real transfer. We conclude with recommendations for future research directions, emphasizing the need for unified frameworks that integrate predictive modeling, multi-modal learning, and formal verification methods to enable robust deployment in safety-critical applications.