Multi Agent Trajectory Generation

Multi-agent trajectory generation focuses on creating safe and efficient paths for multiple agents, such as robots or autonomous vehicles, to move simultaneously within a shared environment. Current research emphasizes developing efficient algorithms, including Bayesian optimization and consensus-based methods like ADMM, to handle the complexity of coordinating multiple agents, often incorporating convex relaxations to improve computational tractability. Data-driven approaches, utilizing diffusion models and language modeling techniques, are also gaining prominence for generating realistic and diverse trajectories, particularly in scenarios involving human-robot interaction. These advancements have significant implications for robotics, autonomous driving, and other fields requiring coordinated multi-agent systems.