发表机构
Georgia Institute of Technology; Sandia National Laboratories（佐治亚理工学院; 桑迪亚国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对多智能体轨迹优化难题，提出分布式基于模型的扩散算法，证明其在有界延迟下的收缩性与鲁棒性，在circleswap和空战任务中性能优于集中式算法。
AI 中文摘要
同时优化多个智能体的轨迹是一个极具挑战性的问题，其面临非线性、非凸性以及维度灾难的困扰。交叉口处的一组相互作用的飞行器或自动驾驶汽车就是复杂多智能体系统的例子，若不做大量简化假设，这类问题仍难以解决。智能体间通信延迟的存在进一步增加了问题的难度。本文分析了分布式基于模型的扩散（Distributed Model-Based Diffusion）：一种适用于高度非线性、非凸、非光滑多智能体系统的基于采样的模型预测控制方法。我们证明了该方法在多智能体非凸问题下的收缩性和延迟鲁棒性，表明其适用于现实世界约束。我们在circleswap任务（合作式中等保真驾驶任务）以及空战场景中测试了该算法。尽管存在延迟，与集中式基于模型的扩散相比，我们的算法将circleswap的完成时间缩短了31%，并将空战胜率提高了25%。
英文摘要
Simultaneously optimizing the trajectories of multiple agents is a challenging problem plagued by nonlinearity, nonconvexity, and the curse of dimensionality. A collection of interacting aerial vehicles or self-driving cars in an intersection are examples of complex multi-agent systems that remain difficult to solve without many simplifying assumptions. The presence of communication latency between agents further increases the difficulty. In this paper, we analyze Distributed Model-Based Diffusion: a sampling-based Model-Predictive Control method suitable for highly nonlinear, nonconvex, nonsmooth, multi-agent systems. We prove contraction and robustness to latency for multi-agent, nonconvex problems, showing applicability to real-world constraints. We test the algorithm on a circleswap task, a cooperative medium-fidelity driving task, and in an aerial combat scenario. Despite the addition of latency, our algorithm improves circleswap makespan by 31% and increases aerial combat win rate by 25% compared to centralized Model-Based Diffusion.
Comments8 pages, 3 figures
