Physics-Informed EvolveGCN: Satellite Prediction for Multi Agent Systems
Timothy Jacob Huber, Madhur Tiwari, Camilo A. Riano-Rios
在快速发展的自治系统领域,共享环境中的代理之间的交互对于增强整体系统能力是不可避免的,也是必不可少的。 这种多智能体系统的一个关键要求是每个代理能够可靠地预测其最近邻居的未来位置。 传统上,图形和图形理论一直是建模代理间通信和关系的有效工具。 虽然这种方法被广泛使用,但本工作提出了一种新的方法,以前瞻性的方式利用动态图形。 具体来说,使用动态图卷积网络EvolveGCN来预测代理间关系随着时间的推移的演变。 为了提高预测准确性和确保物理合理性,本研究结合了基于Clohessy-Wiltshire运动方程的物理约束损失函数。 这种集成方法增强了多智能体场景中未来状态估计的可靠性。
In the rapidly evolving domain of autonomous systems, interaction among agents within a shared environment is both inevitable and essential for enhancing overall system capabilities. A key requirement in such multi-agent systems is the ability of each agent to reliably predict the future positions of its nearest neighbors. Traditionally, graphs and graph theory have served as effective tools for modeling inter agent communication and relationships. While this approach is widely used, the present...