Physics-Informed EvolveGCN: Satellite Prediction for Multi Agent Systems

TL;DR

This paper introduces a physics-informed dynamic graph neural network, EvolveGCN, to predict future positions of agents in multi-agent systems, ensuring physically plausible and accurate forecasts.

Contribution

It combines EvolveGCN with physics-based loss functions to improve multi-agent prediction accuracy and physical consistency.

Findings

Enhanced prediction accuracy over baseline models

Physically plausible future state estimations

Effective modeling of dynamic inter-agent relationships

Abstract

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 work proposes a novel method that leverages dynamic graphs in a forward looking manner. Specifically, the employment of EvolveGCN, a dynamic graph convolutional network, to forecast the evolution of inter-agent relationships over time. To improve prediction accuracy and ensure physical plausibility, this research incorporates physics constrained loss functions based on the Clohessy-Wiltshire equations of motion. This integrated approach enhances the reliability of future state estimations in multi-agent scenarios.