Mission schedule of agile satellites based on Proximal Policy Optimization Algorithm

TL;DR

This paper introduces a novel satellite mission scheduling approach using Proximal Policy Optimization, integrating reinforcement learning to handle complex constraints and improve planning efficiency.

Contribution

It proposes a new reinforcement learning-based model for satellite mission scheduling, differing from traditional heuristic methods.

Findings

Reinforcement learning effectively models complex scheduling constraints.

The PPO-based approach improves scheduling efficiency.

The method adapts to increasing satellite operation complexity.

Abstract

Mission schedule of satellites is an important part of space operation nowadays, since the number and types of satellites in orbit are increasing tremendously and their corresponding tasks are also becoming more and more complicated. In this paper, a mission schedule model combined with Proximal Policy Optimization Algorithm(PPO) is proposed. Different from the traditional heuristic planning method, this paper incorporate reinforcement learning algorithms into it and find a new way to describe the problem. Several constraints including data download are considered in this paper.