Mission schedule of agile satellites based on Proximal Policy Optimization Algorithm
This addresses the increasing complexity of satellite operations for space management, but it appears incremental as it applies an existing reinforcement learning method to a specific domain.
The paper tackled the mission scheduling problem for agile satellites by proposing a model that integrates the Proximal Policy Optimization (PPO) algorithm, incorporating constraints like data download to find a new approach beyond traditional heuristic methods.
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.