Revising the Observation Satellite Scheduling Problem Based on Deep Reinforcement Learning

Yixin Huang,Shufan Wu,Zhongcheng Mu,Benjie Cui,Yuxiao Duan

doi:10.3390/rs13122377

Abstract

Earth observation satellite task scheduling research plays a key role in space-based remote sensing services. An effective task scheduling strategy can maximize the utilization of satellite resources and obtain larger objective observation profits. In this paper, inspired by the success of deep reinforcement learning in optimization domains, the deep deterministic policy gradient algorithm is adopted to solve a time-continuous satellite task scheduling problem. Moreover, an improved graph-based minimum clique partition algorithm is proposed for preprocessing in the task clustering phase by considering the maximum task priority and the minimum observation slewing angle under constraint conditions. Experimental simulation results demonstrate that the deep reinforcement learning-based task scheduling method is feasible and performs much better than traditional metaheuristic optimization algorithms, especially in large-scale problems.

Highlights

Earth observation satellites (EOSs) are platforms equipped with optical instruments in order to take photographs of specific areas at the request of users [1]
Observation satellite task scheduling policy plays a crucial role in providing highquality space-based information services
Many algorithms based on traditional optimization methods such as genetic algorithm (GA) and simulated annealing algorithm (SA) have been successfully applied in the EOS scheduling problem (EOSSP)

Summary

Introduction

Earth observation satellites (EOSs) are platforms equipped with optical instruments in order to take photographs of specific areas at the request of users [1]. With the increase in multi-user and multi-satellite space application scenarios [5,6], it is becoming more difficult to meet various observation requirements under the limitation of satellite resources. An effective EOS scheduling algorithm plays an important role to improve high-quality space-based information services, and guides the corresponding EOSs on how to perform the following actions, and controls the time to start the observations [5]. The main purpose is to maximize the observation profit within the limited observation time window and with other resources (for example, the available energy, the remaining data storage, etc.) [7,8]. Inspired by increasing demands on scheduling EOSs effectively and efficiently, the study of the EOSSP has gained more and more attention. Wang et al [7]

Methods

Results

Conclusion