Reinforcement learning-based decision-making for spacecraft pursuit-evasion game in elliptical orbits

Weizhuo Yu, Chuang Liu, Xiaokui Yue

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

The orbital game theory is a fundamental technology for the cleanup of space debris to improve the safety of useful spacecraft in future, thus, this work develops a decision-making method by reinforcement learning technology to implement the pursuit-evasion game in elliptical orbits. The linearized Tschauner-Hempel equation describes the spacecraft's motion and the problem is formulated by game theory. Subsequently, an impulsive maneuvering model in a complete three-dimensional elliptical orbit is established. Then an algorithm based on deep deterministic policy gradient is designed to solve the optimal strategy for the pursuit-evasion game. For the successful decision of the pursuer, an extensive reward function is designed and improved considering the shortest time, optimal fuel, and collision avoidance. Finally, numerical simulations of a pursuit-evasion mission are performed to demonstrate the effectiveness and superiority of the proposed decision-making algorithm. The game success rate of the algorithm against targets with different maneuvering abilities is verified, which implies that the algorithm can be applied in extended scenarios.

Original languageEnglish
Article number106072
JournalControl Engineering Practice
Volume153
DOIs
StatePublished - Dec 2024

Keywords

  • Decision making
  • Deep deterministic policy gradient
  • Elliptical orbit
  • Impulsive maneuver
  • Pursuit-evasion game

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