The application discloses a
spacecraft autonomous avoidance of
space debris based on a deep deterministic policy gradient
algorithm, classifies a scene, constructs different training environments according to different types of
orbit threats, constructs a constrained Markov
decision process, makes avoidance decisions while satisfying multiple constraints of the
spacecraft, in an offline training stage, makes the
intelligent agent interact with different types of environments continuously, and learns and trains corresponding neural networks, in an online application stage, first,
threat identification is performed, corresponding trained neural networks are extracted, avoidance actions are quickly generated, the
spacecraft can learn to make corresponding optimal decisions in different scenes and environment states, and the avoidance of
space debris is realized online. The technology can enable the spacecraft to
face space debris threats, autonomously formulate avoidance strategies, autonomously complete avoidance actions, autonomously grasp the timing and strength of
space debris avoidance, and effectively respond to the space debris threats at the minimum cost (minimize interference with task execution).