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A Reinforcement Learning-Based Method for Autonomous Evasive Maneuvering of Spacecraft against Multiple Interceptors

A technology of reinforcement learning and spacecraft, applied in the field of anti-interception, can solve the problem of low evasion success rate, achieve the effect of improving evasion success rate, saving energy, and reducing engine switching time

Active Publication Date: 2022-03-01
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to solve the problem of the low success rate of avoiding multi-interceptors in existing spacecraft program maneuvers, and proposes a self-avoiding maneuver method for spacecrafts based on reinforcement learning for multi-interceptors

Method used

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  • A Reinforcement Learning-Based Method for Autonomous Evasive Maneuvering of Spacecraft against Multiple Interceptors
  • A Reinforcement Learning-Based Method for Autonomous Evasive Maneuvering of Spacecraft against Multiple Interceptors
  • A Reinforcement Learning-Based Method for Autonomous Evasive Maneuvering of Spacecraft against Multiple Interceptors

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specific Embodiment approach 1

[0020] Specific implementation mode 1: A method for self-avoiding maneuvers of spacecraft to multiple interceptors based on reinforcement learning described in this implementation mode, the method is specifically implemented through the following steps:

[0021] Step 1: Establish the space dynamics models of the spacecraft and the interceptor respectively;

[0022] Step 2: Based on the space dynamics model of the spacecraft and interceptor established in Step 1, establish a true-scale guidance model for multiple interceptors;

[0023] Step 3: Take each engine of the spacecraft as an agent to establish a decision-making model for spacecraft evasion maneuvers;

[0024] Step 4: Establish a multi-agent autonomous decision-making training system based on reinforcement learning theory;

[0025] Step 5: Apply the model established in step 1, step 2 and step 3 to the system of step 4, and train the spacecraft evasive maneuver decision-making model offline;

[0026] Step 6: Apply the...

specific Embodiment approach 2

[0028] Specific embodiment two: the difference between this embodiment and specific embodiment one is: said step one establishes the space dynamics model of spacecraft and interceptor respectively, and its specific process is:

[0029] In the geocentric inertial coordinate system, the space dynamics model of the spacecraft is:

[0030]

[0031] in, is the space position vector of the spacecraft, r M for Corresponding scalar, m M is the instantaneous mass of the spacecraft, T M is the total thrust of the spacecraft engine, is the unit vector of the thrust direction of the spacecraft engine, μ is the gravitational constant of the earth, and the value is 3.986×10 5 km 3 / s 2 ; for The second derivative of , Be the perturbation acceleration vector, set as a constant value in the present invention;

[0032] The mass change rate of the spacecraft is:

[0033]

[0034] in, is the rate of change of spacecraft mass, I sp,M is the specific impulse of the space...

specific Embodiment approach 3

[0041] Specific embodiment three: the difference between this embodiment and specific embodiment two is that: in the step two, according to the space dynamics model of the spacecraft and the interceptor established in the step one, a multi-interceptor true-scale guidance model is established, which The specific process is:

[0042] According to the space dynamics model of the spacecraft and the interceptor established in step 1, the relative motion model of the spacecraft and the interceptor is obtained as:

[0043]

[0044] in, is the combined thrust vector of the spacecraft engine, is the combined thrust vector of the interceptor engine;

[0045] In order to simplify the calculation, only the maximum sight angle and saturated maneuvering overload are constrained, and the noise and other disturbance problems are not considered. Decompose equation (5) along the direction of the projectile line of sight and the direction perpendicular to the direction of projectile line...

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Abstract

The invention relates to a self-avoiding maneuver method for spacecraft to multiple interceptors based on reinforcement learning, which belongs to the field of anti-interception technology. The invention solves the problem of low success rate of evading multi-interceptors in the conventional spacecraft procedure maneuver. The invention provides an autonomous evasive maneuver method based on a deep neural network that is not limited by the quality and material of the spacecraft. It consists of two parts, an offline training system and an online decision network, which use less computing resources for the spacecraft itself. , with real-time decision-making capabilities, which improves the success rate of spacecraft evasion against multiple interceptors. When the spacecraft adopts the autonomous avoiding maneuver method of the present invention, the average success rate of the avoiding maneuver is 49%, and the avoiding success rate is increased by 29%. This method can effectively reduce the switching time of the engine during the avoidance process, and save energy. The invention can be applied to autonomous avoidance of multiple interceptors by spacecraft.

Description

technical field [0001] The invention belongs to the technical field of anti-interception, and in particular relates to a self-avoiding maneuver method for a spacecraft against multiple interceptors based on reinforcement learning. Background technique [0002] As early as the 1970s, research on maneuvering and avoiding technology has been carried out in foreign countries, mostly based on the analysis of simplified motion models, and maneuvering and avoiding strategies are only designed for special trajectory points. Early domestic research focused on simulation modeling, and a large number of interceptor evasion simulation systems were established based on kinematic constraints. On this basis, some scholars have proposed methods such as maneuver avoidance strategy based on differential game and impulsive avoidance strategy based on optimal control. These methods are offline planning methods based on mathematical models and do not have autonomy. During the orbital operation ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/27G06N3/04G06N3/08
CPCG06F30/27G06N3/08G06N3/045
Inventor 白成超郭继峰郑红星赵毓
Owner HARBIN INST OF TECH
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