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Genetic-algorithm-based active removing task planning method for space debris

A technology for space debris and mission planning, applied in genetic rules, calculations, genetic models, etc., can solve the problems of encoding methods and cross-space debris active clearance task planning falling into local optimum

Inactive Publication Date: 2017-11-10
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0010] The present invention solves the problem that the existing genetic algorithm coding method and crossover and mutation operations easily lead to the problem that the task planning of the active removal of space debris falls into local optimum

Method used

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  • Genetic-algorithm-based active removing task planning method for space debris
  • Genetic-algorithm-based active removing task planning method for space debris
  • Genetic-algorithm-based active removing task planning method for space debris

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Experimental program
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specific Embodiment approach 1

[0083] The task planning method for the active removal of space debris based on the genetic algorithm includes the following steps:

[0084] Step 1, Encoding:

[0085] The debris removal task considers using a robotic arm to grab debris, and the robotic arm is triggered from the platform to remove debris;

[0086] Let the number of fragments be n-1; consider the platform as the first city, and the fragments as the second to n cities; use the traversal order of the cities as the code, that is (z 1 ,z 2 ,...,z n ) is a full permutation of {1,2,...,n}, where z 1 ,z 2 ,...,z n Indicates the order of traversing all cities each time, the order z of traversing all cities each time 1 ,z 2 ,...,z n As an encoding; there are (n-1)! A possibility,! Represents factorial; each possible sequence code is regarded as an individual, and all possible sequence codes are combined as the initial population;

[0087] Step 2. Initialize the group:

[0088] According to the relative posit...

specific Embodiment approach 2

[0105] The specific process of step 4 of this embodiment includes the following steps:

[0106] Step 4.1, first calculate the sum of the fitness of all individuals in the calculation population Where f(j) is the fitness of individual j;

[0107] Step 4.2, calculate the probability that each individual is inherited into the next generation population:

[0108]

[0109] Step 4.3, and calculate the cumulative probability of each individual:

[0110]

[0111] Step 4.4, the next step is to generate a uniformly distributed pseudo-random number r in the [0,1] interval;

[0112] Step 4.5, if r1 , select individual 1; otherwise, select individual i such that: q i ≤r≤q i+1 established;

[0113] Step 4.6, repeat steps 4.4 and 4.5 for a total of M times to complete the selection operation, and obtain the intermediate population.

[0114] Other steps and parameters are the same as those in the first embodiment.

specific Embodiment approach 3

[0115] T1=1000 described in this embodiment. When there are less than 12 space debris, only need to set T1 to 1000 can make the overall performance index reach the optimum, and the time-consuming and fast convergence speed.

[0116] Other steps and parameters are the same as those in Embodiment 1 or 2.

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Abstract

Disclosed in the invention is a genetic-algorithm-based active removing task planning method for space debris. The invention relates to an active removing task planning method for space debris, so that a problem the active space debris removing task planning is easy to fall into local optimization because of the coding way and the crossed and mutation operations according to the existing genetic algorithm is solved. The task planning method is used for solving a debris capturing path optimization problem. According to task characteristics of a platform, a debris removing task is set; mathematical modeling of the platform task planning problem is carried out to obtain a traveling saleman urban shortest path realization problem; on the basis of characteristics of space debris, a fitness function for a mechanical arm capturing plan and a fitness function for a flying net and mechanical arm capturing plan are designed respectively; a specific genetic parameter is set and optimization solution is carried out by using a genetic algorithm, so that convergence is realized rapidly and the capturing path for space debris is planned. The active removing task planning method is suitable for active removing task planning of space debris.

Description

technical field [0001] The invention relates to a task planning method for actively removing space debris. Background technique [0002] In recent years, with the continuous development of aerospace technology, human spaceflight activities have become more and more frequent. At the same time, space debris is increasing at an exponential rate, becoming a serious challenge for the effective use of space resources in the future. Although some passive measures such as evasion and protection have been widely adopted in order to deal with the threat of space debris, in order to fundamentally govern the space environment, active removal measures are still needed, so it is of great significance to carry out research on the active removal of space debris on orbit . Space debris removal tools mainly include robotic arms, flying nets, inflatable structures, javelin ropes, and electrostatic adsorption blankets. The mechanical arm has the best performance when capturing high-torque or...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06N3/12G06F17/18
CPCG06F17/18G06N3/126G06Q10/047
Inventor 吕跃勇杜晔郭延宁张米令邱爽陈亮名
Owner HARBIN INST OF TECH
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