CN109146126A - Satellite imagery task optimum path planning method based on time window discretization - Google Patents

Satellite imagery task optimum path planning method based on time window discretization Download PDF

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CN109146126A
CN109146126A CN201810713347.9A CN201810713347A CN109146126A CN 109146126 A CN109146126 A CN 109146126A CN 201810713347 A CN201810713347 A CN 201810713347A CN 109146126 A CN109146126 A CN 109146126A
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task
time window
observation mission
node
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潘耀
饶启龙
罗达
池忠明
黄朝围
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Shanghai Institute of Satellite Engineering
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Abstract

The task that planning tasks are concentrated is ranked up by the satellite imagery task optimum path planning method based on time window discretization that the present invention provides a kind of, including step 1 according to SEE time sequence；Step 2, the SEE time window of each task is subjected to discretization according to equal numbers a, symbiosis is at a moment point；Step 3, corresponding attitude angle and imaging session time when satellite starts to be imaged to observation mission under each moment point are calculated, and generates corresponding node；Step 4, it according to the minimum principle of the energy, is constantly updated using existing neural network algorithm from first task to the node path of a last task, to obtain the consumption least optimal path of the energy.The present invention solves the technical problem of satellite imagery Task Autonomous planning, has apparent autonomous sexual clorminance and engineering adaptability compared to existing ground-based mission planing method.

Description

Satellite imagery task optimum path planning method based on time window discretization
Technical field
The present invention relates to satellite imagery Task Autonomous planing methods, and in particular, to one kind is based on time window discretization Satellite imagery task optimum path planning method.
Background technique
Satellite imagery mission planning problem refers under conditions of comprehensively considering user task demand and satellite resource ability, By Optimized Operation, reasonable distribution satellite resource, so that satellite can be completed to be imaged in the case where meeting various constraint conditions Task meets user demand in the hope of maximizing.Currently, being complete by ground system for most of satellite imagery mission planning problem At, then instruction sequence is directly uploaded to satellite.
For traditional mission planning, discretization degree is bigger, then the convergence for obtaining optimal solution is better, ground Computing resource also can satisfy requirement.And for the autonomous mission planning on star, due to spaceborne computer computing capability It is limited, if the discretization degree of time window is arranged too big, it will increase the calculation amount of algorithm, do not utilize and realize on star Autonomous mission planning；If what is be arranged is too small, and not can guarantee the convergence of optimal solution.This is also the autonomous mission planning on star With the place of the task with traditional planning different from ground.
Summary of the invention
For the technical problem of the in-orbit imaging task contexture by self of above-mentioned prior art Satellite, it is an object of the invention to A kind of satellite imagery task optimum path planning method based on time window discretization is provided, for realizing the in-orbit imaging of satellite Task Autonomous planning is of great significance.
A kind of satellite imagery task optimum path planning method based on time window discretization provided by the present invention, packet Include following steps:
Step 1, if planning tasks to be planned integrate as M={ m1,m2,L L,mn, it is seen that time window collection is combined into TW= {[Ts1,Te1],[Ts2,Te2],L L,[Tsn,Ten], it is seen that the equal numbers of time window are a, are appointed what planning tasks were concentrated Business is ranked up its visible chronological order according to satellite；
Step 2, the SEE time window of each observation mission is subjected to discretization according to equal numbers a, i.e., by SEE time Window is divided into a equal portions, total a moment point, and each moment point is considered as satellite to the imaging start time of observation mission；
Step 3, corresponding attitude angle and imaging session when satellite starts to be imaged to observation mission under each moment point are calculated Time, and each moment point is defined as a node point, and be ranked up according to moment sequence, and each node includes Observation mission number, imaging start time, corresponding imaging attitude angle and imaging session time, it may be assumed that
Wherein, i is observation mission number, and j is node ID, i.e. point (i, j) is j-th of section of i-th of observation mission Point, βijFor the lateral swinging angle that observation mission is imaged in satellite, qijThe pitch angle that observation mission is imaged for satellite；
Step 4, subsequent sight is successively expanded to from each node of first observation mission according to the serial number of node The feasible node of survey task is constantly updated using neural network algorithm from first observation mission further according to the minimum principle of the energy To the node path of a last observation mission, to obtain the consumption least optimal path of the energy.
Satellite imagery task optimum path planning method based on time window discretization of the invention, it is above-mentioned due to taking Technical solution so that satellite imagery Task Autonomous planing method of the invention has compared to existing ground-based mission planing method There are apparent autonomous sexual clorminance and engineering adaptability.
Detailed description of the invention
Upon reading the detailed description of non-limiting embodiments with reference to the following drawings, other feature of the invention, Objects and advantages will become more apparent upon:
Fig. 1 is a kind of satellite imagery task optimum path planning method based on time window discretization of the present invention Flow chart.
Specific embodiment
The present invention is described in detail combined with specific embodiments below.Following embodiment will be helpful to the technology of this field Personnel further understand the present invention, but the invention is not limited in any way.It should be pointed out that the ordinary skill of this field For personnel, without departing from the inventive concept of the premise, several changes and improvements can also be made.These belong to the present invention Protection scope.
Fig. 1 is the satellite imagery task optimum path planning method flow of the present invention based on time window discretization Figure, includes the following steps:
Step 1, if planning tasks to be planned integrate as M={ m1,m2,L L,mn, it is seen that time window collection is combined into TW= {[Ts1,Te1],[Ts2,Te2],L L,[Tsn,Ten], TsiIndicate satellite to task miBeginning SEE time, TeiExpression is defended Star is to task miEnd SEE time, it is seen that the equal numbers of time window are a, and the task that planning tasks are concentrated is according to satellite Its visible chronological order is ranked up；
Step 2, the SEE time window of each observation mission is subjected to discretization according to equal numbers a, i.e., by SEE time Window is divided into a equal portions, total a moment point, and each moment point is considered as satellite to the imaging start time of observation mission；
Step 3, corresponding attitude angle and imaging session when satellite starts to be imaged to observation mission under each moment point are calculated Time, and each moment point is defined as a node point, and be ranked up according to moment sequence, and each node includes Observation mission number, imaging start time, corresponding imaging attitude angle and imaging session time, it may be assumed that
Wherein, i is observation mission number, and j is node ID, i.e. point (i, j) is j-th of section of i-th of observation mission Point, βijFor the lateral swinging angle that observation mission is imaged in satellite, qijThe pitch angle that observation mission is imaged for satellite；TconijFor satellite To the lasting imaging time of observation mission.
Step 4, subsequent sight is successively expanded to from each node of first observation mission according to the serial number of node The feasible node of survey task, further according to the minimum principle of the 1, energy, i.e. satellite is seen since first observation mission to last one Survey task terminates, and the energy consumed by whole process (including attitude maneuver and imaging) should be minimum；Utilize 2, mature nerve Network algorithm loop iteration is constantly updated from first observation mission to the node path of a last observation mission, final to obtain To the consumption least optimal path of the energy.
By simulating, verifying, when the value range of equal numbers a is 5~50, the present invention achieves satellite imagery task certainly Best equal numbers when master program are 23, both can be reduced the calculation amount of mission planning, and also can guarantee the convergence of optimal path.
Specific embodiments of the present invention are described above.It is to be appreciated that the invention is not limited to above-mentioned Particular implementation, those skilled in the art can make a variety of changes or modify within the scope of the claims, this not shadow Ring substantive content of the invention.In the absence of conflict, the feature in embodiments herein and embodiment can any phase Mutually combination.

Claims (3)

1. a kind of satellite imagery task optimum path planning method based on time window discretization, which is characterized in that including such as Lower step:
Step 1, if task-set to be planned is M={ m1,m2,L L,mn, it is seen that time window collection is combined into TW={ [Ts1,Te1], [Ts2,Te2],L L,[Tsn,Ten], TsiIndicate satellite to task miBeginning SEE time, TeiIndicate satellite to task mi's Terminate SEE time, it is seen that the equal numbers of time window are a, when the task of planning tasks concentration is visible to its according to satellite Between sequencing be ranked up；
Step 2, the SEE time window of each observation mission is subjected to discretization according to equal numbers a, i.e., by SEE time window It is divided into a equal portions, total a moment point, and each moment point is considered as satellite to the imaging start time of observation mission；
Step 3, corresponding attitude angle and imaging session time when satellite starts to be imaged to observation mission under each moment point are calculated, And each moment point is defined as a node point, and be ranked up according to moment sequence, and each node includes that observation is appointed Business number, imaging start time, corresponding imaging attitude angle and imaging session time, it may be assumed that
Wherein, i is observation mission number, and j is node ID, i.e. point (i, j) is j-th of node of i-th of observation mission, βijFor the lateral swinging angle that observation mission is imaged in satellite, qijFor the pitch angle that observation mission is imaged in satellite, TconijFor satellite pair The lasting imaging time of observation mission；
Step 4, it successively expands to subsequent observation from each node of first observation mission according to the serial number of node and appoints The feasible node of business, further according to the minimum principle of the energy, i.e., satellite is since first observation mission to a last observation mission Terminate, the energy consumed by whole process should be minimum；Using mature neural network algorithm loop iteration, constantly update from the One observation mission finally obtains the consumption least optimal path of the energy to the node path of a last observation mission.
2. the satellite imagery task optimum path planning method according to claim 1 based on time window discretization, It is characterized in that, the value range of numbers a is waited to take 5~50.
3. the satellite imagery task optimum path planning method according to claim 2 based on time window discretization, Be characterized in that, wait numbers a be 23 when, both can be reduced the calculation amount of mission planning, and also can guarantee the convergence of optimal path.
CN201810713347.9A 2018-07-02 2018-07-02 Satellite imagery task optimum path planning method based on time window discretization Pending CN109146126A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109767128A (en) * 2019-01-15 2019-05-17 中国人民解放军国防科技大学 imaging satellite autonomous task planning method based on machine learning
CN109918731A (en) * 2019-01-31 2019-06-21 上海卫星工程研究所 Satellite task planning simulation analysis method and system based on critical path
CN111612384A (en) * 2020-06-23 2020-09-01 中国人民解放军国防科技大学 Multi-satellite relay task planning method with time resolution constraint

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104050324A (en) * 2014-06-23 2014-09-17 中国人民解放军国防科学技术大学 Mathematical model construction method and solving method for single-star task planning problem
CN106570614A (en) * 2016-10-14 2017-04-19 上海微小卫星工程中心 Onboard autonomous distributed task scheduling method
CN106647787A (en) * 2016-11-28 2017-05-10 中国人民解放军国防科学技术大学 Satellite onboard autonomous task planning method and system

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104050324A (en) * 2014-06-23 2014-09-17 中国人民解放军国防科学技术大学 Mathematical model construction method and solving method for single-star task planning problem
CN106570614A (en) * 2016-10-14 2017-04-19 上海微小卫星工程中心 Onboard autonomous distributed task scheduling method
CN106647787A (en) * 2016-11-28 2017-05-10 中国人民解放军国防科学技术大学 Satellite onboard autonomous task planning method and system

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109767128A (en) * 2019-01-15 2019-05-17 中国人民解放军国防科技大学 imaging satellite autonomous task planning method based on machine learning
CN109918731A (en) * 2019-01-31 2019-06-21 上海卫星工程研究所 Satellite task planning simulation analysis method and system based on critical path
CN109918731B (en) * 2019-01-31 2023-04-07 上海卫星工程研究所 Satellite task planning simulation analysis method and system based on critical path
CN111612384A (en) * 2020-06-23 2020-09-01 中国人民解放军国防科技大学 Multi-satellite relay task planning method with time resolution constraint
CN111612384B (en) * 2020-06-23 2023-04-25 中国人民解放军国防科技大学 Multi-star relay task planning method with time resolution constraint

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Application publication date: 20190104