CN1858553A - Method for selecting reference planet in deep space self-aid navigation based on genetic algorithm - Google Patents

Abstract

This invention relates to a selection method for reference stars in the independent optical navigation in the sky based on the inherit algorithm including the following steps: selecting navigation minor planets, in which, the vision angle of two planets is greater than 5 deg. and planets meeting the needs are evaluated to take the ones with the first 20 of the evaluated values to be coded to generate an initial stock based on the given maximum iteration times and number of the stocks, then taking the first 12 navigation small planets in each stock to compute the adaptive value and carrying out selective operation based on the turntable bet and cross operation based on greedy cross operators and variance operation based on the random variance operators with a fixed end to be noted as one time of iteration, controlling the stock in density, judging if the iteration times is equal to the maximum iteration times, if so, the operation is finished and the result is output, otherwise, the stock is re-generated to selection operation is carried out.

Description

System of selection based on reference planet in the autonomous deep-space optical navigation of genetic algorithm

Technical field

The present invention relates to the autonomous optical navigation of deep space probe, be specifically related in the autonomous deep-space optical navigation asteroidal selection method as reference planet.

Background technology

Survey of deep space is one of focus of 21 century world's aerospace big country concern, and China also proposes in " China Aerospace " white paper, will " carry out the research in advance based on the survey of deep space of moon exploration ".Because detection of a target distance, factor such as flight environment of vehicle is uncertain, there are many-sided difficult problems such as real-time and reliability in the control of deep space probe and operation.Using the detector proprietary technology is an effective way that addresses these problems, it is by constructing software systems on detector, finish detector mission planning, the movable functions such as execution, fault diagnosis, fault recovery of decomposing, make the operation of detector, control form closed loop on the real star.Detector is the main body in the survey of deep space task, and it enters space from earth transmission, implements moon exploration, planet and satellite sounding thereof and asteroid and comet and surveys.According to the difference of detection mission, detector portability lander or probe vehicles, and it is discharged into the celestial body surface carries out proximity detection.The detection of a target in the survey of deep space and the distance of the earth are very remote (now to have reached 7.2 * 10 ⁹Km), only rely on ground directly the mode of control or straighforward operation be difficult to finish detected event to solar system celestial body.This has been just for the survey of deep space task has proposed very harsh requirement, as the requirement of reliability, communication network and the real-time of operation cost, task etc.The way that solves is exactly to adopt programming dispatching technology in the artificial intelligence, mode identification technology, fault diagnosis technology etc., sets up the self-control system on the detector.

In the process of cruising, the main activities that detector is finished is to carry out based on asteroidal autonomous optical navigation, and to realize finishing automatically the work of independent navigation, the asteroidal selection and the planning of taking pictures are particularly important, the deep space probe autonomous optical navigation mainly is that suitable navigation asteroid is taken pictures, then to Flame Image Process, thereby obtain current position of detector.

In existing autonomous optical navigation planning technology scheme, all only mention the asteroidal a series of criterions of selecting of navigation, and do not relate to next step the sequential program(me) of taking pictures, formerly technology [1] is (referring to StephanieDelavault, Jerome Berthier, Jacques Foliard, Optical Navigation to a Near EarthObject) used the asteroid phase angle in, speed, the distance of detector are selected asteroid as criterion, and further its navigation error of bringing are analyzed relatively.Formerly technology [2] (referring to Laurent Chausson, Stephanie Delavault.Optical Navigation Performance During InterplanetaryCruise, 17 ^ThISSFD, Moscow, Russia, 2003) considered in that more navigation asteroid selects criterion, the speed, the distance that comprise asteroidal visible size, phase angle, relative detector, and the star density of asteroid position etc., and its navigation error of bringing carried out further analysis.But formerly technology is all just finished and is selected asteroidal, and does not have to consider the asteroidal mutual constraint of navigation pick out, can not be too little etc. as the sight line angle between two asteroids, so navigation accuracy is lower.In addition, also do not carry out the consideration of integrated navigation precision and fuel consumption.

Summary of the invention

The purpose of this invention is to provide a kind of system of selection based on reference planet in the autonomous deep-space optical navigation of genetic algorithm, lower and do not consider the defective of fuel consumption to solve the prior art navigation accuracy.It comprises the steps: one, according to the present position of detector in celestial body, from asteroidal location database, select the navigation asteroid; It is as follows to select the asteroidal criterion of navigation from asteroidal location database: (1) visible imaging size is less than 12; (2) spend greater than 135 from detector to asteroidal sight line with the sight line angle from the detector to the sun; (3) asteroidal speed is less than 0.7km/s; (4) asteroid and detector distance are less than 0.8 astronomical unit; (5) two asteroidal sight line angles are greater than 5 degree; Two, the navigation asteroid that meets criterion is carried out comprehensive assessment, and according to assessed value from big to small to its ordering; Three, getting assessed value is preceding 20 navigation asteroid, and encodes for these 20 navigation asteroids; Four,, generate initial population according to given genetic algorithm parameter maximum iteration time and population number; Five, preceding 12 navigation asteroids of getting in each population carry out adaptive value calculating; Six, select operator to carry out selection operation according to roulette respectively, carry out interlace operation according to greedy crossover operator, the random variation operator fixing according to an end carries out mutation operation, and is designated as iteration one time; Seven, population is carried out concentration control; Eight, judge whether iterations equals maximum iteration time; The result is for being, then execution in step nine, finish and the output result, this population is optimum population, preceding 12 navigation asteroids of this population put in order as the order of taking pictures with reference to planet and according to this as navigation; The result of step 8 then returns step 4 for not.

Ultimate principle of the present invention is: work of the present invention is the asteroid of picking out current suitable navigation according to certain criterion, and the asteroid that is fit to is carried out comprehensive assessment, draws 12 asteroidal orders of taking pictures of navigation at last.Thereby make the cost performance of navigation this time the highest.In asteroidal the selecting of navigation, use a series of criterion of selecting, as asteroidal visible size, speed, phase angle, and with the distance of detector, and position in celestial body etc.And each asteroid is for the fitness difference of different criterions, then also need the further comprehensive assessment of the asteroid that meets criterion, for select navigation asteroid, because finish 12 asteroids take pictures spent fuel with its take pictures the order relevant, then need to take pictures the order plan.On the asteroid number, selected the mode of difference for use, promptly plan and take 20 when initial, what finally be used to navigate is 12 asteroids, pick out 12 in these 20, make these 12 total navigation assessed values and total ratio maximum that expends fuel, definition ratio is the cost performance of navigation.Improved genetic algorithm is adopted in the asteroidal sequential program(me) of taking pictures of navigating, and this algorithm extremely is fit to solve the planning of independent navigation.Because the present invention as selecting condition, is improved the precision of navigation in step 1, the order of taking pictures of trying to achieve makes the fuel consumption of detector minimum.

Description of drawings

Fig. 1 is the process flow diagram of the inventive method.

Embodiment

Embodiment one: specify present embodiment below in conjunction with Fig. 1.It is realized by following step: one, according to the present position of detector in celestial body, select the navigation asteroid from asteroidal location database; It is as follows to select the asteroidal criterion of navigation from asteroidal location database: (1) visible imaging size can distinguish it less than 12; (2) spend greater than 135 from detector to asteroidal sight line with the sight line angle from the detector to the sun, to prevent the solar light irradiation camera; (3) asteroidal speed satisfies the requirement of star Pattern Recognition Algorithm less than 0.7km/s; (4) asteroid and detector distance are less than 0.8 astronomical unit, to reduce sight line vector deviation; (5) two asteroidal sight line angles are greater than 5 degree; Two, the navigation asteroid that meets criterion is carried out comprehensive assessment, and according to assessed value from big to small to its ordering; Three, getting assessed value is preceding 20 navigation asteroid, and encodes for these 20 navigation asteroids; If not enough 20 of the navigation asteroid that step 1 is picked out, then explanation is not suitable for carrying out based on asteroidal optical guidance this moment; Can postpone a period of time carries out the operation of the inventive method again; Four,, generate initial population according to given genetic algorithm parameter maximum iteration time and population number; Five, preceding 12 navigation asteroids of getting in each population carry out adaptive value calculating; Six, select operator to carry out selection operation according to roulette respectively, carry out interlace operation according to greedy crossover operator, the random variation operator fixing according to an end carries out mutation operation, and is designated as iteration one time; Seven, population is carried out concentration control; Eight, judge whether iterations equals maximum iteration time; The result is for being, then execution in step nine, finish and the output result, this population is optimum population, preceding 12 navigation asteroids of this population put in order as the order of taking pictures with reference to planet and according to this as navigation; The result of step 8 then returns step 4 for not.

In five criterions of step 1, be easier to realize with the sight line angle of the sun, speed, distance and two constraints such as asteroidal sight line angle, below the constraint of the visible imaging size of weight analysis.Asteroid visible imaging size in the navigation camera also can be called visible yardstick, can weigh asteroid at the magazine imaging size of CCD (following no longer distinguish both), outside the Pass magnitude, the albedo of itself and asteroid self has, also with angular dependence (-dance) mutually.BDL (Bureau des Longitudes) database can provide every asteroidal orbital tracking, parameters such as magnitude H, albedo G.According to the Bowell model, visible imaging size V can calculate by following formula:

V＝H-5log(r·d)+2.5log[(1-G)Φ ₁(α)+GΦ ₂(α)]

${\mathrm{\Φ}}_i\left(\mathrm{\α}\right)=\exp [-A_i{\left(\tan \frac{\mathrm{\α}}{2}\right)}^{B_i}]i=\mathrm{1,2}$

Wherein: r is the sun-asteroidal distance, and d is detector-asteroidal distance, and α is asteroid-detector-sun phase angle, A ₁=3.33, B ₁=0.63, A ₂=1.87, B ₂=1.22

Carry out comprehensive assessment with asteroidal navigation assessed value to the navigation that meets criterion is asteroidal in step 2, asteroidal navigation assessed value is asteroid distance, speed, phase place, visible size and the weighted sum of the deviation of optimal value, specifically suc as formula:

$\mathrm{Value}=\underset{i=1}{\overset{5}{\mathrm{\Σ}}}{w}_i{\mathrm{Er}}_i$

Wherein: w _i---weights Er _i---with the deviation of optimal value

When selecting the navigation asteroid, various parameters are independently to asteroidal assessment, same asteroid is different by different parameters evaluation, bigger apart from less possible speed, and the little possible phasing degree of speed that is to say and can not pick out optimum navigation asteroid by a parameter near limit value.And, the a certain moment, satisfactory asteroid may be a lot, this just requires a criterion to go to select optimum navigation asteroid, can carry out comprehensive assessment, ordering to qualified all asteroids, thereby finds out several optimum navigation asteroids.Each autonomous optical navigation sorts by the navigation assessed value from big to small to the asteroid that satisfies condition, and selects the highest being used for of navigation assessed value to navigate.

Genetic algorithm is biological heredity and evolutionary process and a kind of adaptive global optimization probabilistic search algorithm that forms in physical environment of simulation, and he is a kind of algorithm that the self-adaptation behavior of research Nature and Man worker system proposes by professor Holland of Univ Michigan-Ann Arbor USA the earliest.One is difficult to resolve the problem of determining is how to find optimum solution quickly and prevent " precocity " convergence problem in the genetic algorithm.Adopt improved genetic algorithm, to guarantee the diversity of colony, improve convergence of algorithm speed with greedy crossover operator and heuristic inversion mutation operator with concentration control selection strategy.

Individual all the navigation asteroid sequences to be selected that adopt of population are encoded, and coded strings is (As _J0, As _J1As _J2As _Jn), As wherein _JiBe navigation asteroid numbering, a plurality of sequences of Sheng Chenging have promptly constituted initial population at random.And only select hamiltonian circuit in the individuality when individuality price ratio calculated, promptly from As _J0Through asteroid As _J1, As _J2As _J12, the motor-driven then starting point As that gets back to _J0The initial population process is intersected and mutation operation, and finally restrains and optimum solution, and optimum solution is cost performance R _VsMaximum individuality.

Simultaneously, the stock number of navigating asteroidal comprehensive assessment value and consumed is carried out normalization, then the comprehensive assessment value NV after the normalization _iWith consumption stock number NS _jBe respectively:

${\mathrm{NV}}_j=\left\{\begin{array}{cc}1.0& j=0\\ S_j/\underset{m=1}{\overset{n}{\mathrm{\Σ}}}{V}_m& j>0\end{array}\right.$ ${\mathrm{NS}}_i=S_i/\underset{m=1}{\overset{n}{\mathrm{\Σ}}}{S}_m$

Wherein: n is the asteroidal number of navigation to be selected.NV ₀Comprehensive assessment value for initial position, because certain hamiltonian circuit is satisfactory necessary condition of separating is that it comprises initial position, and allow the comprehensive assessment value of this initial position be far longer than other point, can guarantee that generally speaking optimum solution satisfies this condition.

Evaluation function based on preface is adopted in the calculating of population individual fitness, and concrete form is as follows:

F(i)＝α(1-α) ^k/T

Wherein: α, T are constant, and k is that all are individual by ordering from big to small among the group, and i is individual sequence number.

When step 7 was carried out concentration control to population, definition: t was called pattern M for the set of expensive source individuals with same among the group P (t), and the total pattern count of colony is Q (t), pattern M _iIn individual number be | M _i|, the obvious total number of individual in population $N=\underset{i=1}{\overset{Q\left(t\right)}{\mathrm{\Σ}}}\left|M_i\right|,$ Then the concentration in t generation is defined as:

Th(t)＝max(|M ₀|，|M ₁|…M _Q(t))/N

Concentration as individuality leveled off to 1 o'clock, the expression algorithm convergence.

T for the density control valve value defined is

$\mathrm{\θ}\left(t\right)=\frac{{\mathrm{\θ}}_0}{{\mathrm{\θ}}_0+(1-{\mathrm{\θ}}_0)e^{-t/a}}$

Wherein: θ _nBe the concentration threshold values of the 0th generation population (initial population), generally get

A is a constant; When t → ∞, θ (t) → 1 is to guarantee the global convergence of algorithm.

The concentration selection strategy is embodied as:

1. calculate the concentration Th (t) of t for group P (t);

2., make Th (t)≤θ (t) if Th (t)＞θ (t) then deletes the part individuality in this pattern;

3. generate new individuality at random, and put into the position of deleted individuality, make the scale of colony constant.

The roulette mode is adopted in the individual selection that intersects, though the process of this mode is at random, each individual selecteed chance is directly proportional with its adaptive value, so it can improve the average adaptive value of colony, and specifically mode is:

1. summation is asked in all individual adaptive value additions in the colony, rounded and be m;

2. produce an integer between 0 to m at random;

3. first is individual from colony, with its adaptive value and follow-up individual fitness addition, up to add up be equal to or greater than m, then last individuality that adds up is elected to be the individuality that is used to intersect;

4. repeating step 2,3, up to producing the individual number new colony identical with former colony.

Greedy crossover operator can well keep the syntople on limit, can accelerate the optimizing speed of algorithm.

Suppose that two intersection parents individualities selecting through probability are P1 (As ₁₁, As ₁₂..., As _1n), P2 (As ₂₁, As ₂₂..., As _2n), then the process by greedy crossover operator generation filial generation Off1, Off2 is as follows:

1. selected at random asteroid As _JiAs the starting point of intersecting, put into first position of Off1, Off2 respectively;

2. from P1, P2, find out As respectively _JiThe asteroid As on the right _JiR1, As _JiR2, and calculate As respectively _JiTo both apart from d1, d2;

3. compare d1, d2, smaller's corresponding asteroid is added among the Off1, the As among deletion P1, the P2 _Ji, replace As with the asteroid of putting into Off1 at last _JiChange step 2 over to, the asteroid number in P1, P2 is 1, finishes to generate Off1;

4. the left side is made in the right in the step 2, same operation can generate Off2.

The random variation operator is to select the individuality of variation according to the variation probability, and selects two codings in individual coded strings at random, makes between the two coding randomly ordered and generate new individuality.

Because the hamiltonian circuit of choosing is the preceding part in the individuality, and a back part is very little at the probability that enters hamiltonian circuit in the individuality, this has just influenced the diversity of colony, and the fixing random variation operator of an end, adopt an end at random, and the other end is fixed on coding at last, and variation so each time just can all comprise the part of non-hamiltonian circuit, can increase the diversity of colony.

Claims (1)

1, based on the system of selection of reference planet in the autonomous deep-space optical navigation of genetic algorithm, it is characterized in that it comprise the steps: one, according to the present position of detector in celestial body, from asteroidal location database, select the navigation asteroid; It is as follows to select the asteroidal criterion of navigation from asteroidal location database: (1) visible imaging size is less than 12; (2) spend greater than 135 from detector to asteroidal sight line with the sight line angle from the detector to the sun; (3) asteroidal speed is less than 0.7km/s; (4) asteroid and detector distance are less than 0.8 astronomical unit; (5) two asteroidal sight line angles are greater than 5 degree; Two, the navigation asteroid that meets criterion is carried out comprehensive assessment, and according to assessed value from big to small to its ordering; Three, getting assessed value is preceding 20 navigation asteroid, and encodes for these 20 navigation asteroids; Four,, generate initial population according to given genetic algorithm parameter maximum iteration time and population number; Five, preceding 12 navigation asteroids of getting in each population carry out adaptive value calculating; Six, select operator to carry out selection operation according to roulette respectively, carry out interlace operation according to greedy crossover operator, the random variation operator fixing according to an end carries out mutation operation, and is designated as iteration one time; Seven, population is carried out concentration control; Eight, judge whether iterations equals maximum iteration time; The result is for being, then execution in step nine, finish and the output result, this population is optimum population, preceding 12 navigation asteroids of this population put in order as the order of taking pictures with reference to planet and according to this as navigation; The result of step 8 then returns step 4 for not.