CN115290074A - High-risk terrain intelligent identification method and system by utilizing thermal inertia - Google Patents

High-risk terrain intelligent identification method and system by utilizing thermal inertia Download PDF

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CN115290074A
CN115290074A CN202210761819.4A CN202210761819A CN115290074A CN 115290074 A CN115290074 A CN 115290074A CN 202210761819 A CN202210761819 A CN 202210761819A CN 115290074 A CN115290074 A CN 115290074A
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inertia
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高锡珍
汤亮
黄煌
谢心如
刘昊
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Beijing Institute of Control Engineering
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Abstract

The invention discloses a high-risk terrain intelligent identification method and system by utilizing thermal inertia, wherein the method comprises the following steps: obtaining the heat inertia of the star catalogue according to the temperature of the star catalogue; and establishing a Gaussian mixture model by using the heat inertia of the star table for identifying the star table traps. The invention can predict the passability of the terrain and ensure the accurate and safe detection of the detector.

Description

High-risk terrain intelligent identification method and system by utilizing thermal inertia
Technical Field
The invention belongs to the technical field of extraterrestrial exploration, and particularly relates to a high-risk terrain intelligent identification method and system by utilizing thermal inertia.
Background
Extraterrestrial exploration gradually extends from the nearest moon to more and more distant planets such as mars and asteroids, and the exploration mode gradually progresses from glancing and flying, to landing inspection and sampling return.
In the process of the extraterrestrial patrol, a plurality of risks caused by star surface dangerous terrain environments are encountered, and the risks can be divided into geometric risks and non-geometric risks, wherein the geometric risks refer to obstacles such as large stones, meteorite pits and steep slopes, and the non-geometric risks refer to soft soil, thin weathered layers and the like. In order to improve mobility and safely travel to a target area, the rover should be able to autonomously perceive the star surface environment. For the geometrical risk of the star catalogue, the geometrical risk can be identified through vision and laser radar, so that the patrol device can avoid the obstacles and improve the mobility. However, for the risk of the non-geometric star surface, the traditional perception method only can see the appearance and the geometric shape of the surface, and the looseness and hardness of the terrain are difficult to predict, so that the wheel is easy to slip and sink, the maneuverability is inhibited, and even the vehicle can be permanently trapped, such as the wheel with a 'courage' is finally trapped in soft mars soil and cannot escape.
Disclosure of Invention
The technical problem solved by the invention is as follows: the defects in the prior art are overcome, the high-risk terrain intelligent identification method and the high-risk terrain intelligent identification system using thermal inertia are provided, the trafficability of the terrain can be predicted, and the accurate and safe detection of the detector is guaranteed.
The purpose of the invention is realized by the following technical scheme: a high-risk terrain intelligent identification method utilizing thermal inertia comprises the following steps: obtaining the heat inertia of the star catalogue according to the temperature of the star catalogue; and establishing a Gaussian mixture model by using the heat inertia of the star catalogue for identifying the star catalogue traps.
In the above intelligent high-risk terrain identification method using thermal inertia, the satellite meter thermal inertia is obtained by the following formula:
Figure BDA0003721257230000021
wherein A is star albedo, epsilon is radiation coefficient, sigma is boltzmann constant, and R is sw For solar short-wave radiation, R lw For atmospheric long-wave radiation, T s The star surface temperature is shown as I, the thermal inertia of the star surface is shown as T, z' is the ratio of the depth to the thermal penetration depth, and T (z, T) is the star surface temperature when the time T is the ratio of the depth to the thermal penetration depth z.
The high risk terrain intelligence using thermal inertiaIn the identification method, the ratio z' of the depth to the thermal penetration depth is:
Figure BDA0003721257230000022
where z is the depth and δ is the thermal penetration depth.
In the above intelligent high-risk terrain identification method using thermal inertia, the thermal penetration depth δ is obtained by the following formula:
Figure BDA0003721257230000023
wherein k is thermal conductivity, ρ is density, c is specific heat capacity, and P is day-night cycle period.
In the above intelligent high-risk terrain identification method using thermal inertia, the gaussian mixture model is obtained by the following formula:
Figure BDA0003721257230000024
wherein the content of the first and second substances,
Figure BDA0003721257230000025
the probability of the heat inertia of the star surface under the condition of an intermediate parameter theta, K is the number of the models of the Gaussian mixture model, and alpha n To measure the probability that the data belongs to the nth submodel, phi (I | theta) n ) Is the Gaussian distribution density function of the nth submodel, theta is an intermediate parameter, theta n And the final estimated value of the expectation and the variance of the nth submodel, wherein I is the star surface thermal inertia, and n is the numerical mark of the submodel.
In the high-risk terrain intelligent identification method by utilizing thermal inertia, intermediate parameters
Figure BDA0003721257230000026
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003721257230000027
representing the desired final estimate of the nth sub-model,
Figure BDA0003721257230000028
represents the covariance final estimate for the nth submodel,
Figure BDA0003721257230000029
representing the final estimate of the probability that the nth sub-model occurs in the gaussian mixture model.
A high risk terrain intelligent recognition system utilizing thermal inertia, comprising: the first module is used for obtaining the heat inertia of the star catalogue according to the temperature of the star catalogue; and the second module is used for establishing a Gaussian mixture model by using the heat inertia of the star catalogue and identifying the star catalogue trap.
In the above high-risk terrain intelligent recognition system using thermal inertia, the satellite surface thermal inertia is obtained by the following formula:
Figure BDA0003721257230000031
wherein A is star albedo, epsilon is radiation coefficient, sigma is boltzmann constant, and R is sw For solar short-wave radiation, R lw For atmospheric long-wave radiation, T s The star surface temperature is shown as I, the thermal inertia of the star surface is shown as T, z' is the ratio of the depth to the thermal penetration depth, and T (z, T) is the star surface temperature when the time T is the ratio of the depth to the thermal penetration depth z.
In the above high-risk terrain intelligent identification system using thermal inertia, a ratio z' of a depth to a thermal penetration depth is:
Figure BDA0003721257230000032
where z is the depth and δ is the thermal penetration depth.
In the above high-risk terrain intelligent identification system using thermal inertia, the thermal penetration depth δ is obtained by the following formula:
Figure BDA0003721257230000033
wherein k is thermal conductivity, ρ is density, c is specific heat capacity, and P is day-night cycle period.
Compared with the prior art, the invention has the following beneficial effects:
(1) According to the invention, the heat inertia of the star surface is calculated based on the satellite-borne and orbit instruments, the Gaussian mixture model is established based on the heat inertia estimated value, and the parameters of the Gaussian mixture model are estimated by using the expectation maximization algorithm, so that the star surface trap recognition model is obtained, the trafficability of the terrain is predicted, and the accurate and safe detection of the detector is ensured.
(2) According to the invention, the star catalogue and the physical attributes under the star catalogue are perceived and understood by establishing the Gaussian mixture model, so that the terrain trafficability is predicted, and the self-learning capability is provided.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a schematic diagram of a result of a trap recognition gaussian mixture model according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
The embodiment provides a high-risk terrain intelligent identification method by using thermal inertia, which comprises the following steps:
obtaining the heat inertia of the star surface according to the temperature of the star surface;
and establishing a Gaussian mixture model by using the heat inertia of the star catalogue for identifying the star catalogue traps.
Step 1: satellite surface thermal inertia calculation
The thermal inertia determines the ability of the material to resist temperature changes under periodic light. Thermal inertia is defined as
Figure BDA0003721257230000041
In the formula, k represents thermal conductivity, ρ represents density, c represents specific heat capacity, and the unit of thermal inertia is Jm -2 K -1 s -1/2 。
To calculate the star surface thermal inertia, a one-dimensional heat transfer equation is defined as follows:
Figure BDA0003721257230000042
in the formula, T (z, T) represents the star surface temperature at time T and depth z, and z =0 represents the star surface temperature.
The temperature of the star surface is determined by the equilibrium between the upward and downward flows from the sun, atmosphere and ground. I.e. the following equation is satisfied:
Figure BDA0003721257230000043
wherein A represents star albedo, ε represents emissivity, σ represents Boltzmann constant, P represents circadian cycle, and R represents sw Representing solar short-wave radiation, R lw Representing atmospheric long-wave radiation, T s The temperature of the star catalogue is shown,
Figure BDA0003721257230000051
representing the star temperature gradient values.
Let z' = z/delta, delta denote the depth of thermal penetration, satisfy
Figure BDA0003721257230000052
Then the formula (2) can be rewritten as
Figure BDA0003721257230000053
The combination of formula (3) and formula (4) gives:
Figure BDA0003721257230000054
therefore, by measuring the temperature of the star catalogue according to the formula (4), the thermal inertia I of the star catalogue can be calculated, and a Gaussian mixture model is established for identifying the star catalogue traps.
And 2, step: and establishing a Gaussian mixture model by using the star surface thermal inertia I for identifying star surface traps.
Formula (1) indicates that thermal inertia is affected by the particle size, density and degree of consolidation of the star topographic material. Generally, higher levels of consolidation and greater density and particle size of the star terrain have a higher thermal inertia. For the off-ground patrol detection task, the larger the particle, the greater the soil strength. Also, denser sand bodies pass more easily than fine, loose sand bodies. Furthermore, as the particles stick together, the terrain is more supportive and thus easier to pass through. Thus, there is a strong positive correlation between traversability and thermal inertia, and for a star surface trap, an increase in the depth of the ground sand layer results in a decrease in both thermal inertia and traversability.
The mixture model is a probability model that can be used to represent a population distribution having K sub-distributions, in other words, the mixture model represents a probability distribution of the observed data in the population, which is a mixture distribution composed of K sub-distributions. Since whether the patrollers can traverse the corresponding data is independently distributed, i.e., satisfies a gaussian distribution, a gaussian mixture model is defined as follows:
Figure BDA0003721257230000055
in the formula (I), the compound is shown in the specification,
Figure BDA0003721257230000056
Figure BDA0003721257230000057
representing the desired final estimate of the nth sub-model,
Figure BDA0003721257230000058
represents the covariance final estimate of the nth submodel,
Figure BDA0003721257230000059
representing the final estimate of the probability that the nth sub-model occurs in the gaussian mixture model.
Figure BDA0003721257230000061
Is the probability of the thermal inertia of the star surface under the condition of an intermediate parameter theta, K is the model number of the Gaussian mixture model, and alpha n To measure the probability that the data belongs to the nth submodel, phi (I | theta) n ) Is the Gaussian distribution density function of the nth submodel, theta is an intermediate parameter, theta n And the final estimated value of the expectation and the variance of the nth submodel, wherein I is the star surface thermal inertia, and n is the numerical mark of the submodel. It is to be understood that the gaussian mixture model comprises n submodels.
The parameter θ in the gaussian mixture model can be obtained by learning with an expectation maximization algorithm, which is specifically as follows:
(1) Initializing parameters;
(2) Calculating each data I according to the current parameters j Probability gamma from submodel n jn
Figure BDA0003721257230000062
In the formula, phi (I) j |θ n ) Representing each thermal inertia data I j Probability of belonging to model n.
(3) Calculating model parameter mu of new iteration n ，σ n And alpha n Wherein, mu n ，σ n And alpha n Respectively representing the expected iteration estimation value of each sub-model, the covariance iteration estimation value difference of each sub-model and the probability iteration estimation value occurring in the mixed model.
Figure BDA0003721257230000063
Repeating the step (2) and the step (3) until convergence, and finally obtaining parameters of the Gaussian mixture model
Figure BDA0003721257230000064
Is estimated. Therefore, based on the established Gaussian mixture model, whether the current area is the trap probability or not can be obtained by inputting the observed value of the thermal inertia of the target area, and therefore the passability is judged.
The present embodiment also provides a high-risk terrain intelligent recognition system using thermal inertia, the system including: the first module is used for obtaining the heat inertia of the star catalogue according to the temperature of the star catalogue; and the second module is used for establishing a Gaussian mixture model by using the heat inertia of the star catalogue and identifying the star catalogue trap.
The result of the trap recognition model established in this embodiment is shown in fig. 1, and the simulation result shows that the recognition accuracy is 90.72%.
The method is characterized in that the heat inertia of the star surface is calculated based on a satellite-borne instrument and a rail instrument, a Gaussian mixture model is established based on the heat inertia estimated value, and the parameters of the Gaussian mixture model are estimated by using an expectation maximization algorithm, so that a star surface trap recognition model is obtained, the trafficability of the terrain is predicted, and the accurate and safe detection of a detector is ensured; according to the invention, the star catalogue and the physical attributes under the star catalogue are perceived and understood by analyzing the thermal inertia characteristics of the star catalogue, so that the terrain trafficability is predicted, and the self-learning capability is provided.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to limit the present invention, and those skilled in the art can make variations and modifications of the present invention without departing from the spirit and scope of the present invention by using the methods and technical contents disclosed above.

Claims (10)

1. An intelligent high-risk terrain identification method using thermal inertia is characterized by comprising the following steps:
obtaining the heat inertia of the star surface according to the temperature of the star surface;
and establishing a Gaussian mixture model by using the heat inertia of the star table for identifying the star table traps.
2. The intelligent high-risk terrain recognition method using thermal inertia of claim 1, wherein: the heat inertia of the star meter is obtained by the following formula:
Figure FDA0003721257220000011
wherein A is star albedo, epsilon is radiation coefficient, sigma is Boltzmann constant, and R is sw As solar short-wave radiation, R lw For atmospheric long-wave radiation, T s The star surface temperature is shown as I, the thermal inertia of the star surface is shown as T, z' is the ratio of the depth to the thermal penetration depth, and T (z, T) is the star surface temperature when the time T is the ratio of the depth to the thermal penetration depth z.
3. The intelligent high-risk terrain recognition method using thermal inertia of claim 2, wherein: the ratio z' of depth to thermal skin depth is:
Figure FDA0003721257220000012
where z is the depth and δ is the thermal penetration depth.
4. The intelligent high-risk terrain recognition method using thermal inertia of claim 3, wherein: the thermal penetration depth δ is obtained by the following formula:
Figure FDA0003721257220000013
wherein k is thermal conductivity, ρ is density, c is specific heat capacity, and P is day-night cycle period.
5. The intelligent recognition method for high-risk terrain using thermal inertia of claim 1, wherein: the Gaussian mixture model is obtained by the following formula:
Figure FDA0003721257220000014
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003721257220000015
the probability of the heat inertia of the star surface under the condition of an intermediate parameter theta, K is the number of the models of the Gaussian mixture model, and alpha n To measure the probability that the data belongs to the nth submodel, phi (I | theta) n ) Is the Gaussian distribution density function of the nth submodel, theta is an intermediate parameter, theta n And the final estimated value of the expectation and the variance of the nth submodel, wherein I is the star surface thermal inertia, and n is the numerical mark of the submodel.
6. The intelligent high-risk terrain recognition method using thermal inertia of claim 5, wherein: intermediate parameter
Figure FDA0003721257220000021
Wherein the content of the first and second substances,
Figure FDA0003721257220000022
representing the desired final estimate of the nth sub-model,
Figure FDA0003721257220000023
denotes the n-thThe final estimate of the covariance of the sub-models,
Figure FDA0003721257220000024
representing the final estimate of the probability that the nth sub-model occurs in the gaussian mixture model.
7. An intelligent high-risk terrain recognition system using thermal inertia, comprising:
the first module is used for obtaining the heat inertia of the star catalogue according to the temperature of the star catalogue;
and the second module is used for establishing a Gaussian mixture model by using the heat inertia of the star catalogue and identifying the star catalogue trap.
8. The intelligent high-risk terrain recognition system using thermal inertia of claim 7, wherein: the heat inertia of the star meter is obtained by the following formula:
Figure FDA0003721257220000025
wherein A is star albedo, epsilon is radiation coefficient, sigma is boltzmann constant, and R is sw For solar short-wave radiation, R lw For atmospheric long-wave radiation, T s The star surface temperature is shown as I, the thermal inertia of the star surface is shown as T, z' is the ratio of the depth to the thermal penetration depth, and T (z, T) is the star surface temperature when the time T is the ratio of the depth to the thermal penetration depth z.
9. The intelligent high-risk terrain recognition system using thermal inertia of claim 8, wherein: the ratio z' of depth to thermal skin depth is:
Figure FDA0003721257220000026
where z is the depth and δ is the thermal penetration depth.
10. The intelligent high-risk terrain recognition system using thermal inertia of claim 9, wherein: the thermal penetration depth δ is obtained by the following formula:
Figure FDA0003721257220000027
wherein k is thermal conductivity, ρ is density, c is specific heat capacity, and P is day-night cycle period.
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WO2015101061A1 (en) * 2013-12-31 2015-07-09 华中科技大学 Infrared imaging detection and positioning method for underground tubular facility in plane terrain
CN116227137A (en) * 2022-12-21 2023-06-06 北京控制工程研究所 Extraterrestrial celestial body surface thermal inertia inversion and verification method

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