CN113022898A - State estimation method for flexible attachment system in weak gravity environment - Google Patents

State estimation method for flexible attachment system in weak gravity environment Download PDF

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CN113022898A
CN113022898A CN202110190484.0A CN202110190484A CN113022898A CN 113022898 A CN113022898 A CN 113022898A CN 202110190484 A CN202110190484 A CN 202110190484A CN 113022898 A CN113022898 A CN 113022898A
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陆晓萱
崔平远
朱圣英
梁子璇
葛丹桐
徐瑞
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Beijing Institute of Technology BIT
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Abstract

The invention relates to a state estimation method for a flexible attachment system in a weak gravity environment, and belongs to the technical field of deep space exploration. The method aims at the multi-node state estimation problem in the flexible attachment process, uses the pixel coordinates of the surface appearance characteristics of a small celestial body as the observed quantity of each node state estimation, utilizes a plurality of node visual measurement information to construct an observation equation of multi-node collaborative navigation, introduces the geometrical configuration information among nodes as the constraint condition of the state estimation of the flexible attachment system, converts the pose estimation problem of the flexible attachment system into the multi-node state estimation problem with the constraint condition, and realizes the state estimation of the flexible attachment system in the weak gravity environment. The task goal of accurate and safe attachment is realized.

Description

State estimation method for flexible attachment system in weak gravity environment
Technical Field
The invention relates to a state estimation method for a flexible attachment system in a weak gravity environment, and belongs to the technical field of deep space exploration.
Background
The small celestial body is weak in gravity and irregular in shape, so that the dynamic environment nearby the small celestial body is complex, the surface appearance is rugged and deficient in prior information, and safe and accurate attachment on the surface is very difficult. The existing small celestial body attachment tasks, such as a meditation king number task of NASA and a falcon number 2 task of JAXA, mainly adopt a rigid body attachment mode, and the rigid body lander has a simple structure and mature technology, but is easy to bounce, topple and the like in a small celestial body weak gravity environment, so that the tasks fail. In order to improve the attachment safety in the weak gravity environment, the small celestial body attachment detection can adopt a flexible attachment scheme, which is beneficial to consuming residual kinetic energy at the attachment tail end and provides a new technical approach for the future small celestial body attachment detection task. In the flexible attachment scheme, local areas of the lander carrying sensors and the controller are called nodes, and the state estimation problem in the flexible attachment process can be abstracted into a state estimation problem of a multi-node system, namely, the state of the lander in the flexible attachment process is approximately represented by the state of the multi-node system. In order to realize state estimation of a multi-node system, the existing method comprises collaborative state estimation methods such as centralized Kalman filtering, distributed information filtering, collaborative graph optimization and the like, but the methods do not consider configuration constraints among nodes in a flexible attachment process. Therefore, it is necessary to combine the characteristics of the multi-node state estimation problem in flexible attachment, consider the constraint conditions such as relative distance and direction between nodes, and study the multi-node state estimation method under constraint, improve the accuracy of multi-node state estimation in the flexible attachment process, and achieve the task goal of accurate and safe attachment.
Disclosure of Invention
The invention aims to solve the problem of low landing precision of the existing flexible attachment system, and provides a state estimation method of the flexible attachment system in a weak gravity environment; aiming at the multi-node state estimation problem in the flexible attachment process of the small celestial body, the method establishes a multi-node cooperative observation equation by taking the pixel coordinates of the surface appearance features of the small celestial body shot by a multi-node navigation camera as observed quantities, introduces the geometric configuration information among nodes as constraint conditions for state estimation of a flexible attachment system, and realizes multi-node state estimation in the flexible attachment process.
The purpose of the invention is realized by the following technical scheme.
A state estimation method of a flexible attachment system in a weak gravitation environment aims at the multi-node state estimation problem in the flexible attachment process, pixel coordinates of surface appearance features of a small celestial body are used as observed quantities of state estimation of all nodes, an observation equation of multi-node collaborative navigation is constructed by using a plurality of node visual measurement information, geometric configuration information among the nodes is introduced as a constraint condition of state estimation of the flexible attachment system, the pose estimation problem of the flexible attachment system is converted into the multi-node state estimation problem with the constraint condition, and the state estimation of the flexible attachment system in the weak gravitation environment is achieved. The task goal of accurate and safe attachment is realized.
The state estimation method of the weak gravity environment flexible attachment system comprises the following steps:
the method comprises the steps of firstly, taking pixel coordinates of the surface feature of the small celestial body in each node camera as observed quantities, and establishing a multi-node state estimation collaborative observation equation through matching with a feature database.
Under the support of a ground station, the small celestial body lander can establish a detailed database of the surface topography of a target celestial body in the process of flying around, so that the three-dimensional position of the navigation landmark under the fixed connection coordinate system of the small celestial body is determined. And selecting the morphological feature points as navigation landmarks in the small celestial body attachment process, wherein the commonly used morphological feature points comprise SIFT feature points, angular points, meteorite crater central points and the like.
Lander body coordinate system o of landerB-xByBzBA reference observation coordinate system. Lander body coordinate system oB-xByBzBOrigin o ofBThe center of mass of the lander is the direction of three inertia main shafts of the lander, and the directions of the three shafts are respectively pointed. Defining the position and the direction of each node camera in a navigation reference coordinate system as
([R1|t1],…,[Rm|tm])(1)
Where m is the number of nodes with cameras, t1…tmFor each node cameraTranslation matrix of optical center relative to lander centroid, R1…RmAnd pointing the rotation matrix relative to the navigation reference coordinate system for each node camera. Each node camera is fixedly installed, that is, in the whole attachment process, the pointing direction of each node camera relative to the navigation reference coordinate system is unchanged, and the pointing directions of each node camera in the navigation reference coordinate system are consistent, that is, the following requirements are met:
R1＝…＝Rm＝R (2)
and R is the direction of each node camera in the navigation reference coordinate system.
During the flexible attachment process, the state of the lander in the attachment point coordinate system is characterized as the position of the node in the attachment point coordinate systemLr1，…，LrmAnd a rotation matrix between the plane formed by the nodes and the attachment planeLRoI.e. by
x＝[(Lr1)T,…,Lri…,(Lrm)T,rotm2eul(LRo)]T (3)
Lri＝Lr+R·ti,i＝1,…,m (4)
Figure RE-GDA0003075605280000021
Wherein rotm2eul (-) is an operator for converting the attitude rotation matrix into Euler angles,Lr is the position vector of the lander centroid in the attachment point coordinate system,LRoposition of any three nodes in the coordinate system of attachment pointLri，LrjAndLrkgiven of ex,ey,ezIs an intermediate variable;
the shape characteristic points which can be observed by the camera carried by the node i are niA camera model adopting pinhole imaging, and any morphological feature point pjNavigating pixel position and characteristic point p of image in any frame of node ijOn the attachmentThe relationship between the three-dimensional positions in the touchdown coordinate system is
Figure RE-GDA0003075605280000031
Wherein the content of the first and second substances,Lbr is a transformation matrix from an attachment point coordinate system to a lander body coordinate system, siIs a scale factor, and is a function of,
Figure RE-GDA0003075605280000032
is a characteristic point pjHomogeneous pixel coordinate vector, feature point p, in node i navigation imagejHomogeneous coordinate position under attachment point coordinate system of node i
Figure RE-GDA0003075605280000033
KiIs an internal reference matrix of the node i camera
Figure RE-GDA0003075605280000034
Wherein f isiFor the focal length of the camera at node i,
Figure RE-GDA0003075605280000035
and
Figure RE-GDA0003075605280000036
for the pixel scaling coefficients of the node i camera,
Figure RE-GDA0003075605280000037
is the optical center coordinates of the node i camera.
From this, the co-observation equation for obtaining the state estimates of m nodes is
Figure RE-GDA0003075605280000038
Figure RE-GDA0003075605280000039
Wherein the content of the first and second substances,LM1，…，LMmfor a set of m node feature observations,
Figure RE-GDA00030756052800000310
a set of feature points observed for m nodes.
Correcting the size and direction of the relative distance between the nodes by using the relative measurement information between the nodes;
the size and direction of the relative distance between the nodes are called as a geometric model;
the size and the direction of the relative distance between the nodes are used as constraint conditions for multi-node state estimation, and for a system with m nodes, the constraint of the node state is expressed as
Figure RE-GDA0003075605280000041
Wherein the content of the first and second substances,Lρ1,…,Lρenis the nominal relative position between nodes, and en is the number of constraints.
In the attachment process, under the influence of environmental disturbance, control error and physical characteristic factors of the flexible body, the position of each node camera in the navigation reference coordinate system changes, so that the actual geometric configuration and the nominal quantity between nodes often have deviation, namely
Figure RE-GDA0003075605280000042
Wherein the content of the first and second substances,
Figure RE-GDA0003075605280000043
the correction needs to be carried out in combination with inter-node measurement information for the relative position between nodes with errors.
The method for correcting the equation (11) by combining the inter-node measurement information comprises the following steps:
the method comprises the following steps: establishing orientation constraints of relative positions between nodes by camera vision measurement:
Figure RE-GDA0003075605280000044
wherein the content of the first and second substances,
Figure RE-GDA0003075605280000045
to correct relative position between nodes
Figure RE-GDA0003075605280000046
The unit direction of (a);
the method 2 comprises the following steps: establishing length constraints of relative positions between nodes by radio measurements:
Figure RE-GDA0003075605280000047
wherein the content of the first and second substances,
Figure RE-GDA0003075605280000048
to correct relative position between nodes
Figure RE-GDA0003075605280000049
The die length of (2);
the method 3 comprises the following steps: establishing a vector constraint of relative positions between nodes by a combination of camera vision measurements and radio measurement measurements:
Figure RE-GDA0003075605280000051
and step three, taking the geometric configuration after error correction in the step two, namely the formula (12), (13) or (14), as a constraint condition of multi-node state estimation, converting the pose estimation problem of the flexible attachment system into a multi-node state estimation problem with the constraint condition, and accurately estimating the state of the node.
The direction R of each node camera in a reference observation coordinate system is known, and an objective function for solving the state solution of each node is constructed by the formula (8)
X＝argmin f(X) (15)
Figure RE-GDA0003075605280000052
Where f (X) is a loss function, X is a variable to be estimated,
X＝[rotm2eul(LbR·R),(Lr1)T,…,(Lrm)T]T (17)
taking the corrected node geometric configuration, namely the formula (12), (13) or (14), as a constraint condition for solving the state of each node in a navigation reference coordinate system, and converting the pose estimation problem of the flexible attachment system into a multi-node state estimation problem with the constraint condition
Figure RE-GDA0003075605280000053
Converting the state estimation problem with constraint conditions into the following minimization problem by a penalty function method
minL(X,λ) (19)
Figure RE-GDA0003075605280000054
Wherein λ is a penalty coefficient. Due to f (X) and
Figure RE-GDA0003075605280000055
all have a convex form, so equation (20) is solved using the levenberg-marquardt method and the like.
And step four, rapidly estimating the state of the flexible attachment system in the weak gravity environment through formulas (19) and (20), and combining a guidance control algorithm to realize a task target of accurate and safe attachment on the surface of the small celestial body.
Advantageous effects
1. The method for estimating the state of the soft attachment system in the weak gravity environment disclosed by the invention is characterized in that a local area of a lander carrying sensor or a controller is taken as a node, the state estimation problem of the lander in the soft attachment process is abstracted into the state estimation problem of a multi-node system, and the state of the lander in the soft attachment process is approximately represented by the state of the multi-node system, so that the state estimation in the soft attachment process is realized.
2. The state estimation method of the soft attachment system in the weak gravitation environment disclosed by the invention has the advantages that the pixel coordinate position of the morphological characteristics in each node camera image is used as an observed quantity, the cooperative observation equation of the multi-node system is established by utilizing the visual observation information of a plurality of nodes, and the multi-node state estimation is carried out by matching with the morphological database, so that the estimation precision is high, and the navigation autonomy is strong.
3. The state estimation method of the soft attachment system in the weak gravitation environment corrects the geometric configuration among the nodes through the mutual measurement information among the nodes, takes the geometric configuration among the nodes as the constraint condition for solving the node state, converts the collaborative pose estimation problem into a state estimation problem with the constraint condition, and further improves the state estimation precision in the attachment process.
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FIG. 1 is a flowchart illustrating the steps of a method for estimating the state of a soft attachment system in a weak gravitational environment according to the present invention;
FIG. 2 is a schematic view of a small celestial flexible attachment scheme;
FIG. 3 is a nominal trajectory of attachment of the small celestial body surface;
FIG. 4 is a diagram of single simulation experiment state estimation errors; wherein, the graph (a) is the node position estimation error; graph (b) is the observation plane attitude estimation error;
FIG. 5 is a state estimation error for 500 Monte Carlo experiments, wherein (a) is a node position estimation error; graph (b) observes the planar attitude estimation error.
Detailed Description
For a better understanding of the objects and advantages of the present invention, reference should be made to the following detailed description taken in conjunction with the accompanying drawings and examples.
Example 1:
in order to verify the feasibility of the method, the simulation calculation of the surface attachment autonomous navigation is performed by taking an attachment task for a certain small celestial body as an example. The schematic diagram of the flexible attachment system is shown in fig. 2, a coordinate system of the landing gear body is defined as a reference observation coordinate system, the number of nodes carrying cameras is 3, and the positions of the nodes in the reference observation coordinate system are respectively
Figure RE-GDA0003075605280000071
And [ I3×3|[0,4,-3]T]. The initial position of the lander centroid is [10, 10, 750 ]]Tm, initial velocity of [ -0.03-0.02-0.2 [)]Tm/s, initial attitude of [1,0,0,0]T. The parameters of the node carrying cameras are consistent, the field angle of the camera is 30 degrees, the focal length of the camera is 717mm, the imaging resolution is 1024 multiplied by 1024, the feature extraction precision is 0.5 pixel, and the optical observation step length is 300 s. And extracting a feature central point in the field of view as a feature point in the attachment process, wherein the feature position is randomly generated by the simulation platform. Establishing an attachment nominal track of the lander under an attachment point coordinate system, enabling the lander to reach a position 30m right above the attachment point after the lander passes 1800s from an initial position, generating the nominal attachment track by an Apollo guidance law, and enabling the lander to move to an attitude control lawBzBThe axis is always directed perpendicularly to the attachment plane, the attachment nominal trajectory being shown in fig. 3. By applying the configuration-constrained multi-node collaborative visual navigation method disclosed by the invention, the state estimation precision of a single attachment experiment is shown in fig. 4a and b, and the state estimation error in the attachment process is obviously reduced in comparison with a method without configuration constraint in the figure, so that the requirement of high-precision attachment autonomous navigation is met.
As shown in fig. 1, the method for estimating the state of the soft attachment system in the weak gravitational environment disclosed in this embodiment includes the following specific implementation steps:
the method comprises the steps of firstly, taking pixel coordinates of the surface feature of the small celestial body in each node camera as observed quantities, and establishing a multi-node state estimation collaborative observation equation through matching with a feature database.
During a flexible attachment process, the state of the lander in the attachment point coordinate system may be characterized as the node sitting at the attachment pointPosition in the frameLr1，Lr2，Lr3Rotation matrix of plane formed by nodes between attachment planesLRoI.e. by
x＝[(Lr1)T,(Lr2)T,(Lr3)T,rotm2eul(LRo)]T (21)
Wherein the content of the first and second substances,LRogiven by the position of the three nodes in the attachment point coordinate system
Lri＝Lr+R·ti,i＝1,2,3 (22)
Figure RE-GDA0003075605280000072
The internal reference matrix of each node camera is
Figure RE-GDA0003075605280000073
From this, the co-observation equation that can get the state estimation of m nodes is
Figure RE-GDA0003075605280000081
Wherein the content of the first and second substances,LM1，LM2，LM3for a set of 3 node feature observations,
Figure RE-GDA0003075605280000082
a set of feature points observed for 3 nodes.
And step two, defining the size and the direction of the relative distance between the nodes as the geometric configuration between the nodes, and correcting the geometric configuration between the nodes by using the relative measurement information between the nodes.
In the attachment process, under the influence of factors such as environmental disturbance, control error and physical characteristics of the flexible body, the position of each node camera in the navigation reference coordinate system may change, the position error amplitude of each node camera in the navigation reference coordinate system is 5% of the nominal length, and the error distribution is gaussian distribution, that is, the position error amplitude is 5% of the nominal length
Figure RE-GDA0003075605280000083
In the present embodiment, the inter-node configuration is corrected by the vision measurement information of the homonymous feature points between the cameras. Taking the simulation of the last observation time as an example, s can be observed by i and j between camera nodes at any timeijThe constraint relation between the pixel coordinates of the characteristic points in the two node images can be expressed as
Figure RE-GDA0003075605280000084
Wherein the content of the first and second substances,
Figure RE-GDA0003075605280000085
is s isijNormalized homogeneous pixel coordinates of individual feature points in camera node j,
Figure RE-GDA0003075605280000086
is s isijNormalized homogeneous pixel coordinates of individual feature points in camera node i, EijIs an essential matrix between two images
Figure RE-GDA0003075605280000087
Wherein R isijIs a rotation matrix between the two nodal camera orientations,
Figure RE-GDA0003075605280000088
representing the translation transformation relationship between two nodes,
Figure RE-GDA0003075605280000089
is an antisymmetric matrix, each variable satisfies
Rij＝I3×3 (29)
Figure RE-GDA0003075605280000091
Figure RE-GDA0003075605280000092
From the pixel coordinates of the same-name point, the essential matrix E can be solved by equation (27)ijTo the essence matrix EijPerforming singular value decomposition
Figure RE-GDA0003075605280000093
Where det (U) > 0 and det (V) > 0. The translation transformation relation between two nodes can be obtained as
Figure RE-GDA0003075605280000094
Where ω is a scale factor, [ u ]13,u23,u33]TAre elements in U. Since the scale factor is unknown, only the direction of the relative position between nodes can be obtained
Figure RE-GDA0003075605280000095
From coordinate transformation
Figure RE-GDA0003075605280000096
Thus, a geometric constraint between 3 camera nodes can be established as
Figure RE-GDA0003075605280000097
And step three, taking the geometric configuration subjected to error correction in the step two as a constraint condition of multi-node state estimation, converting the pose estimation problem of the flexible attachment system into a multi-node state estimation problem with the constraint condition, and accurately estimating the state of the node.
Geometric configuration constraint among nodes is used as a constraint condition for state solution of each node in a navigation reference coordinate system, and the flexible attachment system pose estimation problem can be converted into a multi-node state estimation problem with the constraint condition
Figure RE-GDA0003075605280000101
Wherein the content of the first and second substances,
Figure RE-GDA0003075605280000102
(X) is a loss function, X is a variable to be estimated,
X＝[rotm2eul(LbR·R),(Lr1)T,(Lr2)T,(Lr3)T]T (38)
converting the state estimation problem with constraint conditions into the following minimization problem by a penalty function method
Figure RE-GDA0003075605280000103
Where λ is the lagrange multiplier. Due to f (X) and
Figure RE-GDA0003075605280000104
since both are convex, equation (39) is solved by the levenberg-marquardt method.
The position error at the last observation time is deltaLr～N(diag[3m,3m,3m]0), attitude error rotm2eul (Δ)LbR)～N(diag[3°,3°,3°]0), compared with the unconstrained collaborative visual navigation method, the average navigation error result of 500 experiments is shown in fig. 5a and b, the average error of the position estimation of the method disclosed by the invention is 0.0819m, the average error of the position estimation of the unconstrained state estimation method is 0.2000m, the position estimation precision is improved by 59.05%, the average error of the observation plane attitude estimation of the method disclosed by the invention is 0.0195 rad, the average error of the observation plane attitude estimation of the unconstrained state estimation method is 0.0038rad, and the attitude estimation precision is improved by 80.49%.
The above detailed description is intended to illustrate the objects, aspects and advantages of the present invention, and it should be understood that the above detailed description is only exemplary of the present invention and is not intended to limit the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (1)

1. The state estimation method of the weak gravity environment flexible attachment system is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps that firstly, pixel coordinates of the surface appearance features of the small celestial body in each node camera are used as observed quantities, and a multi-node state estimation collaborative observation equation is established through matching with a appearance database;
under the support of a ground station, the small celestial body lander can establish a detailed database of the surface topography of a target celestial body in the process of flying around, so that the three-dimensional position of a navigation landmark under a fixed connection coordinate system of the small celestial body is determined; selecting morphology feature points as navigation landmarks in the small celestial body attachment process;
lander body coordinate system o of landerB-xByBzBObserving a coordinate system as a reference; lander body coordinate system oB-xByBzBOrigin o ofBTo landThe mass center of the landing device points to the directions of three inertia main shafts of the landing device in the three-axis direction respectively; defining the position and the direction of each node camera in a navigation reference coordinate system as
([R1|t1],…,[Rm|tm]) (1)
Where m is the number of nodes with cameras, t1…tmFor each node camera optical center to the landing gear centroid translation matrix, R1…RmPointing a rotation matrix corresponding to the navigation reference coordinate system for each node camera; each node camera is fixedly installed, that is, in the whole attachment process, the pointing direction of each node camera relative to the navigation reference coordinate system is unchanged, and the pointing directions of each node camera in the navigation reference coordinate system are consistent, that is, the following requirements are met:
R1＝...＝Rm＝R (2)
wherein R is the direction of each node camera in the navigation reference coordinate system;
during the flexible attachment process, the state of the lander in the attachment point coordinate system is characterized as the position of the node in the attachment point coordinate systemLr1，...，LrmAnd a rotation matrix between the plane formed by the nodes and the attachment planeLRoI.e. by
x＝[(Lr1)T,…,Lri…,(Lrm)T,rotm2eul(LRo)]T (3)
Lri＝Lr+R·ti,i＝1,…,m (4)
Figure FDA0002943941350000011
Wherein rotm2eul (-) is an operator for converting the attitude rotation matrix into Euler angles,Lr is the position vector of the lander centroid in the attachment point coordinate system,LRoposition of any three nodes in the coordinate system of attachment pointLri，LrjAndLrkgiven of ex,ey,ezIs an intermediate variable;
the shape characteristic points which can be observed by the camera carried by the node i are niA camera model adopting pinhole imaging, and any morphological feature point pjNavigating pixel position and characteristic point p of image in any frame of node ijThe relationship between the three-dimensional positions in the coordinate system of the attachment points is
Figure FDA0002943941350000021
Wherein the content of the first and second substances,Lbr is a transformation matrix from an attachment point coordinate system to a lander body coordinate system, siIs a scale factor, and is a function of,
Figure FDA0002943941350000022
is a characteristic point pjHomogeneous pixel coordinate vector, feature point p, in node i navigation imagejHomogeneous coordinate position under attachment point coordinate system of node i
Figure FDA0002943941350000023
KiIs an internal reference matrix of the node i camera
Figure FDA0002943941350000024
Wherein f isiFor the focal length of the camera at node i,
Figure FDA0002943941350000025
and
Figure FDA0002943941350000026
for the pixel scaling coefficients of the node i camera,
Figure FDA0002943941350000027
the coordinates of the optical center of the camera of the node i are taken as the coordinates of the optical center of the camera of the node i;
from this, the co-observation equation for obtaining the state estimates of m nodes is
Figure FDA0002943941350000028
Figure FDA0002943941350000029
Wherein the content of the first and second substances,LM1，…，LMmfor a set of m node feature observations,
Figure FDA00029439413500000210
a set of feature points observed for m nodes;
correcting the size and direction of the relative distance between the nodes by using the relative measurement information between the nodes;
the size and direction of the relative distance between the nodes are called as a geometric model;
the size and the direction of the relative distance between the nodes are used as constraint conditions for multi-node state estimation, and for a system with m nodes, the constraint of the node state is expressed as
Figure FDA0002943941350000031
Wherein the content of the first and second substances,Lρ1,…,Lρenis the nominal relative position between nodes, en is the number of constraints;
in the attachment process, under the influence of environmental disturbance, control error and physical characteristic factors of the flexible body, the position of each node camera in the navigation reference coordinate system changes, so that the actual geometric configuration and the nominal quantity between nodes often have deviation, namely
Figure FDA0002943941350000032
Wherein the content of the first and second substances,
Figure FDA0002943941350000033
correcting the relative position between nodes with errors by combining inter-node measurement information;
the method for correcting the equation (11) by combining the inter-node measurement information comprises the following steps:
the method comprises the following steps: establishing orientation constraints of relative positions between nodes by camera vision measurement:
Figure FDA0002943941350000034
wherein the content of the first and second substances,
Figure FDA0002943941350000035
to correct relative position between nodes
Figure FDA0002943941350000036
The unit direction of (a);
the method 2 comprises the following steps: establishing length constraints of relative positions between nodes by radio measurements:
Figure FDA0002943941350000037
wherein the content of the first and second substances,
Figure FDA0002943941350000038
to correct relative position between nodes
Figure FDA0002943941350000039
The die length of (2);
the method 3 comprises the following steps: establishing a vector constraint of relative positions between nodes by a combination of camera vision measurements and radio measurement measurements:
Figure FDA00029439413500000310
step three, taking the geometric configuration after error correction in the step two, namely the formula (12), (13) or (14), as a constraint condition of multi-node state estimation, converting the pose estimation problem of the flexible attachment system into a multi-node state estimation problem with the constraint condition, and accurately estimating the state of the node;
the direction R of each node camera in a reference observation coordinate system is known, and an objective function for solving the state solution of each node is constructed by the formula (8)
X＝argminf(X) (15)
Figure FDA0002943941350000041
Where f (X) is a loss function, X is a variable to be estimated,
X＝[rotm2eul(LbR·R),(Lr1)T,…,(Lrm)T]T (17)
taking the corrected node geometric configuration, namely the formula (12), (13) or (14), as a constraint condition for solving the state of each node in a navigation reference coordinate system, and converting the pose estimation problem of the flexible attachment system into a multi-node state estimation problem with the constraint condition
Figure FDA0002943941350000042
Converting the state estimation problem with constraint conditions into the following minimization problem by a penalty function method
minL(X,λ) (19)
Figure FDA0002943941350000043
Wherein, λ is a penalty coefficient; due to f (X) and
Figure FDA0002943941350000044
all have convex forms, so the formula (20) is solved by adopting a Levenberg-Marquardt method and the like;
and step four, rapidly estimating the state of the flexible attachment system in the weak gravity environment through formulas (19) and (20), and combining a guidance control algorithm to realize a task target of accurate and safe attachment on the surface of the small celestial body.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113483748A (en) * 2021-07-05 2021-10-08 北京理工大学 Method for estimating relative pose of flexibly-attached multi-node small celestial body
CN114111782A (en) * 2021-10-11 2022-03-01 北京理工大学 Flexible lander state characterization and estimation method

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103241392A (en) * 2013-04-25 2013-08-14 上海卫星工程研究所 Attached device of deep-space week-gravitation celestial body and method for constructing attached device
US20140136029A1 (en) * 2011-07-12 2014-05-15 Tetsuya Nagase Flight control device, spacecraft, and reference trajectory correcting method
CN109269511A (en) * 2018-11-06 2019-01-25 北京理工大学 The Curve Matching vision navigation method that circumstances not known lower planet lands
CN109506662A (en) * 2018-10-30 2019-03-22 北京控制工程研究所 A kind of small feature loss landing Initial Alignment Method, its Relative Navigation benchmark determine method and device
CN110702122A (en) * 2019-10-22 2020-01-17 北京理工大学 Comprehensive optimization method for autonomous optical navigation characteristics of extraterrestrial celestial body landing
CN111985050A (en) * 2020-08-12 2020-11-24 北京理工大学 Method for generating safe launching window of weak-gravity small celestial body surface lander

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140136029A1 (en) * 2011-07-12 2014-05-15 Tetsuya Nagase Flight control device, spacecraft, and reference trajectory correcting method
CN103241392A (en) * 2013-04-25 2013-08-14 上海卫星工程研究所 Attached device of deep-space week-gravitation celestial body and method for constructing attached device
CN109506662A (en) * 2018-10-30 2019-03-22 北京控制工程研究所 A kind of small feature loss landing Initial Alignment Method, its Relative Navigation benchmark determine method and device
CN109269511A (en) * 2018-11-06 2019-01-25 北京理工大学 The Curve Matching vision navigation method that circumstances not known lower planet lands
CN110702122A (en) * 2019-10-22 2020-01-17 北京理工大学 Comprehensive optimization method for autonomous optical navigation characteristics of extraterrestrial celestial body landing
CN111985050A (en) * 2020-08-12 2020-11-24 北京理工大学 Method for generating safe launching window of weak-gravity small celestial body surface lander

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
崔平远等: "小天体自主附着技术研究进展", 《宇航学报》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113483748A (en) * 2021-07-05 2021-10-08 北京理工大学 Method for estimating relative pose of flexibly-attached multi-node small celestial body
CN113483748B (en) * 2021-07-05 2023-12-12 北京理工大学 Multi-node relative pose estimation method for flexible attachment of small celestial body
CN114111782A (en) * 2021-10-11 2022-03-01 北京理工大学 Flexible lander state characterization and estimation method
CN114111782B (en) * 2021-10-11 2023-11-10 北京理工大学 Flexible lander state characterization and estimation method

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