CN112215417A - Optimal distribution method for landing points of flexible connection multi-node small celestial body detector - Google Patents
Optimal distribution method for landing points of flexible connection multi-node small celestial body detector Download PDFInfo
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Abstract
The invention discloses an optimal distribution method for landing points of flexibly-connected multi-node small celestial body detectors, and belongs to the technical field of aerospace. The implementation method of the invention comprises the following steps: and acquiring scene information which comprises the positions of the detector nodes before landing, the position constraint among the detector nodes and the positions and values of a plurality of selectable landing points of each detector node. Establishing an abstract model for the probe node information and the selectable landing point information, describing the optimal distribution problem of the flexible connection multi-node small celestial body probe landing points with position constraint as a constrained bipartite graph optimal matching problem, and generating a landing point optimal distribution scheme meeting the position constraint by adopting a bipartite graph optimal matching method added with constraint check. The optimal distribution method of the landing points of the flexibly-connected multi-node small celestial body detector can quickly generate an optimal landing position distribution scheme meeting position constraints among nodes on the basis of a given detector node set and an optional landing point set, and improves the landing safety and value benefits of the multi-node detector.
Description
Technical Field
The invention relates to an optimal distribution method for landing points of flexibly-connected multi-node small celestial body detectors, and belongs to the technical field of aerospace.
Background
In the small celestial body detection task, due to the factors of small gravity of the small celestial body, complex surface condition and the like, a single detector has difficulty in landing and is easy to bounce and break away from the gravity constraint of the small celestial body to miss the landing opportunity. To solve this problem, some researchers have proposed the concept of flexibly connecting multi-node detectors. The flexible connection multi-node detector comprises a plurality of nodes, each node is a small detector, the nodes are connected through flexible ropes, and the nodes are mutually pulled through the flexible ropes, so that the bounce of the detector is restrained, and the detector can land more easily. Before the multi-node detector lands, the environment of the surface of the planet needs to be observed, obstacle areas are avoided, a flat point suitable for landing is selected for landing, the scientific value income of the landing point also needs to be analyzed, and the landing is performed at a point with high scientific value income as much as possible. Because the detector nodes are flexibly connected, the distance between the nodes cannot be too far, and if the distance between the nodes exceeds the length of the flexible rope, the physical structure of the detector can be damaged. It follows that finding a suitable landing scenario among many alternative landing sites for multiple nodes is a complex problem.
The flexible connection multi-node small celestial body detector is a novel detector provided for detecting small celestial bodies, and for the detector, no existing method for distributing landing points of the detector exists, but the method in other fields can be used for solving the problem of distributing the landing points. The problem of the distribution of landing points of the flexibly-connected multi-node small celestial body detector is similar to the problem of the distribution of target points observed by multiple satellites in a ground, and the optimal matching problem of selecting one target point for one aircraft can be abstracted. The multi-satellite target point distribution problem adopts methods such as a meta-heuristic algorithm like a genetic algorithm, a distribution algorithm based on a bidding mechanism and the like. The modeling process of the meta-heuristic algorithm is complex, taking a genetic algorithm as an example, the distribution scheme of the ground target selected by the aircraft needs to be abstracted into models such as genes, chromosomes, individuals, populations and the like, the description capability of the constraint relation is not strong, but the optimal distribution of the flexible connection multi-node small celestial body detector landing points needs to describe and process the position constraint between the nodes. The metaheuristic algorithm has high time complexity and space complexity, and the problem of long calculation time and large storage space occupied by program operation can be caused by the large number of nodes in the problem of probe landing point distribution. The meta-heuristic algorithm has certain randomness, and the randomness can reduce the controllability and reliability of the landing of the detector and is not suitable for deep space detection tasks.
The modeling process of the allocation algorithm based on the bidding mechanism is simple and intuitive, and the solving speed is high. But also lacks a description of constraints between multiple tenderers or between multiple bidders, and therefore it is difficult to describe location constraints between probe nodes. Moreover, the bidding algorithm has no backtracking mechanism, so that the problem of the original solution can not be found, and the method is not suitable for optimal distribution of the landing points of the flexibly-connected multi-node small celestial body detector.
Disclosure of Invention
Aiming at the problem of selecting the landing points of the flexibly-connected multi-node small celestial body detector, the invention discloses an optimal distribution method of the landing points of the flexibly-connected multi-node small celestial body detector, which solves the technical problems that: (1) a model is established for the optimal distribution problem of the landing points of the flexibly-connected multi-node small celestial body detector, so that the actual problem is described more clearly, and the problem is solved by using an optimization method conveniently; (2) generating an optimal landing point selection scheme of a plurality of detector nodes to ensure that the detector nodes land at safe and valuable positions; (3) in the process of generating the landing site selection method, the position constraints among the nodes are checked, an optimal landing position distribution scheme meeting the position constraints among the nodes is generated, and the landing safety and the value benefit of the multi-node detector are improved.
The purpose of the invention is realized by the following technical scheme:
the invention discloses an optimal distribution method of landing points of a flexibly-connected multi-node small celestial body detector. Establishing an abstract model for the probe node information and the selectable landing point information, describing the optimal distribution problem of the flexible connection multi-node small celestial body probe landing points with position constraint as a constrained bipartite graph optimal matching problem, and then generating a landing point optimal distribution scheme meeting the position constraint by adopting a bipartite graph optimal matching method added with constraint check. The optimal distribution method of the landing points of the flexibly-connected multi-node small celestial body detector can quickly generate an optimal landing position distribution scheme meeting position constraints among nodes on the basis of a given detector node set and an optional landing point set, and improves the landing safety and value benefits of the multi-node detector.
The invention discloses an optimal distribution method of landing points of a flexible connection multi-node small celestial body detector, which comprises the following steps of:
the method comprises the steps of firstly, obtaining scene information, wherein the scene information comprises the position of the detector nodes before landing, position constraint among the detector nodes and the positions and values of a plurality of landing points which can be selected by each detector node.
And carrying out gridding processing on the landing range in the view field of the detector. The size of the grid depends on the area covered by the probe node after landing. If the detector nodes cover a rectangular area with an area Xm × Ym, the side length of the grid is max (X, Y) meters, i.e., the larger of X and Y. The landing sites have location information and value amount. The position information is represented by a group of L (x, y) coordinates, x is an abscissa, y is an ordinate, the origin of the coordinates is determined by the landing site range formed by all the probe nodes, one point is selected as the origin in the landing area, and the position information is generated for all the probe nodes and the landing sites on the basis. The value amount of the landing point is determined by the terrain condition and the engineering value of the point, the value of the point in the obstacle area with rugged terrain is a negative number, the value of the point with rugged terrain is smaller, the value information of the flat point is a positive number, and the value of the point with flat terrain is higher. For flat landing sites, the higher the engineering value the higher the amount of point value. The position constraint between the detector nodes is determined by the maximum length dmax of the flexible rope, and the position distance between two detector nodes cannot exceed dmax.
And step two, establishing an abstract model for the probe node information and the selectable landing point information acquired in the step one, and describing the optimal distribution problem of the flexible connection multi-node small celestial body probe landing points with position constraint as a constrained bipartite graph optimal matching problem.
A bipartite graph is a model in graph theory, with vertices divided into two sets of mutually disjoint sets. In the multi-detector node landing site allocation problem, a detector node is a point set R ═ { R ═ R1,r2,…,rnThe landing point is another point set P ═ P1,p2,…,pm}. The connecting line between the probe node and the landing point represents the probe node r to select the landing point p. The value on the edge rp represents the amount of value of the point p. One probe node can select a plurality of landing points, one landing point also belongs to the landing selectable range of a plurality of probe nodes, but the landing points and the probe nodes in the final landing scheme need to be matched one to one, and one landing point can only contain one probe node. The bipartite graph optimal matching problem is to solve how to match the points of two sets with each other to maximize the sum of the multiple edge values, which is equivalent to selecting their landing sites for multiple probe nodes, and the total revenue of which is the highest of all feasible landing scenarios. However, in the landing problem of the probe nodes, not only the benefit but also the distance constraint among the probe nodes need to be considered, and the distance among the positions of the probe nodes cannot exceed the maximum length of the flexible rope. Therefore, constraints are added between the probe nodes on the basis of the optimal matching problem of the bipartite graph. Points corresponding to two detector nodes connected by a rope are connected in a bipartite graph by edges, the edges between the detector nodes represent distance constraints, and the value on the edges is more than or equal to the distance between the detector nodes. The optimal distribution problem of the landing points of the flexible connection multi-node small celestial body detector with the position constraint is described as a constraint bipartite graph optimal matching problem.
And step three, on the basis of the constrained bipartite graph optimal matching problem established in the step two, generating a landing site optimal distribution scheme meeting the position constraint by adopting a bipartite graph optimal matching method added with constraint check.
In order to save the calculation time and ensure that an optimal solution is found, the Kuhn-Munkras algorithm (K-M algorithm) is preferably used as the optimal matching method of the bipartite graph.
Step 3.1: and storing the bipartite graph modeled in the second step in the form of a adjacency matrix. w is an n × m matrix, n is the number of detector nodes, and m is the number of landing nodes. w [ i ] [ j ] ═ 0, representing that the probe node i cannot land to the landing point j. w [ i ] [ j ] ═ v, representing that the value benefit of the probe node i landing to the point j is v.
Step 3.2: initializing the parameters of the landing sites and the probe nodes. The initial position of the probe node satisfies the distance constraint, with selectable landing sites for the probe node around the initial position. And initializing the matched probe nodes of all the landing sites to be 0, and representing unmatched probe nodes.
Step 3.3: and generating a sub-graph of the current bipartite graph according to a K-M algorithm. The subgraph includes all points, and each left-side edge with the largest value.
Step 3.4: adding a new parameter in the K-M algorithm: the expected position of each left-hand point. The expected locations are used to check the location constraints between the probe nodes.
Step 3.5: in the subgraph generated in step 3.3, the expected position of the initialized probe node is the currently assigned position. And the expected position of the detector node in the initially generated subgraph is the initial position of the detector node.
Step 3.6: and finding a perfect match of the bipartite graph in the current subgraph through the Hungarian algorithm. If in a match, each vertex in the graph is associated with an edge in the graph, the match is said to be a perfect match, also referred to as a perfect match.
Step 3.6.1: each probe node is searched for a landing site that can be matched in turn. In the current sub-graph, each probe node visits the untested landing sites in turn. The currently attempted landing site is first marked as visited. Then, taking the position of the landing point as the expected position of the probe node, performing distance constraint check of the multiple probe nodes, namely checking whether the distance between the probe nodes formed by the expected positions of all the probe nodes is smaller than dmax.
Step 3.6.2: and if the expected position of each current probe node meets the position constraint, judging whether the landing point can be successfully matched with the landing point. The criterion for judging whether the landing site can be matched is as follows: the landing sites selected by the probe nodes are not matched with other probe nodes, or although the landing sites are matched with other probe nodes, the matched probe nodes can try to match again to find other landing sites. And if the expected positions of the current probe nodes do not meet the position constraint, iterating the next landing point. If all the landing sites do not meet the position constraint, the expected position of the probe node is returned to the position before the attempted landing site without moving the probe node.
Step 3.6.3: and if the current probe node successfully finds the landing point, recording the current landing point of the probe node, marking the landing point as matched, and updating the current distribution position of the probe node to the position of the landing point. And then continuing to search for a matched landing point for the next detector node.
Step 3.7: and if all the detector nodes successfully find the matched landing points, finishing the matching. And if the matched landing point can not be found for a certain detector node in the current sub-graph, expanding the sub-graph. The way of expanding the subgraph is to compare the next maximum edge values of all visited detector nodes, select the maximum edge value maxv, and add the edge value maxv of the visited detector nodes into the subgraph to generate a new subgraph.
Step 3.8: and after a new subgraph is generated, marking all the detector nodes and the landing sites as unvisited, and updating the expected positions of the detector nodes to be the current matching positions.
Step 3.9: repeating the steps from 3.6 to 3.8 until all the detector nodes are matched with the landing points, and returning to the successful distribution, wherein the landing points matched with the detector nodes are the optimal landing point distribution scheme of the detector nodes; or if the unmatched landing points exist but no edge capable of being expanded exists, the allocation failure is returned. If the return distribution fails, the integral translation of the probe node group needs to be considered to search other possible landing areas.
The method also comprises the following four steps: the landing point distribution problem is modeled into a bipartite graph optimal matching problem by the optimal distribution of the landing points of the multi-node small celestial body detector under the position constraint, the landing points are distributed by adopting an improved bipartite graph optimal matching algorithm, and the value benefit of the landing point distribution of multi-detector node landing can be improved. A detector node position constraint checking function is added into the bipartite graph optimal matching algorithm, so that a position distribution scheme meets position constraint among detector nodes, and the safety of the multi-node detector is guaranteed.
The value revenue includes: the flatness of the landing position determines the task safety, and the engineering value is brought by information such as the soil composition of the landing position and the special terrain.
Has the advantages that:
1. the invention discloses an optimal distribution method of landing points of a flexibly-connected multi-node small celestial body detector. The bipartite graph model is suitable for the mutual matching problem of two groups of different objects, the improved bipartite graph optimal matching algorithm is more suitable for processing the optimal distribution problem of the landing points of the flexible-connection multi-node small celestial body detector, the landing scheme with the highest value income is searched efficiently, the detector nodes can be guaranteed to find the respective landing points, and the engineering value income of tasks is improved.
2. The invention discloses an optimal distribution method of landing points of a flexibly-connected multi-node small celestial body detector, which is characterized in that distance constraint check is added on the basis of a bipartite graph optimal matching algorithm, so that a landing point distribution scheme meets the distance constraint caused by a flexible rope between detector nodes, namely the space distance between any two detectors is not more than the length of the flexible rope between the detectors, and the safety of the flexibly-connected multi-node detector is ensured.
3. The invention discloses an optimal distribution method of landing points of a flexible connection multi-node small celestial body detector.
Drawings
FIG. 1 is a scenario diagram of a multi-probe node landing site allocation problem
FIG. 2 is a flow chart of an optimal distribution method of landing points of a flexible connection multi-node small celestial body detector disclosed by the invention.
FIG. 3 is initial information of a field three probe node landing scenario in an embodiment.
FIG. 4 is a modeling of a multi-probe node landing site assignment problem under location constraints as a constrained bipartite graph optimal matching problem.
Detailed Description
For better illustrating the objects and advantages of the present invention, the following description will be made with reference to the accompanying drawings and examples.
In order to verify the feasibility of the method, as shown in fig. 1, taking the problem that a probe including three nodes lands in an area including nine landing sites as an example, the method of the present invention is used to determine the landing positions of the three probe nodes, so as to realize the optimal landing site allocation meeting the position constraint.
As shown in fig. 2, the method for optimally allocating landing sites of a flexible-connection multi-node small celestial body detector disclosed in this embodiment includes the following steps:
the method comprises the steps of firstly, obtaining scene information, wherein the scene information comprises the position of the detector nodes before landing, position constraint among the detector nodes and the positions and values of a plurality of landing points which can be selected by each detector node.
As shown in fig. 3, there are three detector nodes in the detector node set, and the region is divided into a 1 × 1 grid according to the occupied area of the detector nodes. The probe's node 1 may select landing points having points 1, 2, 4 and 5, i.e. four nodes surrounded by dotted lines around circle 1 in fig. 3, the initial position being at the center of the four points, i.e. where circle 1 is located in fig. 3,the initial positions of the other two probe nodes are also at the central positions of the four landing sites which are optional. The initial positions of the three detector nodes are respectively Lr1(0.5,0.5),Lr2(0.5,1.5),Lr3(1.5). The selectable landing range of each probe node is within a certain range around the initial position of each probe node, in order to facilitate the effect of the method to be displayed, only four landing points around each probe node are selected as the selectable landing range in the embodiment, and in an actual problem, the selectable landing range of each probe node can be expanded to the whole visible landing area. The embodiment assumes that four landing sites around the initial position are selectable landing sites, and each landing site has a number, a value amount and position information. The information of the 9 landing sites in the example is shown in the following table. Where points 6 and 8 are obstacle points and the worth amount is negative. Landing sites 1 and 5 are not only flat but also of special chemical composition worth the research of scientists and are therefore of high value.
TABLE 1 landing site information
The position constraint between the detector nodes is determined by the maximum length dmax of the flexible rope, and the position distance between two detector nodes cannot exceed dmax. In the present embodiment, dmax is 2.
And step two, establishing an abstract model for the probe node information and the selectable landing point information acquired in the step one, and describing the problem as a constrained bipartite graph optimal matching problem, wherein the bipartite graph model is shown in FIG. 4.
In the present embodiment, the detector node is a point set R ═ { R ═ R1,r2,r3The landing point is another point set P ═ P1,p2,…,p9}. The landing sites that can be selected by probe node 1 are {1, 2, 4, 5}, so the first point to the left in the bipartite graphAnd edges are connected with the 1, 2, 4 and 5 points on the right side, and the edge value is the value of the landing point. The landing sites that can be selected by the probe node 2 are {2, 3, 5, 6}, and the landing sites that can be selected by the probe node 3 are {5, 6, 8, 9 }.
And step three, on the basis of the constrained bipartite graph optimal matching problem established in the step two, generating a landing site optimal distribution scheme meeting the position constraint by adopting a bipartite graph optimal matching method added with constraint check.
In order to save the calculation time and ensure that an optimal solution is found, the Kuhn-Munkras algorithm (K-M algorithm) is preferably used as the optimal matching method of the bipartite graph.
TABLE 2 values for points in the w matrix
Step 3.1: and storing the bipartite graph modeled in the second step in the form of a adjacency matrix. w is a 3 x 9 matrix, 3 is the number of detector nodes, and 9 is the number of landing nodes. w [ i ] [ j ] ═ 0, representing that the probe node i cannot land to the landing point j. w [ i ] [ j ] ═ v, which represents that the benefit of the detector node i landing to the point j is v. The values in the matrix are shown in table 2. The representative probe node with a value of 0 may not select this landing point, and there is no connecting line in the bipartite graph from this left point to this right point. A value other than 0 indicates a link, and the value of the link indicates the value of the probe node landing at that point.
Step 3.2: initializing the parameters of the landing sites and the probe nodes. The initial position of the probe node satisfies the distance constraint, with selectable landing sites for the probe node around the initial position. And initializing the matched probe nodes of all the landing sites to be 0, and representing unmatched probe nodes.
Step 3.3: and generating a sub-graph of the current bipartite graph according to a K-M algorithm. The sub-graph includes all points, and each side with the largest left-side point value, i.e. side (1, 5), side (2, 5), and side (3, 5).
Step 3.4: adding a new parameter in the K-M algorithm: the expected position of each left-hand point. The expected locations are used to check the location constraints between the probe nodes.
Step 3.5: in the subgraph generated in step 3.3, the expected position of the initialized probe node is the currently assigned position. The expected position of the detector node in the initially generated subgraph is the initial position of the detector node, namely Lr1(0.5,0.5),Lr2(0.5,1.5),Lr3(1.5,1.5)。
Step 3.6: and finding a perfect match of the bipartite graph in the current subgraph through the Hungarian algorithm. If in a match, each vertex in the graph is associated with an edge in the graph, the match is said to be a perfect match, also referred to as a perfect match.
Step 3.6.1: each probe node is searched for a landing site that can be matched in turn. First, the probe node is marked as visited. In the current sub-graph, each probe node visits the untested landing sites in turn. The currently attempted landing site is first marked as visited. Then, taking the position of the landing point as the expected position of the probe node, performing distance constraint check of the multiple probe nodes, namely checking whether the distance between the probe nodes formed by the expected positions of all the probe nodes is smaller than dmax. In this embodiment, the first expected position of the probe node 1 is the landing site 5, while the probe nodes 2, 3 are still in the initial position. Because the three detector nodes are connected by the ropes, the distances between the three detector nodes cannot be larger than 2, and the expected positions of the current detector nodes accord with the constraint according to calculation.
Step 3.6.2: and if the expected position of each current probe node meets the position constraint, judging whether the landing point can be successfully matched with the landing point. The criterion for judging whether the landing site can be matched is as follows: the landing sites selected by the probe nodes are not matched with other probe nodes, or although the landing sites are matched with other probe nodes, the matched probe nodes can try to match again to find other landing sites. And if the expected positions of the current probe nodes do not meet the position constraint, trying the next landing point. If all the attempted landing sites do not satisfy the position constraint, it is equivalent to not moving the probe node and returning its expected position to the position before the attempted landing site.
Step 3.6.3: if the current probe node successfully finds the landing point, the current landing point of the probe node is recorded, the landing point is marked as matched, and the current distribution position of the probe node is updated to the position of the landing point. And then continuing to search for a matched landing point for the next detector node.
Step 3.7: and if all the detector nodes successfully find the matched landing points, finishing the matching. And if the matched landing point can not be found for a certain detector node in the current sub-graph, expanding the sub-graph. The way of expanding the subgraph is to compare the next maximum edge values of all visited detector nodes, select the maximum edge value maxv, and add the edge value maxv of the visited detector nodes into the subgraph to generate a new subgraph. In this embodiment, the probe node 1 selects the landing site 5 successfully, and the probe node 2 also selects the landing site 5, but the landing site 5 is already occupied, and only one edge of the probe node 1 in the first generated subgraph is connected with the right point, so that other landing sites cannot be replaced. At this time, the matching can be continued only by adding edges in the subgraph. For the visited probe node 1 and probe node 2, the point with the highest value except the landing point 5 is the landing point 1, so the edge (1, 1) is added into the subgraph to form a new subgraph.
Step 3.8: and after a new subgraph is generated, marking all the detector nodes and the landing sites as unvisited, and updating the expected positions of the detector nodes to be the current matching positions.
Step 3.9: and repeating the steps from 3.6 to 3.8 until all the detector nodes are matched with the landing sites, returning to the distribution success, and finally matching the detector nodes 1 with the landing sites 1, the detector nodes 2 with the landing sites 3 and the detector nodes 3 with the landing sites 5. At the moment, the positions of the three probe nodes are (0, 0) (0, 2) (1, 1), the distance constraint is met, the allocated landing site gains are maximum, and the values of the three points are 9, 8 and 10 respectively.
Step four: the landing point distribution problem is modeled into a bipartite graph optimal matching problem through the optimal distribution of the landing points of the multi-node small celestial body detector under the position constraint, the landing points are distributed by adopting an improved bipartite graph optimal matching algorithm, and the value benefit of the landing point distribution of the multi-node detector landing can be improved. A detector node position constraint checking function is added into the bipartite graph optimal matching algorithm, so that a position distribution scheme meets position constraint among detector nodes, and the safety of the multi-node detector is guaranteed.
The value revenue includes: the flatness of the landing position determines the task safety, and the engineering value is brought by information such as the soil composition of the landing position and the special terrain.
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 within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (7)
1. The optimal distribution method of the landing points of the flexibly-connected multi-node small celestial body detector is characterized by comprising the following steps: comprises the following steps of (a) carrying out,
acquiring scene information including the position of the detector nodes before landing, position constraint among the detector nodes and the positions and values of a plurality of selectable landing points of each detector node;
step two, establishing an abstract model for the probe node information and the selectable landing point information acquired in the step one, and describing the optimal distribution problem of the flexible connection multi-node small celestial body probe landing points with position constraint as a constrained bipartite graph optimal matching problem;
and step three, on the basis of the constrained bipartite graph optimal matching problem established in the step two, generating a landing site optimal distribution scheme meeting the position constraint by adopting a bipartite graph optimal matching method added with constraint check.
2. The optimal distribution method for landing sites of flexible connection multi-node small celestial body detectors of claim 1, wherein the method comprises the following steps: the method also comprises the following four steps: the landing point distribution problem is modeled into a bipartite graph optimal matching problem by the optimal distribution of the landing points of the multi-node small celestial body detector under the position constraint, the landing points are distributed by adopting an improved bipartite graph optimal matching algorithm, and the value benefit of the landing point distribution of multi-detector node landing can be improved; the bipartite graph optimal matching algorithm is added with a detector node position constraint checking function, so that the position distribution scheme meets the position constraint among detector nodes, and the safety of a multi-detector node system is ensured.
3. The optimal distribution method for landing sites of flexible connection multi-node small celestial body detectors of claim 2, wherein the method comprises the following steps: the value revenue includes: the flatness of the landing position determines the task safety, and the engineering value is brought by information such as the soil composition of the landing position and the special terrain.
4. The optimal distribution method for landing sites of flexible connection multi-node small celestial body probes as claimed in claim 1, 2 or 3, wherein: the first implementation method comprises the following steps of,
carrying out gridding processing on the landing range in the view field of the detector; the size of the grid depends on the area covered by the probe nodes after landing; if the coverage area of the detector node is a rectangular area of Xm multiplied by Ym, the side length of the grid is max (X, Y) meters, namely the larger value of X and Y; the landing points have position information and value quantity; the position information is represented by a group of L (x, y) coordinates, x is an abscissa, y is an ordinate, the origin of the coordinates is determined by the landing site range formed by all the detector nodes, a point is selected as the origin in the landing area, and the position information is generated for all the detector nodes and the landing sites on the basis; the value quantity of the landing points is determined by the terrain condition and the engineering value of the points, the value of the points in the obstacle area with rugged terrain is a negative number, the value of the points with rugged terrain is smaller, the value information of the flat points is a positive number, and the value of the points with flat terrain is higher; for flat landing sites, the higher the engineering value, the higher the amount of point value; the position constraint between the detector nodes is determined by the maximum length dmax of the flexible rope, and the position distance between two detector nodes cannot exceed dmax.
5. The optimal distribution method for landing sites of flexible connection multi-node small celestial body detectors of claim 4, wherein the method comprises the following steps: the second step is realized by the method that,
the bipartite graph is a model in graph theory, and the vertexes are divided into two sets of mutually-disjoint sets; in the multi-detector node landing site allocation problem, a detector node is a point set R ═ { R ═ R1,r2,…,rnThe landing point is another point set P ═ P1,p2,…,pm}; a connecting line between the detector node and the landing point represents the detector node r to select the landing point p; the value on the edge rp represents the amount of value of the point p; a plurality of landing points can be selected for one detector node, one landing point also belongs to the landing selectable range of a plurality of detector nodes, but the landing points and the detector nodes in the final landing scheme need to be matched one to one, and one landing point can only contain one detector node; the bipartite graph optimal matching problem is to solve the problem that how to match points of two sets with each other can maximize the sum of a plurality of edge values, which is equivalent to selecting landing points of a plurality of probe nodes, and the total income of the landing points is the highest of all feasible landing schemes; however, in the landing problem of the detector nodes, not only the benefit but also the distance constraint among the detector nodes need to be considered, and the distance among the detector node positions cannot exceed the maximum length of the flexible rope; therefore, on the basis of the optimal matching problem of the bipartite graph, adding constraints among the nodes of the detector; points corresponding to two detector nodes connected by a rope are connected in a bipartite graph by edges, the edges between the detector nodes represent distance constraints, and the value on the edges is more than or equal to the distance between the detector nodes; the optimal distribution problem of the landing points of the flexible connection multi-node small celestial body detector with the position constraint is described as a constraint bipartite graph optimal matching problem.
6. The optimal distribution method for landing sites of flexible connection multi-node small celestial body detectors of claim 5, wherein the method comprises the following steps: in order to save the calculation time and ensure that an optimal solution is found, the Kuhn-Munkras algorithm is selected as the bipartite graph optimal matching method.
7. The optimal distribution method for landing sites of flexible connection multi-node small celestial body detectors of claim 5, wherein the method comprises the following steps: the third step is to realize the method as follows,
step 3.1: storing the bipartite graph modeled in the second step in the form of an adjacency matrix; w is an n multiplied by m matrix, n is the number of detector nodes, and m is the number of landing points; w [ i ] [ j ] ═ 0, which represents that the detector node i cannot land to the landing point j; w [ i ] [ j ] ═ v, representing that the value benefit of the detector node i landing to the point j is v;
step 3.2: initializing parameters of a landing point and a detector node; the initial position of the detector node meets the distance constraint, and the selectable landing points of the detector node are around the initial position; initializing matched detector nodes of all landing sites to be 0, and representing unmatched detector nodes;
step 3.3: generating a sub-graph of the current bipartite graph according to a K-M algorithm; the subgraph comprises all points and each edge with the maximum left-side point value;
step 3.4: adding a new parameter in the K-M algorithm: the expected location of each left-hand point; the expected positions are used for checking position constraints among the nodes of the detector;
step 3.5: in the subgraph generated in step 3.3, initializing the expected position of the detector node as the currently allocated position; the expected position of the detector node in the primarily generated subgraph is the initial position of the detector node;
step 3.6: finding a perfect match of the bipartite graph in the current subgraph through a Hungarian algorithm; if in a match, each vertex in the graph is associated with an edge in the graph, the match is called a complete match, also called a perfect match;
step 3.6.1: searching a landing point which can be matched for each detector node in sequence; in the current subgraph, each detector node sequentially accesses the untested landing sites; marking the currently attempted landing site as visited; then, taking the position of the landing point as the expected position of the detector node to carry out distance constraint check of the multiple detector nodes, namely checking whether the distance between the detector nodes formed by the expected positions of all the detector nodes is smaller than dmax;
step 3.6.2: if the expected position of each current probe node meets the position constraint, judging whether the landing point can be successfully matched with the landing point; the criterion for judging whether the landing site can be matched is as follows: the landing sites selected by the detector nodes are not matched with other detector nodes, or although the landing sites are matched with other detector nodes, the matched detector nodes can try to match again to find other landing sites; if the expected position of each current detector node does not meet the position constraint, iterating the next landing point; if all the landing sites do not meet the position constraint, equivalently, the probe node is not moved, and the expected position of the probe node is returned to the position before the attempted landing site;
step 3.6.3: if the current probe node successfully finds the landing point, recording the current landing point of the probe node, marking the landing point as matched, and updating the current distribution position of the probe node to the position of the landing point; continuing to search a matched landing point for the next detector node;
step 3.7: if all the detector nodes successfully find the matched landing points, the matching is finished; if the matched landing point cannot be found for a certain detector node in the current sub-graph, expanding the sub-graph; the mode of expanding the subgraph is to compare the next maximum edge values of all visited detector nodes, select the maximum edge value maxv, add the edge values of the visited detector nodes, which are maxv, into the subgraph to generate a new subgraph;
step 3.8: after a new subgraph is generated, all the detector nodes and the landing sites are marked as not-visited, and the expected positions of the detector nodes are updated to be the current matching positions;
step 3.9: repeating the steps from 3.6 to 3.8 until all the detector nodes are matched with the landing points, and returning to the successful distribution, wherein the landing points matched with the detector nodes are the optimal landing point distribution scheme of the detector nodes; or although there is a non-matched landing point, there is no edge that can be expanded, and the distribution is returned to fail; if the return distribution fails, the integral translation of the probe node group needs to be considered to search other possible landing areas.
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