Multi-robot Task Assignment Graph Segmentation Method Based on Road Network Topology Fusion Edge Points

Abstract

The present invention belongs to the field of technical collaborative task allocation for multi-robot systems, specifically a method for segmenting a multi-robot task allocation graph based on fusing edge points of a road network topology, including constructing the smallest separable block in the road network topology based on edge point information and storing it in a data list; generating upper and lower thresholds for balanced cost from the number of robots and an initial threshold factor; passing the smallest separable block into the data list according to the data list and calculating the path cost corresponding to the passed-in block, comparing it with the threshold balanced cost to determine whether to end the task allocation at this stage or pass in the next smallest separable block, selecting the next smallest separable block according to the set rules and then calculating and comparing until the current task allocation ends, and repeating this cycle; dividing the global road network topology into corresponding numbers of task groups according to the number of robots, reducing the set average cost threshold after the division is completed, and obtaining the final segmentation result through iterative optimization. The present invention improves the efficiency of multi-robot traversal search in a complex urban environment road network while reducing blindness and redundancy.

Description

Technical Field

[0001] The present invention belongs to the field of technical collaborative task allocation of multi-robot systems, and specifically relates to a multi-robot task allocation graph segmentation method based on road network topology fusion edge points. Background Art

[0002] In complex urban scenarios, when multi-robots perform tasks such as military reconnaissance, disaster rescue, and search and rescue, for the total task area, the first-stage task of a centralized multi-robot system is to segment the task area map. By allocating subgraphs to each robot, the tasks can be executed in parallel. Its essence is to model the road network topology input into the multi-robot system as a graph composed of a finite non-empty set V(G) of vertices and a set E(G) of edges between the vertices, and segment the input graph according to the number of multi-robots. For the task requirements of multi-robot regional road network traversal, the edges and points in the graph are allocated to the corresponding robots according to rules to perform the traversal task.

[0003] According to the way of splitting graph data, the current segmentation schemes can be divided into vertex partitioning (or edge-cut partitioning) and edge partitioning (or vertex-cut partitioning). Among them, vertex partitioning, as Figure 1 shown, is to allocate the nodes of the graph to each subgraph, maintaining the integrity of the subgraphs between the nodes; edge partitioning, as Figure 2 shown, is to allocate the edges of the graph to each subgraph, and each group of allocated edges forms a subgraph. However, with the increase in the complexity of the scenario and the change of the specific traversal requirements of multi-robots, the limitations of traditional graph segmentation methods begin to emerge, as follows:

[0004] 1. Traditional node-based graph segmentation methods often cause the edges between some nodes to be cut off (edge-cut). That is to say, when two nodes are respectively allocated to two subgraphs according to rules, if the two nodes are connected in the road network topology, then after the allocation is completed, the edge connecting the two nodes will be cut into two segments in the actual road network topology and allocated to different robots, causing redundancy in the actual robot traversal task and greatly increasing the complexity at the same time.

[0005] 2. For graph data that follows the power-law distribution, some nodes may be connected to a large number of edges. If vertex partitioning is performed, it will lead to the loss of a large number of edges and uneven load distribution of the edges, which is almost infeasible in actual multi-robot allocation.

[0006] 3. Traditional edge-based graph segmentation methods use edges as the basic segmentation units, and their segmentation rules often do not consider the connectivity of edges. In the actual allocation of multi-robot systems, the edges assigned to robots by edge-based segmentation often appear disconnected. In reality, the robots' traversal requirements for the edges in the road network demand that the allocated road network topology be reachable, that is, connectivity needs to be ensured.

[0007] 4. Edge-based graph segmentation strategies, due to not considering nodes, can cause node redundancy (vertex-cut). If the relationships between all nodes are calculated in advance to avoid redundancy by overviewing the whole, it will consume a large amount of memory and converge slowly.

[0008] Therefore, when facing the problem of graph segmentation task allocation for the actual road network topology of multi-robots, how to design a practically feasible graph segmentation method with low complexity and ensure the path connectivity of each single robot has become an urgent problem to be solved. Summary of the Invention

[0009] The purpose of the present invention is to provide a multi-robot task allocation graph segmentation method based on the fusion of edge points in the road network topology, so as to improve the efficiency of multi-robots in traversing and searching the road network in complex urban environments, reduce blindness and redundancy, and be able to more efficiently and simply allocate the path traversal tasks of the multi-robot system, meeting the growing demands of the number of robots and task complexity.

[0010] To achieve the above purpose, the present invention adopts the following technical solutions:

[0011] A multi-robot task allocation graph segmentation method based on the fusion of edge points in the road network topology, comprising the following steps:

[0012] Step 1: Obtain the number of robots in the multi-robot system and the road network topology information of the task area, extract the global road network topology graph based on the road network topology information of the task area, and calculate the total cost of the global road network topology graph;

[0013] Step 2: Process the global road network topology graph to construct the smallest separable blocks, number each smallest separable block, and create a data list of the smallest separable blocks at the same time;

[0014] Step 3: According to the number of robots and the total cost of the global road network topology graph, calculate the ideal equilibrium cost of each robot, set an initial cost threshold factor, and calculate the upper and lower thresholds of the equilibrium cost according to the set initial cost threshold factor;

[0015] Step 4: Select the smallest separable block corresponding to the smallest number from the data list created in Step 2 as the input;

[0016] Step 5: Calculate the total cost of all paths in the smallest separable block that encloses the input after inputting this smallest separable block in this task group;

[0017] Step 6: Subtract the total cost obtained in Step 5 from the ideal equilibrium cost. If the absolute value of the difference is less than the lower threshold, find the smallest separable block of the next input based on the rule that the distance to the block center is the closest, and return to Step 5 until the sum of the path costs corresponding to the smallest separable block of the input is greater than the lower threshold and less than the upper threshold, then complete the task graph allocation for this time;

[0018] If the absolute value of the difference is greater than the lower threshold and less than the upper threshold, remove the allocated smallest separable block from the data list, use the smallest separable block with the smallest number in the data list at this moment as the input, and return to Step 5 to generate the graph segmentation area of the next task until the input global road network topology graph is divided according to the number of robots;

[0019] Step 7: Narrow the set mean cost threshold, repeat Steps 3 to 6 to iteratively optimize the allocation result until the task balance index of each robot reaches the optimal, then the optimal graph segmentation multi-robot task allocation result can be obtained.

[0020] Furthermore, the calculation of the block center coordinates and the search rule for the next block in Step 6 are as follows:

[0021] Calculate the mean of the horizontal and vertical coordinates of all points surrounding the smallest separable block to determine a new point to replace the center point of the current smallest separable block; between the new center point and the center points of the remaining smallest separable blocks, use the Euclidean distance as the evaluation index, and select the smallest separable block with the smallest Euclidean distance, that is, the closest block, as the next input.

[0022] The multi-robot task allocation graph segmentation method based on the fusion of road network topology edge points provided by the present invention is aimed at the undirected graph segmentation task of the urban road network topology modeling in the task area. It adopts the method of fusing edge point features, that is, it is segmented based on the smallest separable block surrounded by edge points, with the goal of optimizing the balance of the road network topology cost assigned to each robot. At the same time, according to the adjacency of the blocks, the connectivity of the road network after allocation is ensured, so as to generate the final graph segmentation result and realize the balanced division of the graph. One of the optimization goals in the multi-objective optimization is the balance of different allocation results each time, and the other optimization goal is the closeness of a single task group to the ideal equilibrium cost in each iteration.

[0023] Compared with the prior art, the present invention effectively reduces the blindness and redundancy of multi-robots searching in complex urban areas, optimizes the resource allocation at the same time, improves the multi-robot cooperation balance and efficiency, and thus realizes reasonable task allocation. Description of the Drawings

[0024] Figure 1 is the traditional point segmentation method;

[0025] Figure 2 is the traditional edge segmentation method;

[0026] Figure 3 Flowchart of the multi-robot task allocation graph segmentation method based on road network topology fusion edge points for the embodiment;

[0027] Figure 4 Construct the minimum separable block for the monotonic edge in the embodiment;

[0028] Figure 5 Schematic diagram of the road network topology graph extraction and minimum separable block division for the urban regular scenario task area provided by the embodiment;

[0029] Figure 6 Schematic diagram of the road network topology graph extraction and minimum separable block division for the wide-area irregular scenario task area provided by the embodiment;

[0030] Figure 7 Schematic diagram of the multi-robot task allocation result of the graph segmentation based on road network topology fusion edge points for the urban regular scenario task area provided by the embodiment;

[0031] Figure 8 Schematic diagram of the multi-robot task allocation result of the graph segmentation based on road network topology fusion edge points for the wide-area irregular scenario task area provided by the embodiment. Detailed implementation manners

[0032] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be described in detail below in conjunction with the implementation manners and the accompanying drawings.

[0033] As Figure 3 shown, the multi-robot task allocation graph segmentation method based on road network topology fusion edge points provided by this embodiment includes the following steps:

[0034] Step 1: Obtain the number of robots in the multi-robot system and the road network topology information of the task area, extract the global road network topology graph based on the road network topology information of the task area, and calculate the total cost of the global road network topology graph; the implementation process includes:

[0035] Extract the global road network topology graph from the road network topology information of the task area. The global road network topology graph is composed of a finite non-empty set V(G) of vertices and a set E(G) of edges between the vertices, and is represented as: G = (V, E), where G represents the global road network topology graph, V is the set of vertices in graph G, and E is the set of edges in graph G.

[0036] In the global road network topology graph: Define each vertex as P( , (X, Y)), where is the number of this vertex, and (X, Y) is the coordinate of this vertex; Define each edge as E( ), where Let [[ID=]] be the ID of this edge, and C be the cost corresponding to this edge. In this embodiment, the road network length of this edge in the actual environment is used as its cost;

[0037] For the input global road network topology graph, its total cost is calculated as follows:

[0038] ;

[0039] In the above formula, E n is all the edges in the graph, is the cost corresponding to each edge;

[0040] Traverse all robots to obtain their number k; divide the global road network topology graph into corresponding task groups.

[0041] Step 2: Process the global road network topology graph to construct and number the smallest separable blocks, and number each smallest separable block. At the same time, create a smallest separable block data list. The implementation process includes:

[0042] Define the smallest separable block. The block is surrounded by points and edges. The smallest separable block is defined as a block that cannot be further divided, that is, the degree of each vertex in the block is 2. The specific definition of each smallest separable block is B = (N, P n , E n ), where N is the ID of this smallest separable block, P n is the set of points surrounding this block, and E n is the set of edges surrounding this block. If there is a vertex with degree 1 in the global road network topology graph, the edge connecting this vertex is a monotonic edge and cannot form a block. At this time, construct a smallest separable block for this edge, that is, add a duplicate edge of this edge to form a rectangular block with a pair of opposite sides being 0, so that the global road network topology can be completely divided into a set of smallest separable blocks, and number them and store them in the smallest separable block data list. The smallest separable block data list is . Figure 4 To show the process of constructing the smallest separable block for the monotonic edge, as Figure 4 shown, the road network topology graph consists of five vertices a, b, c, d, and e. Among them, vertex a is a vertex with degree 1, that is, the edge (a, b) connecting this vertex is a monotonic edge and cannot form a block. On the basis of the original topology graph, add a duplicate edge (a’, b’) of this edge to form a rectangular block with a pair of opposite sides being 0, that is, the costs of the edges (a, a’) and (b, b’) are zero, forming a smallest separable block.

[0043] Step 3: Calculate the ideal equilibrium cost of each robot according to the number of robots and the total cost of the global road network topology graph, and set the initial cost threshold factor as , and calculate the upper and lower thresholds of the equilibrium cost according to the set initial cost threshold factor; the specific calculation formula is as follows:

[0044] ;

[0045] ;

[0046] ;

[0047] Among them, Average_Cost is the ideal equilibrium cost, k is the number information of robots, Average_Up is the upper threshold of the equilibrium cost, and Average_Down is the lower threshold of the equilibrium cost.

[0048] Step 4: Based on the global road network topology map obtained in Step 2, select the smallest divisible block corresponding to the smallest number from the data list created in Step 2 as the input;

[0049] Step 5: Calculate the total cost of all paths in the smallest divisible block that encloses the input in this task group after inputting this smallest divisible block; specifically as follows:

[0050] This part is defined as:

[0051] The block set is ;

[0052] Each block has an edge set ;

[0053] Each edge has a cost ;

[0054] The total cost formula of the input smallest divisible block is:

[0055] ;

[0056] Among them, n represents the total number of blocks in the current block set, and m i represents the number of edges of the i-th block.

[0057] Step 6: Subtract the total cost obtained in Step 5 from the ideal equilibrium cost. The difference formula between the current input block cost and the equilibrium cost is: ;

[0058] If the absolute value of the difference is less than the lower threshold, find the next input smallest divisible block as the input according to the rule that the distance from the block center is the closest, and return to Step 5 until the sum of the path costs corresponding to the input smallest divisible block is greater than the lower threshold and less than the upper threshold, then complete the allocation of this task graph. The calculation of the block center coordinates and the search rule for the next block are as follows:

[0059] The input block set is ;

[0060] For each block there is an edge set ;

[0061] Each point has coordinates ;

[0062] The formula for the central coordinates of the input block is:

[0063] ;

[0064] ;

[0065] For the list of minimum separable block data , t represents the minimum separable block in the data list, and m represents the m-th minimum separable block in the data list;

[0066] Each block The central coordinates are obtained from the above formula , and simplified to: ;

[0067] The formula for calculating the distance between blocks is:

[0068] ;

[0069] The condition for finding the next minimum input block, i.e., the update condition:

[0070] ;

[0071] ;

[0072] ;

[0073] If the absolute value of the difference is greater than the lower threshold and less than the upper threshold, then remove the allocated minimum separable block from the data list, use the minimum separable block with the smallest number in the data list at this moment as the input, and return to step 5 to generate the graph segmentation area for the next task until the input global road network topology map is divided according to the number of robots.

[0074] Step 7: Narrow the set mean cost threshold, and iteratively optimize by repeating steps 3 to 6 until the balance index of each robot task reaches the optimal value, then the best multi-robot task allocation result can be obtained. Evaluate the balance quality of each allocation result. There are two conditions to end the iteration, and either condition can end the iteration, where:

[0075] The method for narrowing the set mean cost threshold is: set the mean cost threshold to 90% of the previous one for each iteration to achieve the narrowing of the mean cost threshold. The initial cost threshold in this embodiment is If it is reduced according to the above method, the cost threshold factor for the second iteration is 0.9 and so on until its variance no longer decreases or the path cost of each task allocation result reaches the set ideal threshold range;

[0076] One of the two conditions for terminating the iteration is that the cost of each allocation reaches the set ideal threshold range:

[0077] In the i-th iteration, if the cost of each allocation and the set ideal equilibrium cost The difference is within the tolerance then this condition is satisfied, that is:

[0078] ;

[0079] ;

[0080] wherein, is the set tolerance factor;

[0081] The other condition is that the variance no longer decreases:

[0082] In the i-th iteration, if the variance is greater than or equal to the variance of the previous iteration then this condition is satisfied, that is:

[0083] ;

[0084] ;

[0085] wherein, represents the variance, is the total cost of each observation value, that is, the corresponding block after each allocation , is the mean value, which is replaced by here, and n is the number of observation values, which is the number of robots k, that is, the number of tasks finally allocated.

[0086] To verify the feasibility and advantages of the multi-robot task allocation graph segmentation method of this embodiment, it will be described below in a specific scenario:

[0087] Figure 5 shows the extraction of the topological graph of the task area network and the minimum block division in the urban rule scenario; Figure 6 is for showing the extraction of the topological graph of the task area network and the minimum block division in the wide-area irregular scenario. As can be seen from Figure 5 , the extracted road network topology has a total of 15 vertices from A to O, 22 edges, and 8 minimum divisible blocks; as can be seen from Figure 6It can be seen that the extracted road network topology has a total of 17 vertices from A to Q, 26 edges, and 10 minimum separable blocks. It can be seen that in this embodiment, the segmentation is performed based on the minimum separable blocks surrounded by edge points, with the goal of optimizing the balance of the road network topology cost assigned to each robot. At the same time, according to the adjacency of the blocks, the connectivity of the road network after allocation can be ensured. Figure 7 It shows a schematic diagram of the multi-robot task allocation result of graph segmentation based on the fusion of edge points in the task area of the urban regular scenario road network topology; Figure 8 It shows a schematic diagram of the multi-robot task allocation result of graph segmentation based on the fusion of edge points in the task area of the wide-area irregular scenario road network topology. From Figure 7 and Figure 8 It can be seen that in this embodiment, the final graph segmentation result is generated to achieve an equal division of the graph. The road network topology of the area surrounded by each color represents that in the two scenarios, the graph segmentation results assigned to the four robots are Robot 1, Robot 2, Robot 3, and Robot 4 respectively, indicating that the method of this embodiment can more efficiently and simply allocate the path traversal tasks of the multi-robot system to meet the growing number of robots and the requirements of task complexity.

[0088] In summary, the multi-robot task allocation graph segmentation method based on the fusion of edge points in the road network topology of this embodiment is aimed at the undirected graph segmentation task of the urban road network topology modeling in the task area. It adopts the method of fusing edge point features, that is, segmentation is performed based on the minimum separable blocks surrounded by edge points, with the goal of optimizing the balance of the road network topology cost assigned to each robot. At the same time, according to the adjacency of the blocks, the connectivity of the road network after allocation is ensured, and the final graph segmentation result is generated by cooperating with multi-objective optimization to achieve an equal division of the graph.

[0089] It can be understood that the present invention is described through some embodiments. Those skilled in the art know that without departing from the spirit and scope of the present invention, various changes or equivalent replacements can be made to these features and embodiments. In addition, under the teaching of the present invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application belong to the scope protected by the present invention.

Claims

1. A multi-robot task allocation graph segmentation method based on road network topology fusion edge points, characterized in that: The following steps are involved: Step 1: Obtain the number of robots in the multi-robot system and the road network topology information of the task area, extract the global road network topology map based on the road network topology information of the task area, and calculate the total cost of the global road network topology map; The implementation methods include: Extracting a global road network topology graph from the task area road network topology information, wherein the global road network topology graph is composed of a finite non-empty set V(G) of vertices and a set E(G) of edges between vertices, expressed as: G=(V, E), wherein G represents the global road network topology graph, V is the set of vertices in the graph G, and E is the set of edges in the graph G; In the global road network topology, each vertex is defined as P(N p ,(X,Y)), where N p is the number of the vertex, (X, Y) is the coordinate of the vertex; each edge is defined as E(N e , C), where N e is the number of the edge, and C is the cost corresponding to the edge; For the input global road network topology, the total cost is calculated as follows: In the above formula, Sum_Cost is the total cost of the global road network topology, E n For all edges in the graph, c i Corresponding cost for each edge; Traverse all robots and obtain their number k; divide the global road network topology map into corresponding task groups; Step 2: Process the global road network topology map to construct the smallest divisible block, number each smallest divisible block, and create a smallest divisible block data list; the specific implementation method includes the following steps: Define the smallest divisible block. The block is surrounded by points and edges. The smallest divisible block is defined as a block that has no further divisible blocks, that is, the degree of each vertex in the block is 2; For each minimum divisible block, it is specifically defined as B = (N, P n , E n ), where N is the number of the smallest divisible block, P n is the set of points surrounding the block, E n is the set of edges surrounding the block; If there is a point with degree 1 in the global road network topology, that is, the edge connecting the point is a monotone edge and cannot form a block, then add a duplicate edge of the edge to construct a set of rectangular blocks with opposite edges of 0. In this way, the global road network topology is completely divided into a set of the smallest divisible blocks. The smallest divisible blocks are numbered, constructed and stored in a smallest divisible block data list, where the smallest divisible block data list is T = {t1, t2, ..., t m }, where t represents the smallest divisible block in the data list, and m represents the mth smallest divisible block in the data list; Step 3: Calculate the ideal equilibrium cost of each robot based on the number of robots and the total cost of the global road network topology, and set the initial cost threshold factor. Calculate the upper and lower thresholds of the equilibrium cost according to the following formula based on the set initial cost threshold factor. Average_Cost = Sum_Cost / k; Average_Up=Average_Cost+α*Average_Cost; Average_Down=Average_Cost-α*Average_Cost; Among them, Average_Cost is the ideal equilibrium cost, k is the number of robots, Average_Up is the upper threshold of the equilibrium cost, Average_Down is the lower threshold of the equilibrium cost, and α is the initial cost threshold factor; Step 4: Select the smallest divisible block corresponding to the smallest number from the data list created in step 2 as input; Step 5: After inputting this smallest divisible block, calculate the sum of the costs of all paths in the smallest divisible block in the task group that surrounds the input; Step 6: Subtract the sum of the costs obtained in step 5 from the ideal equilibrium cost: If the absolute value of the difference is less than the lower threshold, the minimum divisible block of the next input is searched as the input based on the rule of the closest block center distance, and the process returns to step 5 until the sum of the path costs corresponding to the minimum divisible block of the input is greater than the lower threshold and less than the upper threshold, and the task graph allocation is completed; If the absolute value of the difference is greater than the lower threshold but less than the upper threshold, the allocated minimum divisible block is removed from the data list, and the minimum divisible block with the smallest number in the data list at this moment is used as input, and the process returns to step 5 to generate the graph segmentation area for the next task until the input global road network topology graph is divided according to the number of robots; Step 7: Reduce the set mean cost threshold, repeat steps 3 to 6 to iteratively optimize the allocation results until the task balance index of each robot reaches the optimal level, and the optimal graph segmentation multi-robot task allocation result can be obtained.

2. According to claim 1, a multi-robot task allocation graph segmentation method based on road network topology fusion edge points is characterized in that: The implementation method of step 5 includes: Define the block set as B = {b1, b2, ..., b n }; Each block b i The edge set E i ={e i1 , e i2 , ..., e im }; Each edge e ij There is a price C ij ; The sum of the costs on all paths in the smallest divisible block that encloses the input is: Where n represents the total number of blocks in the current block set, m i Represents the number of edges of the i-th smallest divisible block.

3. The multi-robot task allocation graph segmentation method based on road network topology fusion edge points according to claim 2 is characterized in that: The calculation of the block center coordinates and the search rules for the next block in step 6 are as follows: Calculate the mean of the horizontal and vertical coordinates of all points surrounding the minimum separable block to determine a new point to replace the center point of the current minimum separable block; use the Euclidean distance between the new center point and the center points of the remaining minimum separable blocks as the evaluation index, and select the minimum separable block with the smallest Euclidean distance, that is, the nearest block as the next input.

4. The multi-robot task allocation graph segmentation method based on road network topology fusion edge points according to claim 3 is characterized in that: The condition for ending the iteration in step 7 should satisfy any one of the two conditions at the same time, one of the two conditions is that the cost of each allocation reaches the set ideal threshold range, and the other condition is that the variance no longer decreases.

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