Aircraft nest deployment and flight planning method for multi-target combined power inspection

Abstract

The invention discloses a nest deployment and flight planning method for multi-target combined power inspection. The method comprises the following steps: S1, establishing an unmanned aerial vehicle inspection scene model; s2, setting constraint conditions of an unmanned aerial vehicle inspection process; s3, mathematical modeling is completed according to the optimization target; s4, designing a multi-unmanned aerial vehicle nest deployment and flight path planning algorithm; s5, generating a visual aircraft nest deployment and flight path planning scheme; and S6, evaluating the performance of the proposed algorithm, aiming at reducing the nesting cost and the total inspection time to the maximum extent by jointly optimizing the nesting and unmanned aerial vehicle paths and simultaneously meeting the constraint condition that the flight time of the unmanned aerial vehicle is longest, and solving the problem by decomposing the problem into a nest deployment sub-problem and an unmanned aerial vehicle path planning sub-problem. Path optimization is carried out through a joint optimization scheme, wherein the joint optimization scheme comprises a Kmeans + + algorithm used for aircraft nest deployment and a minimum weight matching algorithm and a Fleure algorithm used for unmanned aerial vehicle path planning.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle (UAV) power inspection, and in particular relates to a method for deploying a drone nest and planning a flight for multi-target joint power inspection. Background Art

[0002] Intelligent inspection by drones is an important direction for the digital construction of power grids. Its key technologies include: path planning, wire tracking, precise inspection, long-term endurance, data transmission, etc. Path planning involves confidential tower coordinates, and other technologies can be provided by third parties.

[0003] Currently, scholars mostly use heuristic algorithms for path planning. Heuristic algorithms refer to methods inspired by natural operating rules or specific problem experiences. Commonly used heuristic algorithms include simulated annealing algorithms, genetic algorithms, ant colony algorithms, etc. Most scholars treat path planning as a traveling salesman problem, that is, traversing all target points without considering the connections between the target points to find the shortest path.

[0004] Most scholars treat the problem of UAV line inspection path planning for power grid lines as a traveling salesman problem, only considering traversing all towers and ignoring the line inspection between towers. In addition, all scholars' research data are simulation data, and no scholar has yet conducted path planning for the actual tower line coordinates of the power grid; therefore, it is necessary to design a drone nest deployment and flight planning method for multi-objective joint power inspection to solve the above problems. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a nest deployment and flight planning method for multi-objective joint power inspection, which aims to minimize the nesting cost and total inspection time by jointly optimizing nesting and UAV paths while satisfying the constraint of the longest UAV flight time. The problem is decomposed into nest deployment sub-problems and UAV path planning sub-problems, and path optimization is performed through a joint optimization scheme, including a Kmeans++ algorithm for nest deployment and a minimum weight matching algorithm and Fleury algorithm for UAV path planning.

[0006] In order to achieve the above technical effects, the technical solution adopted by the present invention is: A method for nest deployment and flight planning for multi-target joint power inspection includes the following steps: S1, establish drone inspection scene model; S2, setting constraints for the drone inspection process; S3, complete mathematical modeling according to the optimization goal; S4, design multi-UAV nest deployment and flight trajectory planning algorithm; S5, generate visual machine nest deployment and flight trajectory planning scheme; S6, evaluate the performance of the proposed algorithm.

[0007] Preferably, in step S1, establishing a drone inspection scene model includes: S101, Establish a multi-UAV transmission line tower inspection system: The system includes U drones, drone nests, A tower to be inspected, connecting two towers and The transmission lines between them are expressed as: ; ; ; , ; The flight altitude of the drone during the inspection is set to be constant, and the drone flies in a two-dimensional Cartesian coordinate system. The coordinates of ; Machine Nest The position coordinates are expressed as ; The coordinates of points on the transmission line are marked as , ; S102, in a three-dimensional environment, the physical performance constraints of the UAV are considered, including the maximum track constraint, the minimum track constraint, the flight altitude constraint and the angle of attack constraint of the UAV; during the inspection process, the UAV departs from the machine nest, and then performs hovering inspection and channel inspection according to the assigned tasks and the planned path; assuming that the take-off and landing time of the UAV is zero, and the UAV travels at a constant speed v during the channel inspection, the hovering time of the UAV hovering inspection is denoted by Indicates that, in each round of inspection, it only hovers once at each tower; then the distance between the two towers is: ; In the formula, x i ,y i and z i Represents the three-dimensional coordinates of the position of the i-th UAV; S103, during the inspection process, when the UAV battery power is lower than the threshold, it returns to the nest to charge; a complete inspection process includes multiple round trips, represented as ;d f represents the fth round trip; Set the total battery capacity of the drone to E and the flight power to , the hovering power is ; The remaining power is: ; Where t1 represents the flight time, and t2 represents the hovering time.

[0008] Preferably, in step S2, setting the constraint conditions of the drone inspection process includes: The flight distance of a drone cannot exceed its maximum flight distance; the flight altitude of a drone cannot exceed its maximum flight distance and cannot be lower than its minimum flight altitude; the climbing angle of a drone cannot exceed its angle of attack limit; Each machine nest is deployed on a pole tower, and each pole tower can deploy at most one machine nest; Each inspection point must be visited once and only once; The drone takes off from the nest and returns to the nest; Each charging station is deployed at only one tower; During the drone inspection process, the remaining power when returning to the machine nest shall not be less than 95% of the maximum power.

[0009] Preferably, in step S3, completing mathematical modeling according to the optimization target includes: The cost of drone nest deployment and the total time of drone mission execution are normalized and jointly defined as the cost function minimization problem, which is expressed as: ; ; ; ; ; in, is the system cost objective function, and Respectively represent the normalized time cost function and the machine nest construction cost function, is the inspection period of the task area, and is the total inspection time, where Indicates the total time the drone flies between task points during the inspection process; is the path planning variable. If the UAV starts from the mission point Fly to the mission point ,but ,otherwise ; Indicates the task point With mission points The distance between Represents the total time the drone spends hovering to check all mission points; The time it takes for a drone to inspect a tower; Indicates the time it takes for the drone to travel back and forth between the nest and the mission point; Represents the charging variable. If the drone is Fly to the Nest ,but ,otherwise ; z is the starting variable. If the drone starts from the nest Fly to the mission point ,but ,otherwise ; Indicates the total charging time of the drone during the inspection process; Indicates the overall cost of building the machine nest, Indicates the actual cost of building the machine nest; For deployment variables, if the machine nest Construction at the mission point Where ,otherwise .

[0010] Preferably, in step S4, designing a multi-UAV nest deployment and flight trajectory planning algorithm includes: S401, use K-Means++ algorithm to cluster the towers: a. From the dataset A sample point is randomly selected as the first initial cluster center ; b. For each data in the data set , calculate the square of the distance to the nearest center point among the selected center points; , where U represents the set of selected center points; c. Select a new data point uk as the new center, and the probability of selection is: ; d. If K cluster centers are selected, stop the iteration; otherwise return to a; S402, using the minimum weight matching algorithm to convert the Euler circuit: The input is a weighted undirected graph G = (V, E), where V is the vertex set, E is the edge set, and the weight of each edge (u,v) is w(u, v); the output is the minimum weight matching M, M is a matching on V, so that the sum of the weights of all edges in the matching is minimized. The execution process is: Initialization: M = empty set, U = V represents the unmatched vertex set; Calculate the weight matrix W between each vertex Repeat the following steps until all vertices are matched: Select an unmatched vertex u ∈ U; Select another vertex v ∈ U from the unmatched vertex set so that the weight w(u, v) of the edge (u, v) is minimized; Add (u, v) to the match M; Remove vertices u and v from U; Output matches M; S403, path planning using Fleury algorithm: The input is a connected undirected graph G = (V, E), where V is the vertex set and E is the edge set; the output is an Euler circuit or Euler path; the execution process is: Initialization: Select a vertex v from the graph G as the starting point. If there is an Euler path, the starting point must be one of the vertices with an odd degree; initialize an empty path P; Repeat the following steps until all edges have been traversed: At the current vertex v, select an edge e = (v, u) connected to v: If selecting edge e does not destroy connectivity, select that edge; If there are no other edges to choose from, then choose the only edge; Remove edge e from the graph and add vertex u to path P; Set the current vertex v to u; Output path P.

[0011] Preferably, in step S5, generating a visualized machine nest deployment and flight trajectory planning scheme includes: The coordinates of the tower points are displayed in a two-dimensional Cartesian coordinate system. According to the drone nest site selection plan, the location of the nest is marked, and the towers are connected in sequence according to the path planning plan to form the final path planning map.

[0012] Preferably, in step S6, evaluating the performance of the proposed algorithm includes: S601, obtaining the three-dimensional coordinate points of the tower; S602, using a minimum weight matching algorithm to calculate the minimum sum of weights of all edges; S603, performing path planning using Fleury algorithm; S604, setting the drone's own performance parameters.

[0013] The beneficial effects of the present invention are as follows: 1. This paper proposes a joint design scheme that aims to minimize the nesting cost and total inspection time by jointly optimizing nesting and UAV paths while satisfying the constraint of the longest UAV flight time. However, the proposed problem is a multi-objective coupling problem, and the complexity of the algorithm increases step by step with the number of towers. To solve these problems, we first decompose the problem into nest deployment subproblems and UAV path planning subproblems. Then, we propose a joint optimization scheme, including the Kmeans++ algorithm for nest deployment and the minimum weight matching algorithm and Fleury algorithm for UAV path planning.

[0014] 2. The present invention aims to resolve the contradiction between the improvement of UAV inspection efficiency and the increase of construction cost. It establishes an objective function for jointly optimizing the nest deployment cost and the total inspection time, while satisfying the constraint of the longest flight time of the UAV, thereby minimizing the nesting cost and increasing the total inspection time. It can be reasonably constructed according to actual engineering needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Schematic diagram of drone inspection scenario. DETAILED DESCRIPTION

[0016] Embodiment 1: like Figure 1 As shown, a method for nest deployment and flight planning for multi-target joint power inspection includes the following steps: S1, establish drone inspection scene model; S2, setting constraints for the drone inspection process; S3, complete mathematical modeling according to the optimization goal; S4, design multi-UAV nest deployment and flight trajectory planning algorithm; S5, generate visual machine nest deployment and flight trajectory planning scheme; S6, evaluate the performance of the proposed algorithm.

[0017] Preferably, in step S1, establishing a drone inspection scene model includes: S101, Establish a multi-UAV transmission line tower inspection system: The system includes U drones, drone nests, A tower to be inspected, connecting two towers and The transmission lines between them are expressed as: ; ; ; , ; The flight altitude of the drone during the inspection is set to be constant, and the drone flies in a two-dimensional Cartesian coordinate system. The coordinates of ; Machine Nest The position coordinates are expressed as ; The coordinates of points on the transmission line are marked as , ; S102, in a three-dimensional environment, the physical performance constraints of the UAV are considered, including the maximum track constraint, the minimum track constraint, the flight altitude constraint and the angle of attack constraint of the UAV; during the inspection process, the UAV departs from the machine nest, and then performs hovering inspection and channel inspection according to the assigned tasks and the planned path; assuming that the take-off and landing time of the UAV is zero, and the UAV travels at a constant speed v during the channel inspection, the hovering time of the UAV hovering inspection is denoted by Indicates that, in each round of inspection, it only hovers once at each tower; then the distance between the two towers is: ; In the formula, x i ,y i and z i Represents the three-dimensional coordinates of the position of the i-th UAV; S103, during the inspection process, when the UAV battery power is lower than the threshold, it returns to the nest to charge; a complete inspection process includes multiple round trips, represented as ;d f represents the fth round trip; Set the total battery capacity of the drone to E and the flight power to , the hovering power is ; The remaining power is: ; Where t1 represents the flight time, and t2 represents the hovering time.

[0018] Preferably, in step S2, setting the constraint conditions of the drone inspection process includes: The flight distance of a drone cannot exceed its maximum flight distance; the flight altitude of a drone cannot exceed its maximum flight distance and cannot be lower than its minimum flight altitude; the climbing angle of a drone cannot exceed its angle of attack limit; Each machine nest is deployed on a pole tower, and each pole tower can deploy at most one machine nest; Each inspection point must be visited once and only once; The drone takes off from the nest and returns to the nest; Each charging station is deployed at only one tower; During the drone inspection process, the remaining power when returning to the machine nest shall not be less than 95% of the maximum power.

[0019] Preferably, in step S3, completing mathematical modeling according to the optimization target includes: The cost of drone nest deployment and the total time of drone mission execution are normalized and jointly defined as the cost function minimization problem, which is expressed as: ; ; ; ; ; in, is the system cost objective function, and Respectively represent the normalized time cost function and the machine nest construction cost function, is the inspection period of the task area, and is the total inspection time, where Indicates the total time the drone flies between task points during the inspection process; is the path planning variable. If the UAV starts from the mission point Fly to the mission point ,but ,otherwise ; Indicates the task point With mission points The distance between Represents the total time the drone spends hovering to check all mission points; The time it takes for a drone to inspect a tower; Indicates the time it takes for the drone to travel back and forth between the nest and the mission point; Represents the charging variable. If the drone is Fly to the Nest ,but ,otherwise ; z is the starting variable. If the drone starts from the nest Fly to the mission point ,but ,otherwise ; Indicates the total charging time of the drone during the inspection process; Indicates the overall cost of building the machine nest, Indicates the actual cost of building the machine nest; For deployment variables, if the machine nest Construction at the mission point Where ,otherwise .

[0020] Preferably, in step S4, designing a multi-UAV nest deployment and flight trajectory planning algorithm includes: S401, use K-Means++ algorithm to cluster the towers: a. From the dataset A sample point is randomly selected as the first initial cluster center ; b. For each data in the data set , calculate the square of the distance to the nearest center point among the selected center points; , where U represents the set of selected center points; c. Select a new data point uk as the new center, and the probability of selection is: ; d. If K cluster centers are selected, stop the iteration; otherwise return to a; S402, using the minimum weight matching algorithm to convert the Euler circuit: The input is a weighted undirected graph G = (V, E), where V is the vertex set, E is the edge set, and the weight of each edge (u,v) is w(u, v); the output is the minimum weight matching M, M is a matching on V, so that the sum of the weights of all edges in the matching is minimized. The execution process is: Initialization: M = empty set, U = V represents the unmatched vertex set; Calculate the weight matrix W between each vertex Repeat the following steps until all vertices are matched: Select an unmatched vertex u ∈ U; Select another vertex v ∈ U from the unmatched vertex set so that the weight w(u, v) of the edge (u, v) is minimized; Add (u, v) to the match M; Remove vertices u and v from U; Output matches M; S403, path planning using Fleury algorithm: The input is a connected undirected graph G = (V, E), where V is the vertex set and E is the edge set; the output is an Euler circuit or Euler path; the execution process is: Initialization: Select a vertex v from the graph G as the starting point. If there is an Euler path, the starting point must be one of the vertices with an odd degree; initialize an empty path P; Repeat the following steps until all edges have been traversed: At the current vertex v, select an edge e = (v, u) connected to v: If selecting edge e does not destroy connectivity, select that edge; If there are no other edges to choose from, then choose the only edge; Remove edge e from the graph and add vertex u to path P; Set the current vertex v to u; Output path P.

[0021] Preferably, in step S5, generating a visualized machine nest deployment and flight trajectory planning scheme includes: The coordinates of the tower points are displayed in a two-dimensional Cartesian coordinate system. According to the drone nest site selection plan, the location of the nest is marked, and the towers are connected in sequence according to the path planning plan to form the final path planning map.

[0022] Preferably, in step S6, evaluating the performance of the proposed algorithm includes: S601, obtaining the three-dimensional coordinate points of the tower; S602, using a minimum weight matching algorithm to calculate the minimum sum of weights of all edges; S603, performing path planning using Fleury algorithm; S604, setting the drone's own performance parameters.

[0023] Embodiment 2: In order to resolve the contradiction between the improvement of drone inspection efficiency and the increase in construction costs, it is considered that when deploying drone nests, it is necessary not only to achieve full coverage of the search range, but also to weigh the one-time investment in nest construction costs and the time cost of drone inspections. Therefore, different weights are selected to compare the objective functions. Table 1 shows the number of drone nest deployments and the total inspection time optimized by the minimum weight algorithm and the Fleury algorithm under different weight coefficients. When the weight coefficient increases over time, it can be observed that the number of drone nests decreases from 2 to 1, while the total inspection time increases. This is because different weight coefficients have different degrees of optimization for drone inspection time and nest costs. In particular, when the weight coefficient is small, more emphasis is placed on optimizing the drone inspection time, and as the weight coefficient increases, the focus shifts to optimizing the drone nesting cost. The specific results are shown in Table 1 below:

[0024] Table 1: Comparison of simulation results of the minimum weight algorithm and Fleury algorithm under different weight factors; From Table 1, we can see that although the increase The construction cost of the drone nest can be reduced, but the total inspection time will increase accordingly.

Claims

1. A method for nest deployment and flight planning for multi-target joint power inspection, characterized in that: The following steps are involved: S1, establish drone inspection scene model; S2, setting constraints for the drone inspection process; S3, complete mathematical modeling according to the optimization goal; S4, design multi-UAV nest deployment and flight trajectory planning algorithm; S5, generate visual machine nest deployment and flight trajectory planning scheme; S6, evaluate the performance of the proposed algorithm.

2. The method for nest deployment and flight planning for multi-target joint power inspection according to claim 1 is characterized in that: In step S1, establishing a drone inspection scene model includes: S101, Establish a multi-UAV transmission line tower inspection system: The system includes U drones, drone nests, A tower to be inspected, connecting two towers and The transmission lines between them are expressed as: ； ； ； ， ； The flight altitude of the drone during the inspection is set to be constant, and the drone flies in a two-dimensional Cartesian coordinate system. The coordinates of ; Machine Nest The position coordinates are expressed as ; The coordinates of points on the transmission line are marked as , ; S102, in a three-dimensional environment, the physical performance constraints of the UAV are considered, including the maximum track constraint, the minimum track constraint, the flight altitude constraint and the angle of attack constraint of the UAV; during the inspection process, the UAV departs from the machine nest, and then performs hovering inspection and channel inspection according to the assigned tasks and the planned path; assuming that the take-off and landing time of the UAV is zero, and the UAV travels at a constant speed v during the channel inspection, the hovering time of the UAV hovering inspection is denoted by Indicates that, in each round of inspection, it only hovers once at each tower; then the distance between the two towers is: ； In the formula, x i ,y i and z i Represents the three-dimensional coordinates of the position of the i-th UAV; S103, during the inspection process, when the UAV battery power is lower than the threshold, it returns to the nest to charge; a complete inspection process includes multiple round trips, represented as ;d f represents the fth round trip; Set the total battery capacity of the drone to E and the flight power to , the hovering power is ; The remaining power is: ； Where t1 represents the flight time, and t2 represents the hovering time.

3. The method for nest deployment and flight planning for multi-target joint power inspection according to claim 1 is characterized in that: In step S2, setting the constraints of the drone inspection process includes: The flight distance of a drone cannot exceed its maximum flight distance; the flight altitude of a drone cannot exceed its maximum flight distance and cannot be lower than its minimum flight altitude; the climbing angle of a drone cannot exceed its angle of attack limit; Each machine nest is deployed on a pole tower, and each pole tower can deploy at most one machine nest; Each inspection point must be visited once and only once; The drone takes off from the nest and returns to the nest; Each charging station is deployed at only one tower; During the drone inspection process, the remaining power when returning to the machine nest shall not be less than 95% of the maximum power.

4. The method for nest deployment and flight planning for multi-target joint power inspection according to claim 1 is characterized in that: In step S3, completing mathematical modeling according to the optimization target includes: The cost of drone nest deployment and the total time of drone mission execution are normalized and jointly defined as the cost function minimization problem, which is expressed as: ； ； ； ； ； in, is the system cost objective function, and Respectively represent the normalized time cost function and the machine nest construction cost function, is the inspection period of the task area, and is the total inspection time, where Indicates the total time the drone flies between task points during the inspection process; is the path planning variable. If the UAV starts from the mission point Fly to the mission point ,but ,otherwise ; Indicates the task point With mission points The distance between Represents the total time the drone spends hovering to check all mission points; The time it takes for a drone to inspect a tower; Indicates the time it takes for the drone to travel back and forth between the nest and the mission point; Represents the charging variable. If the drone is Fly to the Nest ,but ,otherwise ; z is the starting variable. If the drone starts from the nest Fly to the mission point ,but ,otherwise ; Indicates the total charging time of the drone during the inspection process; Indicates the overall cost of building the machine nest, Indicates the actual cost of building the machine nest; For deployment variables, if the machine nest Construction at the mission point If ,otherwise .

5. The method for nest deployment and flight planning for multi-target joint power inspection according to claim 1 is characterized in that: In step S4, designing a multi-UAV nest deployment and flight trajectory planning algorithm includes: S401, use K-Means++ algorithm to cluster the towers: a. From the dataset A sample point is randomly selected as the first initial cluster center ; b. For each data in the data set , calculate the square of the distance to the nearest center point among the selected center points; , where U represents the set of selected center points; c. Select a new data point uk as the new center, and the probability of selection is: ； d. If K cluster centers are selected, stop the iteration; otherwise return to a; S402, using the minimum weight matching algorithm to convert the Euler circuit: The input is a weighted undirected graph G = (V, E), where V is the vertex set, E is the edge set, and the weight of each edge (u, v) is w(u, v); the output is the minimum weight matching M, M is a matching on V, so that the sum of the weights of all edges in the matching is minimized. The execution process is: Initialization: M = empty set, U = V represents the unmatched vertex set; Calculate the weight matrix W between each vertex Repeat the following steps until all vertices are matched: Select an unmatched vertex u ∈ U; Select another vertex v ∈ U from the unmatched vertex set so that the weight w(u, v) of the edge (u, v) is minimized; Add (u, v) to the match M; Remove vertices u and v from U; Output matches M; S403, path planning using Fleury algorithm: The input is a connected undirected graph G = (V, E), where V is the vertex set and E is the edge set; the output is an Euler circuit or Euler path; the execution process is: Initialization: Select a vertex v from the graph G as the starting point. If there is an Euler path, the starting point must be one of the vertices with an odd degree; initialize an empty path P; Repeat the following steps until all edges have been traversed: At the current vertex v, select an edge e = (v, u) connected to v: If selecting edge e does not destroy connectivity, select that edge; If there are no other edges to choose from, then choose the only edge; Remove edge e from the graph and add vertex u to path P; Set the current vertex v to u; Output path P.

6. The method for nest deployment and flight planning for multi-target joint power inspection according to claim 1 is characterized in that: In step S5, generating a visualized nest deployment and flight trajectory planning scheme includes: The coordinates of the tower points are displayed in a two-dimensional Cartesian coordinate system. According to the drone nest site selection plan, the location of the nest is marked, and the towers are connected in sequence according to the path planning plan to form the final path planning map.

7. The method for nest deployment and flight planning for multi-target joint power inspection according to claim 1 is characterized in that: In step S6, evaluating the performance of the proposed algorithm includes: S601, obtaining the three-dimensional coordinate points of the tower; S602, using a minimum weight matching algorithm to calculate the minimum sum of weights of all edges; S603, performing path planning using Fleury algorithm; S604, setting the drone's own performance parameters.

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