Multi-uav path planning method, controller and storage medium

Abstract

The application discloses a multi-unmanned aerial vehicle path planning method, a controller and a storage medium, and the method comprises the steps of: dividing a target region based on flight parameters of unmanned aerial vehicles, and dividing the target region into a plurality of discrete grids; dividing the grids of the target region according to the number of unmanned aerial vehicles and the starting positions of the unmanned aerial vehicles, so as to obtain target sub-regions corresponding to the unmanned aerial vehicles respectively; and planning flight paths of the unmanned aerial vehicles in the corresponding target sub-regions, so that the flight paths of the unmanned aerial vehicles cover the corresponding target sub-regions. The application converts the multi-unmanned aerial vehicle path planning problem into the single-unmanned aerial vehicle path planning through the division of the target region, and reduces the difficulty of the multi-unmanned aerial vehicle path planning.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle path planning, and in particular to a multi-unmanned aerial vehicle path planning method, a controller and a computer readable storage medium. BACKGROUND

[0002] With the increase of the scale of photovoltaic power stations and the increase of the operation time, the failure problem of photovoltaic modules is becoming increasingly serious, which leads to the increasing operation and maintenance pressure of photovoltaic power stations. Research shows that the failure of photovoltaic modules itself accounts for a large proportion in the failure of photovoltaic power stations. The photovoltaic modules installed outdoors are long-term challenged by natural weather such as rain and snow, dust erosion and high-rise building shading, so that these modules are prone to various defects and failures. These problems not only affect the power generation capacity and efficiency of photovoltaic power stations, but also may cause various failures such as hot spots, breakage, cracks and component shedding, thereby reducing the power generation efficiency. In serious cases, these failures may even cause power station fires and other safety hazards.

[0003] Traditionally, the operation and maintenance of photovoltaic power stations mainly rely on manual inspection. However, manual inspection is low in efficiency and high in labor cost, especially in photovoltaic power stations with complex terrain, manual inspection is not only difficult to carry out, but also may bring safety risks. In contrast, due to the advantages of small size, light weight, low cost and flexibility, the use of unmanned aerial vehicles for inspection can significantly improve the inspection efficiency, reduce the operation and maintenance cost, and enhance the safety and reliability.

[0004] The principle of unmanned aerial vehicle inspection of photovoltaic power stations is that the unmanned aerial vehicle carries a visible light camera to complete the image acquisition of photovoltaic modules in the area to be inspected in the photovoltaic power station area by using a regional coverage path planning algorithm, and uses image processing technology to realize the detection and identification of photovoltaic module defects. However, when performing inspection of large photovoltaic power stations, due to the large area occupied by photovoltaic power stations and uneven distribution, a single unmanned aerial vehicle is insufficient in endurance and is difficult to complete the inspection at one time and may have omission problems. Therefore, the cooperation between multiple unmanned aerial vehicles is needed to make up for the shortcomings of a single unmanned aerial vehicle, and thus the path planning problem of multiple unmanned aerial vehicles becomes a technical problem to be solved in the field. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a multi-unmanned aerial vehicle path planning method, a controller and a computer readable storage medium, so as to convert the path planning problem of multiple unmanned aerial vehicles into the path planning problem of a single unmanned aerial vehicle by dividing the target area, and reduce the difficulty of path planning of multiple unmanned aerial vehicles.

[0006] In order to solve the above technical problems, according to one aspect of the present application, a multi-unmanned aerial vehicle path planning method is provided, comprising:

[0007] dividing the target area into a plurality of discrete grids based on flight parameters of the UAVs;

[0008] dividing the grids of the target area according to the number of the UAVs and the starting positions of the UAVs to obtain target sub-areas corresponding to the UAVs respectively;

[0009] planning flight paths of the UAVs in the corresponding target sub-areas, so that the flight paths of the UAVs cover the corresponding target sub-areas.

[0010] In some embodiments, the step of dividing the target area into a plurality of discrete grids based on flight parameters of the UAVs comprises:

[0011] calculating a field of view width of the UAV at a preset flight speed according to the flight parameters;

[0012] dividing the target area according to the field of view width to determine the side length of the grids to obtain a plurality of grids.

[0013] In some embodiments, the flight parameters comprise the preset flight height of the UAV and a field of view angle of a camera of the UAV;

[0014] The step of calculating the field of view width of the UAV at a preset flight speed according to the flight parameters comprises:

[0015] calculating a diagonal length of a matrix area covered by the field of view of the camera according to the preset flight height and the field of view angle;

[0016] calculating the field of view width based on an aspect ratio of an image captured by the camera and the diagonal length.

[0017] In some embodiments, the side length of the grid is equal to the field of view width multiplied by a preset multiple, wherein the preset multiple is less than or equal to 1.

[0018] In some embodiments, the step of dividing the grids of the target area according to the number of the UAVs and the starting positions of the UAVs comprises:

[0019] based on the number of the UAVs and the starting positions of the UAVs, constructing an evaluation matrix corresponding to each of the UAVs by using a DARP algorithm to realize the division of the target area to obtain a plurality of sub-areas with a number of grids being the same or similar;

[0020] introducing a reward and punishment matrix to integrate the grids of each of the sub-areas into a connected state to obtain a plurality of target sub-areas.

[0021] In some embodiments, at least one of the following constraints is introduced when dividing the plurality of target sub-regions:

[0022] The target sub-regions corresponding to the plurality of unmanned aerial vehicles do not overlap with each other, the target sub-regions corresponding to the plurality of unmanned aerial vehicles are capable of covering the target region, the number of the grids in the target sub-regions is the same or similar, the grids in each target sub-region are in a connected state, and the starting positions of the plurality of unmanned aerial vehicles are located in the target sub-regions corresponding to the plurality of unmanned aerial vehicles.

[0023] In some embodiments, the step of planning the flight path of each unmanned aerial vehicle in the corresponding target sub-region comprises:

[0024] The ant colony algorithm is improved based on the prior knowledge of straight-line priority and minimum number of turns when adjusting the movement rule;

[0025] The pheromone of the edges in the generated tree is enhanced based on the quality of the generated tree constructed by the improved ant colony algorithm;

[0026] A penalty coefficient is introduced to quantify the turn cost in path planning, thereby completing the planning of the flight path of each unmanned aerial vehicle.

[0027] In some embodiments, the target region includes obstacle grids having obstacles and free grids having no obstacles;

[0028] The flight path of the unmanned aerial vehicle passes through all the free grids of the target region and does not pass through the obstacle grids;

[0029] During the movement of the unmanned aerial vehicle, the unmanned aerial vehicle is only capable of moving from the center point of the current free grid to the center point of the grid sharing a side with the current free grid.

[0030] According to another aspect of the present application, a controller is provided, which comprises a memory and a processor, the memory stores a computer program, and the program, when executed by the processor, is capable of implementing the steps of the path planning method of the plurality of unmanned aerial vehicles according to any one of the above aspects.

[0031] According to still another aspect of the present application, a computer readable storage medium is provided for storing a computer program, and the program, when executed by a computer or a processor, is capable of implementing the steps of the path planning method of the plurality of unmanned aerial vehicles according to any one of the above aspects.

[0032] The present application has obvious advantages and beneficial effects compared with the prior art. By means of the above technical scheme, the multi-unmanned aerial vehicle path planning method, the controller and the computer readable storage medium can achieve considerable technical progress and practicability, and have wide industrial utilization value, and at least have the following advantages:

[0033] In the present application, the target region is divided into a plurality of target sub-regions, and each target sub-region corresponds to a single unmanned aerial vehicle. When planning the path of the plurality of unmanned aerial vehicles, the path planning of the plurality of unmanned aerial vehicles is converted into the path planning of the single unmanned aerial vehicle, thereby effectively simplifying the complexity of the path planning of the plurality of unmanned aerial vehicles and reducing the difficulty of the path planning of the plurality of unmanned aerial vehicles.

[0034] The above description is only a summary of the technical scheme of the present application. In order to more clearly understand the technical means of the present application, the content of the specification can be implemented, and in order to make the above and other purposes, characteristics and advantages of the present application more obvious and easy to understand, the following preferred embodiments are described in detail below, and the accompanying drawings are described as follows. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 The flowchart of the path planning method of the plurality of unmanned aerial vehicles of an embodiment of the present application is shown in the figure.

[0036] Figure 2 The schematic diagram of the grid of the target region of an embodiment of the present application is shown in the figure.

[0037] Figure 3 The schematic diagram of the field of view width of the unmanned aerial vehicle of an embodiment of the present application is shown in the figure.

[0038] Figure 4A The schematic diagram of the relative concentration of the starting position of the plurality of unmanned aerial vehicles in the target region of an embodiment of the present application is shown in the figure.

[0039] Figure 4B The schematic diagram of the relative dispersion of the starting position of the plurality of unmanned aerial vehicles in the target region of an embodiment of the present application is shown in the figure.

[0040] Figure 5A The schematic diagram of the division of the target sub-region when the starting position of the plurality of unmanned aerial vehicles is relatively concentrated of an embodiment of the present application is shown in the figure.

[0041] Figure 5B The schematic diagram of the division of the target sub-region when the starting position of the plurality of unmanned aerial vehicles is relatively dispersed of an embodiment of the present application is shown in the figure.

[0042] Figure 6A The schematic diagram of the division of the target sub-region when the starting position of the plurality of unmanned aerial vehicles is relatively concentrated of another embodiment of the present application is shown in the figure.

[0043] Figure 6B The schematic diagram of the division of the target sub-region when the starting position of the plurality of unmanned aerial vehicles is relatively dispersed of another embodiment of the present application is shown in the figure.

[0044] Figure 7 A schematic diagram of planning of a flight path of multiple unmanned aerial vehicles based on an STC algorithm;

[0045] Figure 8 A schematic diagram of planning of a flight path of multiple unmanned aerial vehicles based on an ACO-STC algorithm;

[0046] Figure 9 A schematic diagram of planning of a flight path of multiple unmanned aerial vehicles based on a KGACO-STC algorithm. DETAILED DESCRIPTION

[0047] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined inventive objectives, the specific embodiments of a path planning method of multiple unmanned aerial vehicles, a path planning system controller of multiple unmanned aerial vehicles and a computer readable storage medium according to the present application and their effects will be described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0048] The present application provides a path planning method of multiple unmanned aerial vehicles, as shown in the accompanying drawings, the path planning method of multiple unmanned aerial vehicles comprises: Figure 1 The path planning method of multiple unmanned aerial vehicles comprises:

[0049] In step S10, the target area is divided based on the flight parameters of the unmanned aerial vehicles, and the target area is divided into a plurality of discrete grids.

[0050] In this step, environmental modeling analysis is performed according to the characteristics of the multiple unmanned aerial vehicle inspection, and the grid method is used to model the environment of the target area to be inspected.

[0051] In the environmental modeling of the target area to be inspected by the multiple unmanned aerial vehicles, the core idea of the grid method is to subdivide the target area into a series of discrete grids, and define each grid as its environmental state characteristics. These characteristics reflect the specific environmental information represented by each grid. Although the multiple unmanned aerial vehicles actually fly in a three-dimensional space, it is necessary to maintain the consistency of the field of view of the on-board equipment of the multiple unmanned aerial vehicles, and it is specified that the multiple unmanned aerial vehicles fly in the same horizontal plane. At this time, the environmental modeling of the target area can be simplified as a grid of a two-dimensional plane. At the same time, since the grid is also used as a basic unit for flight path planning of the multiple unmanned aerial vehicles, the density of the grid division should be matched with the flight speed of the multiple unmanned aerial vehicles to ensure efficient and accurate execution of the inspection.

[0052] Figure 2 A schematic diagram of the gridding of the target area is shown. The target area is denoted as D, the size of the target area is L x ×L y , if the number of planned grids is m x m, then the grid side length (l x , l yThe calculation formula for ) is as shown in Equation (1-1), and any grid cell is defined as D(x, y) as shown in Equation (1-2):

[0053]

[0054] U={(x, y)|(x, y)∈D} (1-2)

[0055] In the above formula: U represents any grid cell within the target area, x∈(1,2,3…L) x ), y∈(1,2,3…L y ).

[0056] It should be noted that in some cases, obstacles may exist within the target area that hinder the flight of the UAV. Among the multiple grids obtained by rasterizing the target area, there will be obstacle grids with obstacles and free grids without obstacles. The obstacle grid is denoted by B, as shown in equation (1-3):

[0057] B = {(x, y) | (x, y) ∈ U and (x, y) is inaccessible} (1-3)

[0058] The free grid is represented by V, as shown in equation (1-4):

[0059] V = UB (1-4)

[0060] In one embodiment, step 10 includes:

[0061] Step 100: Calculate the field of view width of the UAV at the preset high speed of flight based on the flight parameters.

[0062] In this step, the field of view of the drone at the preset flight altitude is determined by the drone's flight altitude, the field of view angle of the gimbal camera, and the performance parameters of the visible light and infrared sensors.

[0063] Optionally, the drone's flight parameters include the drone's preset flight altitude and the drone's camera's field of view. Step 100 specifically includes:

[0064] The diagonal length of the matrix area covered by the camera's field of view is calculated based on the preset flight altitude and field of view angle; the width of the field of view is calculated based on the aspect ratio and diagonal length of the image captured by the camera.

[0065] like Figure 3 As shown, the drone's preset flight altitude is h, and the camera's field of view angle is θ. The drone's field of view width can be calculated based on the drone's preset flight altitude h and the camera's field of view angle θ.

[0066] Specifically, the diagonal length d of the matrix region covered by the camera's field of view is calculated using equation (1-5).

[0067]

[0068] Based on the aspect ratio and diagonal length d of the image captured by the camera, the field of view width l of the UAV is calculated by formula (1-6).

[0069]

[0070] In formula (1-6), the aspect ratio of the image captured by the camera is 16:9, and if a different aspect ratio is used, the numbers in the formula can be replaced, and the present application is not limited to a specific aspect ratio.

[0071] Step 102, the length of the field of view width determination grid is used to divide the target area to obtain a plurality of grids.

[0072] After the field of view width l of the UAV at the preset flight height is calculated by step 100, the length of the grid is determined as the length of the grid, and the target area is divided based on the length of the grid to obtain a plurality of rectangular grids.

[0073] When the UAV performs on-site inspection, due to natural environmental factors such as wind interference, the UAV may deviate from its predetermined flight path during flight, thereby causing inspection omission.

[0074] Therefore, the length of the grid of the target area is set as the field of view width l of the UAV multiplied by a preset multiple. The preset multiple is less than or equal to 1.

[0075] Preferably, the length of 0.7 times the field of view width l of the UAV is used as the length of a single grid, and the target area is then rasterized using a square grid.

[0076] Step S20, the grid of the target area is divided according to the number of UAVs and the starting position of each UAV to obtain a target sub-area corresponding to each UAV.

[0077] In this step, the DARP algorithm is used to divide the target area, and in the known terrain target area, the target sub-area corresponding to each UAV is reasonably divided according to the number of UAVs and the starting position of the UAV.

[0078] In an embodiment, step S20 includes:

[0079] Step 200, based on the number of UAVs and the starting position of each UAV, a DARP algorithm is used to construct an evaluation matrix corresponding to each UAV to divide the target area to obtain a plurality of sub-areas with the same or similar number of grids.

[0080] In this step, the DARP algorithm is used to construct an evaluation matrix E for each UAV according to the number and starting position of the UAVs, wherein the evaluation matrix of the ith UAV is represented as E i The evaluation matrix of the ith UAV is defined as formula (1-7).

[0081]

[0082] The sub-area A allocated to each UAV i The minimum value of E i can be obtained by solving, which is calculated by formula (1-8).

[0083]

[0084] To ensure that the divided sub-area meets the aforementioned conditions, ensure the fairness of the task allocation of each UAV, and ensure that the number of grids in each sub-area is roughly equal, formula (1-9) is used for quantification.

[0085]

[0086] f represents the global "fair share", when k1=k2=...=k n , formula (1-10) takes the minimum value, wherein k i represents the number of grids of the sub-area A i allocated to the ith UAV.

[0087]

[0088] In the solving process of the DARP algorithm, the evaluation matrix E needs to be appropriately modified, and a correction factor m i is introduced for optimization, as formula (1-11).

[0089] E i = m i E i (1-11)

[0090] Step 202, introduce a reward and punishment matrix to integrate the grids of each sub-area into a connected state to obtain multiple target sub-areas.

[0091] In this step, after the grids of the target area are equally divided by step 200, it cannot be guaranteed that each allocated sub-area is spatially connected. In order to solve this problem, a reward and punishment matrix is introduced, and the definition of the reward and punishment matrix is as formula (1-12).

[0092]

[0093] In the formula: R irepresents the grid assigned to the ith UAV, and S represents the starting position of the ith UAV i (t0) the set of grids within the same connected sub-region; Q i Conversely, is the set of grids not within the same connected sub-region as the starting position S i (t0) the set of grids not within the same connected sub-region as the starting position S

[0094] The role of the reward and punishment matrix is to reward the grids within the same connected sub-region as the starting position of the UAV, and to punish the grids assigned to the ith UAV but not within the same connected sub-region as the starting position of the ith UAV. By applying this reward and punishment mechanism, the purpose is to gradually integrate the sub-regions assigned to each UAV into a connected region, forming the target sub-region corresponding to each UAV. The update expression of the UAV evaluation matrix is defined as shown in equation (1-13).

[0095]

[0096] In summary, in the process of task allocation of the multi-UAV regional coverage task, the DARP algorithm allocates the grids in the task area to each UAV as evenly as possible according to the number of UAVs, thereby achieving balanced task allocation. At the same time, the allocation process takes into account the grid of the starting position of each UAV, ensuring that the set of grids finally assigned to each UAV includes its starting position, further optimizing the inspection efficiency and the completeness of coverage.

[0097] In an embodiment, in order to reasonably allocate the grids in the target region based on the number of UAVs, at least one of the following constraints is introduced when the target sub-region is divided:

[0098] (1) The target sub-regions corresponding to each of the UAVs do not overlap with each other, in order to prevent mutual collision between UAVs and avoid repeated coverage of the same region.

[0099]

[0100] (2) The target sub-regions corresponding to multiple UAVs can cover the target region.

[0101]

[0102] (3) The number of grids in the target sub-region is the same or similar, in order to fully utilize the advantages of the multi-UAV inspection system and balance the task burden.

[0103]

[0104] (4) The grids in each target sub-region are in a connected state, ensuring that the grids in the target sub-region allocated to each UAV form a compact connected region, reducing the time and energy consumed by the UAV when moving between scattered sub-regions.

[0105] Sub-region A i is continuous,

[0106] (5) The starting positions of each UAV are in the target sub-region corresponding to the UAV, so as to facilitate inspection from the starting position of the UAV.

[0107]

[0108] Step S30, planning a flight path for each UAV in the corresponding target sub-region, so that the flight path of each UAV covers the corresponding target sub-region.

[0109] In this step, the division of the target sub-region is based on the starting position of the UAV, simplifying the path planning problem of multi-UAV coverage inspection to a single-UAV coverage inspection problem. The ant colony generated tree coverage algorithm based on prior knowledge effectively solves the single-UAV coverage inspection problem.

[0110] The turning operation of multi-UAV consumes more energy, and frequent turning also reduces the inspection efficiency. To solve the problem that the structure of the generated tree is not unique, resulting in different number of turns for multi-UAV, an algorithm based on KGACO is proposed to optimize the STC structure, introducing a penalty for the number of turns and a reward for the consistency of direction to generate a more efficient and straighter flight path, and to reduce the number of turns as much as possible, thereby improving the safety of flight and reducing energy consumption.

[0111] In an embodiment, step S30 comprises:

[0112] Step 300, introducing prior knowledge based on straight line priority and minimum number of turns to improve the ant colony algorithm when adjusting the movement rule.

[0113] In this step, the improved ant colony algorithm introduces prior knowledge based on straight line priority and minimum number of turns when adjusting the movement rule, as shown in equation (1-19). The tendency to reduce the number of turns makes the movement of the ants more inclined to straight line paths.

[0114]

[0115] θ ij = exp(-λΔθ) (1-19)

[0116] Step 302, based on the quality of the generated tree constructed by the improved ant colony algorithm, the pheromone of the edges in the generated tree is enhanced.

[0117] In this step, after each iteration, the pheromone of the edges in the tree is enhanced according to the quality of the spanning tree constructed by the ants. This process preferentially rewards those paths that help form straight lines and reduce turns by increasing the concentration of pheromones on these paths, guiding the search direction of subsequent ants. As formula (1-20):

[0118]

[0119] Step 304, introduce a penalty coefficient to quantify the cost of turning in path planning, to complete the planning of the flight path of each UAV.

[0120] In this step, a turning penalty coefficient (such as formula 1-21) is introduced to quantify the cost of turning, which increases exponentially with the increase of turning angle. At the same time, the heuristic information of direction consistency is also considered to promote the ants to prefer to move in the current direction when choosing paths, further reducing turns. Through such design, the ant colony algorithm can naturally prefer straight flight and reduce turns while searching for the shortest path, so as to optimize the whole path planning and form the flight path of each UAV.

[0121]

[0122] In this embodiment, an algorithm based on KGACO is proposed to optimize the STC structure, and a turning penalty and a direction consistency reward are introduced to generate more efficient and more linear flight paths and reduce turns as much as possible, thereby improving flight safety and reducing energy consumption.

[0123] In an embodiment, the UAV can only move from the center point of the current free grid to the center point of the grid sharing the edge with the current free grid during movement.

[0124] Specifically, when performing area coverage tasks, the multi-UAV system requires accurate position information of each UAV, including its starting position. This means that the specific grid position of each UAV must be clear. In addition, the regulation requires that the UAV can only move from the center point of the current grid to the center point of the adjacent grid sharing the edge with the current grid (up, down, left, right) each time. That is, the grid (x i , y i ) and the grid (x j , y j ) are defined as adjacent grids.

[0125] ||x i -x j ||+||y i -y j||≤1 (1-22)

[0126] The flight path S of the multiple UAVs in the target region successively passes through the center points of multiple grids, which can be represented by the grid coordinate sequence it passes through as:

[0127] S = ((x1, y1), (x2, y2),..., (x |s| , y |s| )) (1-23)

[0128] Wherein: And any two consecutive grids (x i , y i ) and (x i+1 , y i+1 ) on S satisfy the definition of adjacent grids in formula (1-22) above.

[0129] Taking a large photovoltaic power station in a certain place as the inspection object, the total number of inspection UAVs is n, and the coordinates of each UAV are defined as follows when the UAV moves one step:

[0130]

[0131] Wherein: t represents the step number of the current UAV movement, and the initial step number is t0.

[0132] In an embodiment, the target region includes obstacle grids with obstacles and free grids without obstacles, and the flight path of the UAV passes through all the free grids of the target region and does not pass through the obstacle grids.

[0133] The flight path length of each UAV covering the target sub-region can be represented by the total number of free grids it passes through. The core objective of the present application is to optimize and minimize the path length of each UAV in its target sub-region while strictly avoiding the path crossing the obstacle grids provided with obstacles. This requires planning the flight route of each UAV to ensure efficient coverage of the allocated region while performing the task without being hindered by any obstacles, as follows:

[0134]

[0135] Wherein, |S i | represents the length of the flight path S i of the target region of the i-th UAV.

[0136] In an embodiment, a multi-UAV coverage inspection path planning algorithm based on KGACO-STC of DARP is proposed, and the steps are as follows:

[0137] Step 1: Discretization of photovoltaic power station region. The target photovoltaic power station region is divided into a grid model.

[0138] Step two, perform inspection. According to the specified number of drones to perform the photovoltaic power station area of coverage inspection.

[0139] Step three, inspection area division. According to the starting position of each drone, first initialize the evaluation matrix E of each drone, and then preliminarily divide the inspection area.

[0140] Step four, inspection area division judgment. If the area division is completed, generate an inspection area allocation matrix A, and enter step six to directly use the spanning tree algorithm to solve the single drone coverage inspection problem. If not, go to step five.

[0141] Step five, optimization. If the area division cannot ensure that the area of each sub-region is roughly equal and continuous, it needs to be adjusted and optimized, and then jump to step four.

[0142] Step six, for obstacles in the environment, an approximate processing method is adopted, that is, each square grid is either covered by an obstacle to form an obstacle grid or a free grid.

[0143] Step seven, spanning tree construction and flight path generation. After defining the photovoltaic power station environment, the improved ant colony algorithm and STC optimization are applied to construct the spanning tree and plan the flight path of the drone.

[0144] Simulation verification

[0145] (1) Multi-drone area division simulation verification

[0146] The DARP algorithm is simulated to study the influence of the number and starting position of the drones on the target area division. The simulation environment is set to 20x20 large grids, that is, 40x40 detection cells, and the white grid is a free grid and the black grid is an obstacle grid.

[0147] Figure 4A shows the case where multiple drones start from a relatively concentrated initial position, while Figure 4B shows the case where multiple drones start from a relatively dispersed initial position. The DARP area division algorithm is used to divide the target area of Figure 5, and the results of the area division are shown in Figure 5. Among them, multiple drones start from a relatively concentrated initial position, as shown in Figure 5A , it takes 582ms to complete the target area coverage; while multiple drones start from a relatively dispersed initial position, as shown in Figure 5BAs shown, it only takes 96 ms to complete the target area coverage. This difference shows that the relatively dispersed starting positions of the multiple UAVs help to improve the efficiency of the area division, and make the divided areas more regular, which also facilitates the subsequent area coverage using the spanning tree algorithm. Further experiments of adjusting the number of UAVs show that reducing the number of UAVs (from 4 in Fig. 5 to 3) leads to a reduction in the time consumption of area division, as shown in Fig. 6. Figure 6A Time consumption: 425 ms, Figure 6B Time consumption: 67 ms, and Figure 5B In comparison, reducing the number of UAVs can reduce the running time of the DARP algorithm, however, this means that each UAV needs to patrol a larger area of the target sub-area. In summary, the efficiency of the multi-UAV area division algorithm is significantly affected by the starting positions and the number of UAVs. The dispersed starting positions and the fewer number of UAVs are more helpful to improve the efficiency of the area division.

[0148] (2) Simulation experiment of multi-UAV photovoltaic patrol area coverage path planning

[0149] The simulation experiment environment is set to 20x20 large grids, i.e. 40x40 detection cells, and the white grids are free grids and the black grids are obstacle grids. The KGACO-STC algorithm parameters are set as follows: number of ants = 100, a = 2, b = 2, p = 0.1, weight e = 2, direction consistency weight Turning penalty coefficient C = 1, iteration number is 100, and the number of UAVs is set to 4. In this experiment, the area division after the DARP algorithm is used Figure 5B The black grid in the figure is the starting position grid of each UAV, and the starting position grid coordinates are (1, 2), (1, 13), (2, 20), and (16, 20). The black grid represents an obstacle grid, and the grids of different colors represent the target sub-area allocated to different UAVs.

[0150] After the patrol area allocation is completed, different methods are used to generate the STC path. The STC path generated by the minspantree function of MATLAB is as shown in Figure 7 The patrol path based on the ACO-STC algorithm is as shown in Figure 8 The patrol path based on the KGACO-STC algorithm is as shown in Figure 9 , wherein different colors represent the patrol paths of each UAV.

[0151] Another embodiment of the application is a controller comprising a memory and a processor, the memory storing a computer program, the program being capable of implementing the steps of the multi-UAV path planning method of any embodiment when executed by the processor.

[0152] A computer readable storage medium according to another embodiment of the present application is configured to store a computer program, which, when executed by a computer or a processor, implements the steps of the path planning method for multiple unmanned aerial vehicles according to any one of the embodiments.

[0153] The present application establishes a target area coverage environment model based on a grid method through modeling analysis of multiple unmanned aerial vehicle inspection environments, and completes target area gridding processing by comprehensively considering flight height of multiple unmanned aerial vehicles, field of view range of a gimbal camera and other parameters, and establishes a mathematical model taking the path of a single unmanned aerial vehicle covering its target sub-area as an objective function. The target area is divided into several target sub-areas by using a DARP algorithm, the multiple unmanned aerial vehicle flight path planning problem is converted into a single unmanned aerial vehicle flight path planning problem, and the influence of the number and starting position of multiple unmanned aerial vehicles on area division is studied. For the single unmanned aerial vehicle area coverage path planning problem, a path planning algorithm based on STC is proposed, and considering that the diversity of spanning tree structure may increase the number of path turns, affecting the inspection efficiency and energy consumption of the unmanned aerial vehicle, a KGACO-STC path planning algorithm is proposed to optimize the STC structure. MATLAB is used for example analysis, and the experimental results show that the proposed algorithm can achieve the shortest path and the least number of turns under the condition of full coverage of the path.

[0154] The above description is only a preferred embodiment of the present application, and is not intended to limit the present application in any form. Although the present application has been disclosed as above with reference to the preferred embodiment, it is not intended to limit the present application, and any person skilled in the art can make some changes or modifications to the above disclosed technical content without departing from the scope of the technical solution of the present application, and any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present application still belong to the scope of the technical solution of the present application.

Claims

1. A method for path planning of multiple unmanned aerial vehicles, characterized in that, The method comprises the following steps: dividing a target area into a plurality of discrete grids based on flight parameters of a UAV; dividing the grids of the target area according to the number of UAVs and the starting positions of the UAVs to obtain target sub-areas corresponding to the UAVs respectively; introducing prior knowledge based on straight-line priority and minimum number of turns to improve the ant colony algorithm when adjusting the movement rules, as follows: θ ij = exp(-λΔθ); enhancing pheromone of edges in the generated tree based on the improved ant colony algorithm, as follows: introducing a penalty coefficient to quantify the turn cost in path planning to complete the planning of flight paths of the UAVs, so that the flight paths of the UAVs cover the corresponding target sub-areas, and the penalty coefficient is as follows: 2.The method of claim 1, wherein, The step of dividing the target area into a plurality of discrete grids based on flight parameters of a UAV comprises the following steps: calculating the field of view width at the preset flight height of the UAV according to the flight parameters; dividing the target area according to the field of view width to determine the side length of the grid to obtain a plurality of grids. 3.The method of claim 2, wherein, The flight parameters include the preset flight height of the UAV and the field of view angle of the camera of the UAV; The step of calculating the field of view width at the preset flight height of the UAV according to the flight parameters comprises the following steps: calculating the diagonal length of the matrix area covered by the field of view of the camera according to the preset flight height and the field of view angle; calculating the field of view width based on the aspect ratio of the image captured by the camera and the diagonal length. 4.The method of claim 2, wherein, The side length of the grid is equal to the field of view width multiplied by a preset multiple, wherein the preset multiple is less than or equal to 1. 5.The method of claim 1, wherein, The step of dividing the grids of the target area according to the number of UAVs and the starting positions of the UAVs comprises the following steps: based on the number of UAVs and the starting positions of the UAVs, constructing an evaluation matrix corresponding to each UAV respectively by using a DARP algorithm to realize the division of the target area to obtain a plurality of sub-areas with the same or similar number of grids; introducing a reward and punishment matrix to integrate the grids of each sub-area into a connected state to obtain a plurality of target sub-areas. 6.The method of claim 1, wherein, When dividing a plurality of target sub-areas, at least one of the following constraints is introduced: the target sub-areas corresponding to each UAV do not overlap with each other, the target sub-areas corresponding to a plurality of UAVs can cover the target area, the number of grids in the target sub-area is the same or similar, the grids in each target sub-area are in a connected state, and the starting positions of each UAV are in the target sub-area corresponding to each UAV. 7.The method of claim 1, wherein, The target area includes obstacle grids with obstacles and free grids without obstacles; The flight path of the UAV passes through all the free grids of the target area and does not pass through the obstacle grids. The drone is only able to move from the center point of a current free grid to the center point of a grid that shares a side with the current free grid during movement.

8. A controller comprising a memory and a processor, said memory storing a computer program which, when executed by the processor, implements the steps of the method of any one of claims 1 to 7.

9. A computer readable storage medium storing a computer program which, when executed by a computer or processor, implements the steps of the method of any one of claims 1 to 7.

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