Path planning method for multiple unmanned aerial vehicles, controller and storage medium

Abstract

The invention discloses a path planning method for multiple unmanned aerial vehicles, a controller and a storage medium, and the method comprises the steps: dividing a target region based on the flight parameters of the unmanned aerial vehicles, and dividing the target region into a plurality of discrete grids; dividing grids of the target area according to the number of the unmanned aerial vehicles and the initial position of each unmanned aerial vehicle so as to obtain target sub-areas corresponding to each unmanned aerial vehicle; and planning a flight path of each unmanned aerial vehicle in the corresponding target sub-region, so that the flight path of each unmanned aerial vehicle covers the corresponding target sub-region. According to the invention, by dividing the target area, the path planning problem of multiple unmanned aerial vehicles is converted into the path planning problem of a single unmanned aerial vehicle, and the path planning difficulty of multiple unmanned aerial vehicles is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) path planning, and in particular to a multi-UAV path planning method, a controller and a computer-readable storage medium. Background Art

[0002] As the scale of photovoltaic power stations increases and the number of years of operation increases, the failure of photovoltaic modules is becoming more and more serious, resulting in increasing pressure on the operation and maintenance of photovoltaic power stations. Studies have shown that the failure of photovoltaic modules themselves accounts for a large proportion of the failures of photovoltaic power stations. Photovoltaic modules installed outdoors have long faced the challenges of natural weather, such as rain, snow, dust erosion, and the obstruction of high-rise buildings, making these modules very prone to various defects and failures. These problems not only affect the power generation and efficiency of photovoltaic power stations, but may also cause hot spots, cracks, cracks, and module shedding, thereby reducing power generation efficiency. In severe cases, these failures may even cause safety hazards such as power station fires.

[0003] Traditionally, the operation and maintenance of photovoltaic power plants mainly rely on manual inspections. However, manual inspections are inefficient and labor-intensive, especially in photovoltaic power plants with complex terrains. Manual inspections are not only difficult to conduct, but may also bring safety risks. In contrast, drones can significantly improve inspection efficiency, reduce operation and maintenance costs, and enhance safety and reliability due to their small size, light weight, low cost, and flexibility.

[0004] The principle of drone inspection of photovoltaic power stations is that the drone equipped with a visible light camera completes the image acquisition of photovoltaic modules in the area of ​​the photovoltaic power station to be inspected through a path planning algorithm with regional coverage, and uses image processing technology to detect and identify defects in photovoltaic modules. However, when performing inspections of large photovoltaic power stations, due to the large area and uneven distribution of photovoltaic power stations, a single drone is somewhat insufficient due to its endurance, making it difficult to complete the inspection in one go and there may be omissions. The inadequacy of a single drone can be compensated by the collaborative cooperation between multiple drones, so the path planning problem of multiple drones has become a technical problem that needs to be solved urgently in this field. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a path planning method, a controller and a computer-readable storage medium for multiple UAVs, so as to convert the path planning problem of multiple UAVs into the path planning of a single UAV by dividing the target area, thereby reducing the difficulty of path planning for multiple UAVs.

[0006] In order to solve the above technical problems, according to one aspect of the present invention, a path planning method for multiple UAVs is provided, comprising:

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

[0008] Dividing the grid of the target area according to the number of the drones and the starting position of each drone to obtain target sub-areas corresponding to each drone;

[0009] Plan the flight path of each UAV in the corresponding target sub-area so that the flight path of each UAV covers the corresponding target sub-area.

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

[0011] Calculating the field of view width of the drone at a preset high flight speed according to the flight parameters;

[0012] The target area is divided by determining the side length of the grid according to the width of the field of view to obtain a plurality of grids.

[0013] In some embodiments, the flight parameters include the preset flight altitude of the drone and the field of view angle of the camera of the drone;

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

[0015] Calculating the diagonal length of the matrix area covered by the camera's field of view according to the preset flight altitude and the field of view angle;

[0016] The field of view width is calculated 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, where the preset multiple is less than or equal to 1.

[0018] In some embodiments, the step of dividing the grid of the target area according to the number of the drones and the starting position of each of the drones includes:

[0019] Based on the number of the drones and the starting position of each drone, the DARP algorithm is used to construct an evaluation matrix corresponding to each drone, so as to divide the target area to obtain multiple sub-areas with the same or similar number of grids;

[0020] A reward and punishment matrix is ​​introduced to integrate the grids of each sub-region into a connected state, thereby obtaining a plurality of target sub-regions.

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

[0022] The target sub-areas corresponding to the various drones do not overlap with each other, the target sub-areas corresponding to multiple drones can cover the target area, the number of grids in the target sub-areas is the same or similar, the grids in each target sub-area are connected, and the starting position of each drone is within the target sub-area corresponding to each drone.

[0023] In some embodiments, the step of planning the flight path of each drone within the corresponding target sub-area includes:

[0024] When adjusting the movement rules, the ant colony algorithm is improved by introducing prior knowledge based on straight line priority and minimum number of turns;

[0025] Based on the quality of the spanning tree constructed by the improved ant colony algorithm, performing pheromone enhancement on the edges in the spanning tree;

[0026] A penalty coefficient is introduced to quantify the turning cost in path planning to complete the planning of the flight path of each UAV.

[0027] In some embodiments, the target area includes an obstacle grid with obstacles and a free grid without obstacles;

[0028] The flight path of the UAV passes through all the free grids of the target area and does not pass through the obstacle grid;

[0029] During the movement of the drone, it can only move from the center point of the current free grid to the center point of the grid that shares a common edge with the current free grid.

[0030] According to another aspect of the present invention, a controller is provided, which includes a memory and a processor, wherein the memory stores a computer program, and when the program is executed by the processor, the steps of the multi-UAV path planning method described in any one of the above items are implemented.

[0031] According to another aspect of the present invention, a computer-readable storage medium is provided for storing a computer program, wherein the program, when executed by a computer or a processor, implements the steps of any one of the above-described methods for path planning of multiple UAVs.

[0032] Compared with the prior art, the present invention has obvious advantages and beneficial effects. By means of the above technical solution, the multi-UAV path planning method, controller and computer-readable storage medium of the present invention can achieve considerable technical advancement and practicality, and have wide industrial utilization value, and have at least the following advantages:

[0033] In the present invention, the target area is divided into several target sub-areas, and the target sub-areas correspond to a single UAV respectively. Then, when planning the paths of multiple UAVs, they are converted into path planning for a single UAV, which effectively simplifies the complexity of path planning for multiple UAVs and reduces the difficulty of path planning for multiple UAVs.

[0034] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the following specifically cites a preferred embodiment and describes it in detail with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A schematic diagram of a flow chart of a method for path planning of multiple UAVs according to an embodiment of the present invention;

[0036] Figure 2 A schematic diagram of rasterizing a target area according to an embodiment of the present invention;

[0037] Figure 3 A schematic diagram of the field of view width of a drone according to an embodiment of the present invention;

[0038] Figure 4A This is a schematic diagram of a plurality of UAVs having relatively concentrated starting positions in a target area according to an embodiment of the present invention;

[0039] Figure 4B A schematic diagram of relatively dispersed starting positions of multiple UAVs in a target area according to an embodiment of the present invention;

[0040] Figure 5A This is a schematic diagram of dividing the target sub-areas when the starting positions of multiple UAVs are relatively concentrated according to an embodiment of the present invention;

[0041] Figure 5B This is a schematic diagram of dividing target sub-areas when the starting positions of multiple UAVs are relatively dispersed according to an embodiment of the present invention;

[0042] Fig. 6A This is a schematic diagram of dividing target sub-areas when the starting positions of multiple UAVs are relatively concentrated according to another embodiment of the present invention;

[0043] Figure 6B This is a schematic diagram of dividing target sub-areas when the starting positions of multiple UAVs are relatively dispersed according to another embodiment of the present invention;

[0044] Figure 7 This is a schematic diagram of multi-UAV flight path planning based on the STC algorithm;

[0045] Figure 8 This is a schematic diagram of multi-UAV flight path planning based on the ACO-STC algorithm;

[0046] Fig. 9 Schematic diagram of multi-UAV flight path planning based on KGACO-STC algorithm. DETAILED DESCRIPTION

[0047] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the following, in combination with the accompanying drawings and preferred embodiments, a specific implementation method and effects of a multi-UAV path planning method, a multi-UAV path planning system controller and a computer-readable storage medium proposed in the present invention are described in detail as follows.

[0048] The present invention proposes a path planning method for multiple UAVs, such as Figure 1 As shown, the path planning method for multiple UAVs includes:

[0049] Step S10, dividing the target area into a plurality of discrete grids based on the flight parameters of the UAV.

[0050] In this step, environmental modeling and analysis are performed based on the characteristics of multi-UAV inspections, and the grid method is used to model the environment of the target area that needs to be inspected.

[0051] In the target area environmental modeling of multi-UAV inspection, 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 multiple UAVs actually fly in three-dimensional space, considering the need to maintain the consistency of the field of view of the onboard equipment of multiple UAVs, multiple UAVs are required to fly on the same horizontal plane. At this point, the environmental modeling of the target area can be simplified to the rasterization of a two-dimensional plane. At the same time, given that the grid is also the basic unit for multi-UAV flight path planning, the density of its grid division should match the flight speed of the multi-UAV to ensure efficient and accurate inspection.

[0052] Figure 2 The target area is represented by D, and the size of the target area is L. x ×L y , if the number of planned grids is m×m, then the grid side length (l x , l y) is calculated as formula (1-1), and any grid is defined as D(x, y) as formula (1-2):

[0053]

[0054] U={(x, y)|(x, y)∈D} (1-2)

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

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

[0057] B = {(x, y)|(x, y)∈U and (x, y) is not accessible} (1-3)

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

[0059] V=UB (1-4)

[0060] In one embodiment, step 10 includes:

[0061] Step 100, calculating the field of view width of the drone at a preset high flight speed according to the flight parameters.

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

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

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

[0065] like Figure 3 As shown, the preset flight height of the drone is h, and the field of view angle of the camera is θ. According to the preset flight height h of the drone and the field of view angle θ of the camera, the field of view width of the drone can be calculated.

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

[0067]

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

[0069]

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

[0071] Step 102 , dividing the target area by the side length of the grid determined by the width of the field of view to obtain a plurality of grids.

[0072] After calculating the field of view width l of the drone at the preset flight altitude through step 100, the field of view width l is determined as the side length of the grid, and the target area is divided based on the side length of the grid to obtain multiple rectangular grids.

[0073] When drones perform field inspections, natural environmental factors, such as wind interference, may cause the drones to deviate from their intended flight path during flight, resulting in inspection omissions.

[0074] Based on this, the side length of the grid of the target area is set to be the width of the UAV's field of view l multiplied by a preset multiple, wherein the preset multiple is less than or equal to 1.

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

[0076] Step S20 , dividing the target area grid according to the number of drones and the starting position of each drone to obtain target sub-areas corresponding to each drone.

[0077] In this step, the target area is divided based on the DARP algorithm. In the target area with known terrain, the target sub-area corresponding to each drone is reasonably divided according to the number of drones and the starting position of the drones.

[0078] In one embodiment, step S20 includes:

[0079] Step 200, based on the number of drones and the starting position of each drone, the DARP algorithm is used to construct an evaluation matrix corresponding to each drone, so as to divide the target area to obtain multiple 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 drone according to the number of drones and the starting position, where the evaluation matrix of the i-th drone is represented by E i , the evaluation matrix of the i-th UAV is defined as formula (1-7).

[0081]

[0082] Each drone is assigned a sub-area A i By solving E i The minimum value of is obtained and calculated by formula (1-8).

[0083]

[0084] In order to ensure that the divided sub-areas meet the above conditions, guarantee the fairness of task allocation for each UAV, and ensure that the number of grids in each sub-area is roughly equal, quantification is performed using formula (1-9).

[0085]

[0086] f represents the global "fair share", when k1 = k2 = ... = k n , formula (1-10) takes the minimum value, where k i represents the sub-area A assigned to the i-th drone i The number of grids.

[0087]

[0088] In the process of solving the DARP algorithm, the evaluation matrix E needs to be appropriately corrected by introducing the correction factor m. i Optimize as shown in formula (1-11).

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

[0090] Step 202 , introducing a reward and punishment matrix to integrate the grids of each sub-region into a connected state, thereby obtaining a plurality of target sub-regions.

[0091] In this step, after the grid of the target area is equally divided through 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. The definition of the reward and punishment matrix is ​​as shown in formula (1-12).

[0092]

[0093] Where: R iRepresents the grid assigned to the i-th UAV and the starting position S of the i-th UAV i (t0) grid set in the same connected sub-region; Q i On the contrary, it is the starting position S of the i-th UAV i (t0) A set of grids that are not in the same connected sub-region.

[0094] The role of the reward and penalty matrix is ​​to reward those grids that are in the same connected sub-region as the starting position of the drone, and to punish those grids that are assigned to the i-th drone but are not in the same connected sub-region as its starting position. By applying this reward and penalty mechanism, the goal is to gradually integrate the sub-regions assigned to each drone into a connected region, forming the target sub-region corresponding to each drone. The update expression of the drone evaluation matrix is ​​defined as shown in formula (1-13).

[0095]

[0096] In summary, in the process of allocating multi-UAV area coverage tasks, 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 a balanced task allocation. At the same time, the allocation process takes into account the grids of the starting positions of each UAV, ensuring that the grid set finally allocated to each UAV includes its starting position, further optimizing the inspection efficiency and coverage integrity.

[0097] In one embodiment, in order to reasonably allocate grids in the target area based on the number of drones, at least one of the following constraints is introduced when dividing the target sub-areas:

[0098] (1) The target sub-areas corresponding to the drones do not overlap with each other, so as to prevent collisions between drones and avoid repeated coverage of the same area.

[0099]

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

[0101]

[0102] (3) The number of grids in the target sub-area is the same or similar 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-area are connected, ensuring that the grids in the target sub-area assigned to each UAV form a tightly connected area, thus reducing the time and energy consumed by the UAV when moving between scattered sub-areas.

[0105] Sub-area A i is continuous,

[0106] (5) The starting position of each UAV is within the target sub-area corresponding to each UAV, so that inspection can be started from the starting position of the UAV.

[0107]

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

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

[0110] The turning operation of multiple UAVs will consume more energy, and frequent turning will also reduce the inspection efficiency. In order to solve the problem that the structure of the spanning tree is not unique, resulting in different numbers of turns of multiple UAVs, an algorithm based on KGACO is proposed to optimize the STC structure. The penalty of the number of turns and the reward of direction consistency are introduced to generate a more efficient and linear flight path, and to reduce turns as much as possible, thereby improving flight safety and reducing energy consumption.

[0111] In one embodiment, step S30 includes:

[0112] Step 300, when adjusting the movement rules, the ant colony algorithm is improved by introducing the prior knowledge based on straight line priority and minimum number of turns.

[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 rules, such as formula (1-19), to reduce the tendency of turning and make the movement of ants more inclined to straight line paths.

[0114]

[0115] θ ij =exp(-λΔθ) (1-19)

[0116] Step 302: Based on the quality of the spanning tree constructed by the improved ant colony algorithm, pheromone enhancement is performed on the edges in the spanning tree.

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

[0118]

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

[0120] In this step, a turning penalty coefficient (such as formula 1-21) is introduced to quantify the cost of turning. This penalty increases exponentially with the increase of the turning angle. At the same time, the heuristic information of directional consistency is also considered to encourage ants to tend to move in the current direction when selecting a path, further reducing turns. Through such a design, the ant colony algorithm can naturally tend to fly in a straight line and reduce turns while searching for the shortest path, thereby achieving the purpose of optimizing the entire path planning and forming the flight path of each drone.

[0121]

[0122] In this embodiment, a KGACO-based algorithm is proposed to optimize the STC structure, introduce a penalty for the number of turns and a reward for directional consistency to generate a more efficient and linear flight path, and minimize turns, thereby improving flight safety and reducing energy consumption.

[0123] In one embodiment, during the movement of the drone, it can only move from the center point of the current free grid to the center point of the grid that shares the same edge with the current free grid.

[0124] Specifically, when performing area coverage tasks, the multi-UAV system requires accurate knowledge of the location information of each UAV, including its starting position. This means that the specific grid location of each UAV must be clear. In addition, the regulations require that each time a UAV moves, it can only move directly from the center point of the current grid to the center point of the adjacent grid with common edges around it (up, down, left, and right). That is, the grid (x) that satisfies equation (1-22) i ,y i ) and grid (x j ,y j ) is defined as an adjacent grid.

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

[0126] The flight paths S of multiple UAVs in the target area continuously pass through the center points of multiple grids, which can be expressed as the grid coordinate sequence they pass through:

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

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

[0129] A large photovoltaic power station in a certain place is taken as the inspection object. The total number of inspection drones is n. Every time the drone moves one step, the coordinates of each drone are defined as follows:

[0130]

[0131] Where: t represents the number of steps of the current UAV movement, and the number of steps at the initial moment is t0.

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

[0133] The length of the flight path of each drone covering the target sub-area can be represented by the total number of free grids it passes through. The core goal of the present invention is to optimize and minimize the path length of each drone in its target sub-area, while strictly avoiding the path from crossing the obstacle grid with obstacles. This requires planning the flight path of each drone to ensure that while performing the mission, it can efficiently cover the assigned area without being hindered by any obstacles. The formula is as follows:

[0134]

[0135] Among them, |S i | represents the flight path S of the target area of ​​the i-th UAV i Length.

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

[0137] Step 1: Discretize the photovoltaic power station area. Divide the target photovoltaic power station area into a grid model.

[0138] Step 2: Perform inspections. Perform coverage inspections of the photovoltaic power station area according to the specified number of drones.

[0139] Step 3: Inspection area division: Based on the starting position of each UAV, the evaluation matrix E of each UAV is initialized first, and then the inspection area is preliminarily divided.

[0140] Step 4: Inspection area division judgment. If the area division is completed, generate an inspection area allocation matrix A and proceed to step 6 to directly use the spanning tree algorithm to solve the single drone coverage inspection problem. If not completed, proceed to step 5.

[0141] Step 5: Solve and optimize. If the area division fails to ensure that the area of ​​each sub-area is roughly equal and continuous, it is necessary to readjust and optimize, and then jump to step 4.

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

[0143] Step 7: Spanning tree construction and flight path generation. After defining the PV 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-UAV area division simulation verification

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

[0147] Figure 4A shows the situation where multiple drones start from relatively concentrated initial positions, while Figure 4B The DARP region partitioning algorithm is used to partition the target area in Figure 5. The region partitioning result is shown in Figure 5. Among them, multiple drones start from relatively concentrated initial positions, such as Figure 5A As shown in Figure 2, it takes 582ms to complete the coverage of the target area. Multiple drones start from relatively scattered starting positions, such as Figure 5BAs shown in Figure 6, it only takes 96ms to complete the target area coverage. This difference shows that the relatively scattered starting positions of multiple drones help improve the efficiency of area division and make the divided areas more regular, which is also convenient for subsequent regional coverage using the spanning tree algorithm. Further experiments to adjust the number of drones show that reducing the number of drones (from 4 to 3 in Figure 5) leads to a reduction in the time consumption of regional division, as shown in Figure 6. Fig. 6A It takes 425ms. Figure 6B It takes 67ms, and Figure 5B In comparison, reducing the number of drones can reduce the running time of the DARP algorithm. However, this means that each drone needs to inspect a larger target sub-area. In summary, the efficiency of the multi-drone region partitioning algorithm is significantly affected by the starting position and number of drones. Drones with dispersed starting positions and fewer numbers are more conducive to improving the efficiency of region partitioning.

[0148] (2) Simulation experiment on path planning for multi-UAV photovoltaic inspection area coverage

[0149] The simulation experiment environment is set to a 20×20 large grid, that is, 40×40 detection cells, the white grid is a free grid, and the black grid is an obstacle grid. Among them, the KGACO-STC algorithm parameters are set as follows: number of ants = 100, α = 2, β = 2, ρ = 0.1, weight ε = 2, direction consistency weight The penalty coefficient of turning is C=1, the number of iterations is 100, and the number of drones is set to 4. Figure 5B The black grid in the figure is the starting position grid of each UAV. The starting position grid coordinates are (1,2), (1,13), (2,20), and (16,20). The black grid represents the obstacle grid, and grids of different colors represent the target sub-areas assigned to different UAVs.

[0150] After the inspection area is allocated, different methods are used to generate STC paths. The STC path is generated by the minspantree function provided by MATLAB. Figure 7 The inspection path based on the ACO-STC algorithm is shown in Figure 8 As shown; the inspection path based on KGACO-STC algorithm is as follows Fig. 9 As shown, different colors represent the inspection paths of each drone.

[0151] A controller according to another embodiment of the present invention includes a memory and a processor, wherein the memory stores a computer program, and when the program is executed by the processor, the steps of the path planning method for multiple UAVs described in any embodiment can be implemented.

[0152] A computer-readable storage medium according to another embodiment of the present invention is used to store a computer program, which, when executed by a computer or a processor, implements the steps of the path planning method for multiple drones described in any embodiment.

[0153] The present invention establishes a target area coverage environment model based on the grid method by modeling and analyzing the multi-UAV inspection environment, and completes the target area rasterization processing by integrating the flight altitude of multiple UAVs, the field of view of the gimbal camera, and other parameters, and establishes a mathematical model with the objective function of minimizing the path of a single UAV covering its target sub-area. The DARP algorithm is used to divide the target area into several target sub-areas, and the multi-UAV flight path planning problem is converted into a single UAV flight path planning problem, and the influence of the number and starting position of multiple UAVs on the regional division is studied; for the regional coverage path planning problem of a single UAV, a path planning algorithm based on STC is proposed, and considering that the diversity of the spanning tree structure may increase the number of path turns and affect the UAV inspection efficiency and energy consumption, it is proposed to optimize the STC structure based on the KGACO-STC path planning algorithm. The example analysis is carried out through MATLAB, and the experimental results show that the proposed algorithm can have the shortest path and the least number of turns when the path is fully covered.

[0154] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this profession can make some changes or modifications to equivalent embodiments of equivalent changes using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the scope of the technical solution of the present invention.

Claims

1. A path planning method for multiple UAVs, characterized in that: include: Dividing the target area based on the flight parameters of the UAV, dividing the target area into a plurality of discrete grids; Dividing the grid of the target area according to the number of the drones and the starting position of each drone to obtain target sub-areas corresponding to each drone; Plan the flight path of each UAV in the corresponding target sub-area so that the flight path of each UAV covers the corresponding target sub-area.

2. The multi-UAV path planning method according to claim 1, characterized in that: The step of dividing the target area into a plurality of discrete grids based on the flight parameters of the UAV comprises: Calculating the field of view width of the drone at a preset high flight speed according to the flight parameters; The target area is divided by determining the side length of the grid according to the width of the field of view to obtain a plurality of grids.

3. The multi-UAV path planning method according to claim 2, characterized in that: The flight parameters include the preset flight altitude of the drone and the field of view angle of the camera of the drone; The step of calculating the field of view width of the drone at a preset high flight speed according to the flight parameters comprises: Calculating the diagonal length of the matrix area covered by the camera's field of view according to the preset flight altitude and the field of view angle; The field of view width is calculated based on an aspect ratio of an image captured by the camera and the diagonal length.

4. The multi-UAV path planning method according to claim 2, characterized in that: The side length of the grid is equal to the field of view width multiplied by a preset multiple, where the preset multiple is less than or equal to 1.

5. The multi-UAV path planning method according to claim 1, characterized in that: The step of dividing the grid of the target area according to the number of the drones and the starting position of each drone comprises: Based on the number of the drones and the starting positions of the drones, the DARP algorithm is used to construct evaluation matrices corresponding to the drones, so as to divide the target area and obtain multiple sub-areas with the same or similar number of grids. A reward and punishment matrix is ​​introduced to integrate the grids of each sub-region into a connected state, thereby obtaining a plurality of target sub-regions.

6. The multi-UAV path planning method according to claim 1, characterized in that: When dividing the target sub-areas, at least one of the following constraints is introduced: The target sub-areas corresponding to the various drones do not overlap with each other, the target sub-areas corresponding to multiple drones can cover the target area, the number of grids in the target sub-areas is the same or similar, the grids in each target sub-area are connected, and the starting position of each drone is within the target sub-area corresponding to each drone.

7. The multi-UAV path planning method according to claim 1, characterized in that: The step of planning the flight path of each UAV in the corresponding target sub-area includes: When adjusting the movement rules, the ant colony algorithm is improved by introducing prior knowledge based on straight line priority and minimum number of turns; Based on the quality of the spanning tree constructed by the improved ant colony algorithm, performing pheromone enhancement on the edges in the spanning tree; A penalty coefficient is introduced to quantify the turning cost in path planning to complete the planning of the flight path of each UAV.

8. The multi-UAV path planning method according to claim 1, characterized in that: 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 grid; During the movement of the drone, it can only move from the center point of the current free grid to the center point of the grid that shares a common edge with the current free grid.

9. A controller comprising a memory and a processor, wherein the memory stores a computer program, and when the program is executed by the processor, the steps of the method described in any one of claims 1 to 8 can be implemented.

10. A computer-readable storage medium for storing a computer program, which, when executed by a computer or a processor, implements the steps of the method according to any one of claims 1 to 8.

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