Multi-unmanned aerial vehicle cooperative control method based on floating algorithm

Abstract

The invention discloses a multi-unmanned aerial vehicle cooperative control method based on a floating algorithm, and belongs to the technical field of substations. The multi-unmanned aerial vehicle cooperative control method based on the floating algorithm comprises the following steps: acquiring a target to-be-detected area corresponding to a target unmanned aerial vehicle in a plurality of unmanned aerial vehicles in a plurality of to-be-detected areas, and a target path between the target unmanned aerial vehicle and the target to-be-detected area; in the process of controlling the target unmanned aerial vehicle to move along the target path, acquiring at least one of position information and speed information of each unmanned aerial vehicle; obtaining at least one of new position information and new speed information corresponding to the target unmanned aerial vehicle based on at least one of the position relationship and the speed relationship between the target unmanned aerial vehicle and the other unmanned aerial vehicles; and based on at least one of the new position information and the new speed information corresponding to the target unmanned aerial vehicle, controlling the target unmanned aerial vehicle to move to the target to-be-detected area so as to detect the plurality of to-be-detected electrical devices in the target to-be-detected area.

Description

Technical Field

[0001] The present application belongs to the technical field of substations, and in particular to a multi-UAV collaborative control method based on a flocking algorithm. Background Art

[0002] With the development of industry, electricity has become more and more important in people's lives. As a key node in the power transmission and distribution system, the safe and stable operation of substations is also a prerequisite for ensuring the normal operation of the power system. In the related technology, there is a method of inspecting substations by using multiple drones, but in this method, drones are prone to collision during the inspection of substations, thereby damaging the detection equipment, and there are areas that are easily missed or repeatedly inspected during the inspection of substations, which wastes detection resources and has low detection efficiency. Summary of the invention

[0003] The present application aims to solve at least one of the technical problems existing in the related art. To this end, the present application proposes a multi-UAV collaborative control method based on the flocking algorithm, which effectively reduces the collision of multiple UAVs during the substation inspection process, thereby improving the safety of the substation inspection process by UAVs; and avoids the occurrence of missed inspections or repeated inspections during the substation inspection process, thereby improving the utilization rate of detection resources and detection efficiency.

[0004] In a first aspect, the present application provides a multi-UAV collaborative control method based on a flocking algorithm, which is applied to a substation, wherein the substation includes a plurality of areas to be inspected, each of which is provided with a plurality of electrical equipment to be inspected; the method includes:

[0005] Acquire a target area to be inspected corresponding to a target drone among the multiple drones, and a target path between the target drone and the target area to be inspected;

[0006] In the process of controlling the target UAV to move along the target path, obtaining at least one of position information and speed information of each UAV;

[0007] Based on at least one of the position relationship and the speed relationship between the target UAV and each other UAV, obtaining at least one of the new position information and the new speed information corresponding to the target UAV; the other UAVs are UAVs other than the target UAV among the multiple UAVs; the position relationship and the speed relationship are determined based on the position information and the speed information of each UAV;

[0008] Based on at least one of the new position information and the new speed information corresponding to the target UAV, the target UAV is controlled to move to the target inspection area to inspect a plurality of electrical devices to be inspected in the target inspection area.

[0009] According to the multi-UAV collaborative control method based on the flocking algorithm provided in the embodiment of the present application, the position and speed of the UAV are updated in real time according to the position relationship and speed relationship between the UAV and other UAVs during the movement of the UAV to the area to be inspected, and then the UAV is controlled to fly based on the updated position and speed, thereby effectively reducing the collision of multiple UAVs during the substation inspection process, thereby improving the safety of the UAV inspection of the substation; and by allocating a corresponding area to be inspected to each UAV, missed inspections or repeated inspections during the substation inspection process are avoided, thereby improving the utilization rate of detection resources and detection efficiency.

[0010] In a multi-UAV cooperative control method based on a flocking algorithm according to an embodiment of the present application, obtaining at least one of new position information and new speed information corresponding to the target UAV based on at least one of a position relationship and a speed relationship between the target UAV and each other UAV includes:

[0011] Based on at least one of the position relationship and the speed relationship between the target UAV and each of the other UAVs, acquiring new speed information corresponding to the target UAV;

[0012] Based on the new speed information corresponding to the target UAV and the position information corresponding to the target UAV, the new position information corresponding to the target UAV is acquired.

[0013] In a multi-UAV cooperative control method based on a flocking algorithm according to an embodiment of the present application, obtaining new speed information corresponding to the target UAV based on at least one of the position relationship and the speed relationship between the target UAV and each of the other UAVs includes:

[0014] Based on the difference between the position information of the target UAV and the position information of each of the other UAVs, a separation vector corresponding to the target UAV is obtained; based on the average speeds corresponding to the multiple other UAVs, an alignment vector corresponding to the target UAV is obtained; based on the average positions corresponding to the multiple other UAVs, a gathering vector corresponding to the target UAV is obtained;

[0015] A weighted sum process is performed on the separation vector, the aggregation vector and the alignment vector to obtain new speed information corresponding to the target UAV.

[0016] A multi-UAV cooperative control method based on a flocking algorithm in one embodiment of the present application, controlling the target UAV to move to the target inspection area based on at least one of the new position information and the new speed information corresponding to the target UAV, includes:

[0017] Based on at least one of the new position information and the new speed information corresponding to the target UAV in the current update period, and at least one of the new position information and the new speed information corresponding to each of the other UAVs in the current update period, obtaining at least one of the new position information and the new speed information corresponding to the target UAV in the next update period of the current update period;

[0018] Based on at least one of the new position information and the new speed information of the target UAV corresponding to the next update period, the target UAV is controlled to move to the target inspection area.

[0019] In one embodiment of the present application, a multi-UAV cooperative control method based on a flocking algorithm, obtaining a target inspection area corresponding to a target UAV among the multiple UAVs in the multiple inspection areas includes:

[0020] Based on the plurality of drones and the plurality of areas to be inspected, a cost matrix is ​​constructed; the cost matrix is ​​used to characterize at least one of the flight distance and flight duration of each drone to reach each area to be inspected;

[0021] The cost matrix is ​​processed by using the Hungarian algorithm to obtain the target inspection area corresponding to the target UAV.

[0022] A multi-UAV cooperative control method based on a flocking algorithm in one embodiment of the present application obtains a target path between the target UAV and the target inspection area, including:

[0023] A target path planning algorithm is used to obtain the target path between the target UAV and the target area to be inspected; the target path planning algorithm includes: an A-star algorithm or a Dijkstra algorithm.

[0024] In a second aspect, the present application provides a multi-UAV cooperative control device based on a flocking algorithm, which is applied to a substation, wherein the substation includes a plurality of areas to be inspected, each of which is provided with a plurality of electrical equipment to be inspected; the device includes:

[0025] A first processing module is used to obtain a target area to be inspected corresponding to a target drone among the multiple drones, and a target path between the target drone and the target area to be inspected;

[0026] A second processing module is used to obtain at least one of position information and speed information of each of the UAVs in the process of controlling the target UAV to move along the target path;

[0027] A third processing module is used to obtain at least one of new position information and new speed information corresponding to the target drone based on at least one of a position relationship and a speed relationship between the target drone and each other drone; the other drones are drones other than the target drone among the plurality of drones; the position relationship and the speed relationship are determined based on the position information and the speed information of each drone;

[0028] The fourth processing module is used to control the target drone to move to the target inspection area based on at least one of the new position information and the new speed information corresponding to the target drone, so as to detect multiple electrical devices to be inspected in the target inspection area.

[0029] According to the multi-UAV collaborative control device based on the flocking algorithm provided in the embodiment of the present application, the position and speed of the UAV are updated in real time according to the position relationship and speed relationship between the UAV and other UAVs during the movement of the UAV to the area to be inspected, and then the UAV is controlled to fly based on the updated position and speed, thereby effectively reducing the collision of multiple UAVs during the process of inspecting substations, thereby improving the safety of UAVs in the process of inspecting substations; and by allocating a corresponding area to be inspected to each UAV, missed inspections or repeated inspections during the process of inspecting substations are avoided, thereby improving the utilization rate of detection resources and detection efficiency.

[0030] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the multi-UAV collaborative control method based on the flocking algorithm as described in the first aspect above is implemented.

[0031] In a fourth aspect, the present application provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the multi-UAV collaborative control method based on the flocking algorithm as described in the first aspect above.

[0032] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the multi-UAV collaborative control method based on the flocking algorithm as described in the first aspect above.

[0033] The above one or more technical solutions in the embodiments of the present application have at least one of the following technical effects:

[0034] By updating the position and speed of the drone in real time according to the position and speed relationship between the drone and other drones as the drone moves toward the area to be inspected, and then controlling the drone to fly based on the updated position and speed, the collision of multiple drones during the substation inspection process is effectively reduced, thereby improving the safety of drone inspection of substations; and by assigning a corresponding area to be inspected to each drone, missed inspections or repeated inspections during the substation inspection process are avoided, thereby improving the utilization rate of inspection resources and the inspection efficiency.

[0035] Furthermore, the position and speed relationship between the target UAV and other UAVs is used to calculate the separation vector, alignment vector and aggregation vector, and their weighted sum is performed to update the speed information of the target UAV, and then update its position information, thereby realizing collaborative control among multiple UAVs and ensuring the safety of UAV group inspection operations.

[0036] Furthermore, by updating the position and speed information of the drone in real time during the drone inspection of the substation, the flight status of the drone can be adjusted according to the actual flight conditions of the drone swarm, thereby ensuring that the drone swarm can perform the inspection task in a coordinated manner.

[0037] Furthermore, by establishing the cost of each drone to each inspection area to construct a cost matrix, and using the Hungarian algorithm to process the cost matrix, each drone can be matched to the inspection area that is most suitable for the drone to handle, thereby improving the inspection efficiency of the drone swarm.

[0038] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0040] Figure 1 This is one of the flow charts of the multi-UAV cooperative control method based on the flocking algorithm provided in the embodiment of the present application;

[0041] Figure 2 This is the second flow chart of the multi-UAV cooperative control method based on the flocking algorithm provided in the embodiment of the present application;

[0042] Figure 3 This is the third flow chart of the multi-UAV cooperative control method based on the flocking algorithm provided in the embodiment of the present application;

[0043] Figure 4 This is the fourth flow chart of the multi-UAV cooperative control method based on the flocking algorithm provided in the embodiment of the present application;

[0044] Figure 5 It is a structural schematic diagram of a multi-UAV cooperative control device based on a flocking algorithm provided in an embodiment of the present application;

[0045] Figure 6 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0047] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0048] In the following, in combination with the accompanying drawings, the multi-UAV collaborative control method based on the flocking algorithm, the multi-UAV collaborative control device based on the flocking algorithm, the electronic device and the readable storage medium provided in the embodiments of the present application are described in detail through specific embodiments and their application scenarios.

[0049] Among them, the multi-UAV cooperative control method based on the flocking algorithm can be applied to the terminal, and can be specifically executed by the hardware or software in the terminal.

[0050] The terminal includes, but is not limited to, a portable communication device such as a mobile phone or tablet computer with a touch-sensitive surface (e.g., a touch screen display and / or a touch pad). It should also be understood that in some embodiments, the terminal may not be a portable communication device, but a desktop computer with a touch-sensitive surface (e.g., a touch screen display and / or a touch pad).

[0051] In the following various embodiments, a terminal including a display and a touch-sensitive surface is described. However, it should be understood that the terminal may include one or more other physical user interface devices such as a physical keyboard, a mouse and a joystick.

[0052] The embodiment of the present application provides a multi-UAV collaborative control method based on a flocking algorithm. The execution subject of the multi-UAV collaborative control method based on the flocking algorithm can be an electronic device or a functional module or functional entity in the electronic device that can implement the multi-UAV collaborative control method based on the flocking algorithm. The electronic devices mentioned in the embodiment of the present application include but are not limited to mobile phones, tablet computers, computers, cameras, and wearable devices. The multi-UAV collaborative control method based on the flocking algorithm provided in the embodiment of the present application is described below using an electronic device as an example of the execution subject.

[0053] like Figure 1 As shown, the multi-UAV cooperative control method based on the flocking algorithm includes: step 110, step 120, step 130 and step 140.

[0054] It should be noted that the multi-UAV collaborative control method based on flocking algorithm can be applied to substations.

[0055] A substation is a station in the power system that transforms voltage and current, receives electrical energy, and distributes electrical energy.

[0056] The substation includes a plurality of inspection areas, each of which is provided with a plurality of electrical equipment to be inspected.

[0057] The area to be inspected is the geographical area or spatial range that needs to be inspected.

[0058] The multiple electrical equipment to be inspected include power transformers, circuit breakers, busbars, cables, control devices and auxiliary equipment.

[0059] Drones can obtain the appearance and operating status of electrical equipment, etc., and inspect the electrical equipment to be inspected based on the appearance and operating status.

[0060] Step 110: acquiring a target area to be inspected corresponding to a target drone among the multiple drones, and a target path between the target drone and the target area to be inspected;

[0061] In this step, multiple drones need to work together, and the target drone is any one of the multiple drones.

[0062] The target area to be inspected is one or more areas to be inspected among the multiple areas to be inspected.

[0063] For example, an algorithm (such as the Hungarian algorithm, genetic algorithm, or particle swarm optimization algorithm, etc.) can be used to assign one or more inspection areas to each drone, where the user can select the corresponding algorithm based on needs to obtain the target inspection area corresponding to the target drone, which is not limited in this application.

[0064] The target path is the path from the target UAV to the target area to be inspected. For example, the target path can be the shortest path from the target UAV to the target area to be inspected, or it can be the easiest path to fly from the target UAV to the target area to be inspected (such as the path with the least obstacles), or it can also be other paths.

[0065] For example, the optimal path from the starting point to the corresponding inspection area can be planned for each drone. During the planning process, flight restrictions, obstacles, weather conditions, etc. can be comprehensively considered.

[0066] In the actual implementation process, algorithms such as A-star algorithm, Dijkstra algorithm or genetic algorithm can be used to plan the corresponding target path for each UAV.

[0067] Step 120: in the process of controlling the target UAV to move along the target path, obtaining at least one of the position information and the speed information of each UAV;

[0068] In this step, during the flight of the drone, the position information and speed information of each drone can be obtained in real time, for example, through sensors such as GPS positioning, radar or laser radar (LiDAR).

[0069] Based on at least one of the acquired position information and speed information of each UAV, at least one of the relative position and relative speed relationship between the target UAV and other UAVs can be calculated.

[0070] Step 130: based on at least one of a position relationship and a speed relationship between the target UAV and each of the other UAVs, obtaining at least one of new position information and new speed information corresponding to the target UAV;

[0071] In this step, other drones are drones other than the target drone among the multiple drones.

[0072] The position relationship and speed relationship are determined based on the position information and speed information of each drone.

[0073] The positional relationship between the target UAV and each other UAV is used to characterize the relative positional relationship between the target UAV and each other UAV. For example, the positional relationship may include the distance difference and height difference between the target UAV and each other UAV.

[0074] The speed relationship between the target UAV and each other UAV is used to characterize the relative speed relationship between the target UAV and each other UAV. For example, the speed relationship may include the speed difference between the target UAV and each other UAV.

[0075] Based on at least one of the position relationship and speed relationship between the target UAV and other UAVs, the position information and speed information of the target UAV can be dynamically adjusted to obtain new position information and new speed information corresponding to the target UAV. For example, when a potential flight conflict is detected, the flight altitude, speed or path of the UAV can be adjusted to avoid collision between UAVs.

[0076] Step 140: Based on at least one of the new position information and the new speed information corresponding to the target UAV, control the target UAV to move to the target inspection area to inspect the multiple electrical devices to be inspected in the target inspection area.

[0077] In this step, during the flight of the target UAV, the position information and speed information of the target UAV can be updated in real time based on the position relationship and speed relationship between the target UAV and other UAVs, and the target UAV can be controlled to fly to the target inspection area based on the updated position information and speed information.

[0078] After the drone arrives at the area to be inspected, the corresponding detection equipment (such as an infrared thermal imager and a camera, etc.) can be started to detect multiple electrical devices to be inspected corresponding to the area to be inspected.

[0079] In this application, during the process of multiple drones inspecting substations, by adjusting the flight paths and task allocations of the drones in real time, it is possible to respond to environmental changes and abnormal situations, and to effectively avoid duplication or omissions in the areas inspected by the drones, thereby wasting resources and missing possible abnormal situations.

[0080] According to the multi-UAV collaborative control method based on the flocking algorithm provided in the embodiment of the present application, the position and speed of the UAV are updated in real time according to the position relationship and speed relationship between the UAV and other UAVs during the movement of the UAV to the area to be inspected, and then the UAV is controlled to fly based on the updated position and speed, thereby effectively reducing the collision of multiple UAVs during the substation inspection process, thereby improving the safety of the UAV inspection of the substation; and by allocating a corresponding area to be inspected to each UAV, missed inspections or repeated inspections during the substation inspection process are avoided, thereby improving the utilization rate of detection resources and detection efficiency.

[0081] In some embodiments, step 130 may include:

[0082] Based on at least one of a position relationship and a speed relationship between the target UAV and each of the other UAVs, new speed information corresponding to the target UAV is obtained;

[0083] Based on the new speed information corresponding to the target UAV and the position information corresponding to the target UAV, the new position information corresponding to the target UAV is obtained.

[0084] In this embodiment, the speed information of the target UAV can be updated based on at least one of the position relationship and speed relationship between the target UAV and each other UAV, and then the position information of the target UAV can be updated based on the product of the updated speed information and the selected time step and the sum of the current position information of the target UAV.

[0085] In some embodiments, based on at least one of a position relationship and a speed relationship between the target drone and each of the other drones, obtaining new speed information corresponding to the target drone may include:

[0086] Based on the difference between the position information of the target UAV and the position information of each other UAV, a separation vector corresponding to the target UAV is obtained; based on the average speed of multiple other UAVs, an alignment vector corresponding to the target UAV is obtained; based on the average position of multiple other UAVs, a gathering vector corresponding to the target UAV is obtained;

[0087] The separation vector, aggregation vector and alignment vector are weighted and summed to obtain the new speed information corresponding to the target UAV.

[0088] In this embodiment, the difference between the position information of the target drone and the position information of each other drone may include the distance difference between the target drone and each other drone.

[0089] When the distance between the target drone and all other drones is less than the set safety range, the target drone can be controlled to stay away from the drones that are too close.

[0090] The average speed of all other drones except the target drone among the multiple drones can be obtained, and then the target drone can be controlled to align with the average speed and direction.

[0091] The average positions of all other drones except the target drone among the multiple drones can be obtained, and then the target drone is controlled to move to the average position.

[0092] After obtaining the separation vector, the aggregation vector, and the alignment vector, the three vectors can be integrated and different weights can be set to update the speed information of the target UAV.

[0093] In the actual implementation process, Figure 2As shown, the flocking algorithm can be used to update the speed information and position information of the target UAV.

[0094] The location information P of each drone can be obtained i =(x i ,y i ,z o ), and speed information

[0095] For each drone, its distance from all other drones can be calculated, that is, the relative position between the target drone and each other drone can be calculated, and then a separation vector can be selected.

[0096] When the distance is less than the set safety range, the target UAV is controlled to stay away from the UAV that is too close. The separation vector S corresponding to the target UAV i The formula is as follows:

[0097]

[0098] Among them, S i is the separation vector corresponding to the i-th UAV (target UAV), P j is the location information of other drones except the target drone among multiple drones, P i is the location information of the ith UAV, and N is the number of UAVs.

[0099] For the target UAV, the average speed of all the other UAVs except the target UAV is calculated, and then the target UAV is controlled to align with the average speed and direction. The alignment vector A corresponding to the target UAV is i The formula is as follows:

[0100]

[0101] Among them, A i is the alignment vector corresponding to the i-th UAV (target UAV), V j is the speed information of other UAVs except the target UAV among the multiple UAVs, and N is the number of the multiple UAVs.

[0102] For the target UAV, the average position of all the other UAVs except the target UAV can be calculated, and then the target UAV can be controlled to move to the average position, where the aggregation vector C corresponding to the target UAV is i The formula is as follows:

[0103]

[0104] Among them, C iis the aggregation vector corresponding to the i-th UAV (target UAV), P j is the location information of other drones except the target drone among multiple drones, P i is the location information of the ith UAV, and N is the number of UAVs.

[0105] After obtaining the separation vector, alignment vector, and aggregation vector, different weights can be set for each vector, and the three vectors are weighted summed to update the speed information of the target UAV. The new speed information V′ corresponding to the target UAV i for:

[0106] V′ i =V i +w1*S o +w2*A i +w3*C i

[0107] Among them, w1, w2, and w3 are weights, which respectively represent the influence of the separation vector, the alignment vector, and the aggregation vector on the speed update. i To update the speed information of the target drone, V i ′ is the updated target UAV speed information, S i is the separation vector corresponding to the target UAV, A i is the alignment vector corresponding to the target UAV, C i is the gathering vector corresponding to the target UAV.

[0108] Then the position information of the target UAV can be updated according to the new speed information corresponding to the target UAV. The new position information corresponding to the target UAV is P i ′ :

[0109] P i ′ =P i +V i ′ *Δt

[0110] Among them, P i ′ is the updated position information of the target UAV, P i To update the location information of the target drone, V i ′ To update the speed information of the target UAV, Δt is the selected time step, the size of the time step can be 0.01s-0.1s, and the size of the time step can be determined based on the physical characteristics of the system, the speed of the UAV and the dynamic response requirements, etc., and this application does not limit it.

[0111] For example, after a time step, the target UAV has moved to the updated position, and the new position information in the last update process can be determined as P i , and update the location information of the target drone again.

[0112] According to the multi-UAV collaborative control method based on the flocking algorithm provided in the embodiment of the present application, the position relationship and speed relationship between the target UAV and other UAVs are used to calculate the separation vector, alignment vector and aggregation vector, and perform weighted summation on them to update the speed information of the target UAV, and then update its position information, thereby realizing collaborative control among multiple UAVs and ensuring the safety of UAV group inspection operations.

[0113] In some embodiments, step 140 may include:

[0114] Based on at least one of the new position information and the new speed information corresponding to the target UAV in the current update period and at least one of the new position information and the new speed information corresponding to each other UAV in the current update period, obtaining at least one of the new position information and the new speed information corresponding to the target UAV in the next update period of the current update period;

[0115] Based on at least one of the new position information and the new speed information corresponding to the target UAV in the next update cycle, the target UAV is controlled to move to the target inspection area.

[0116] In this embodiment, the current update period is a period during which the position information and speed information of the target UAV need to be updated.

[0117] The new position information and new speed information of the drone in the current update cycle can be determined as the initial position information and initial speed information of the drone in the next update cycle, and then the position information and speed information of the drone in the next update cycle are updated.

[0118] The motion state of the target UAV can be adjusted according to the new position information and new speed information corresponding to the target UAV.

[0119] When the target UAV has reached the target inspection area or meets other termination conditions (such as time limit or energy limit, etc.), the iteration stops.

[0120] When the termination condition is not met, the next update cycle may be determined as the current update cycle, and then the next update cycle of the current update cycle is entered.

[0121] According to the multi-UAV collaborative control method based on the flocking algorithm provided in the embodiment of the present application, by updating the position information and speed information of the UAV in real time during the UAV inspection of the substation, the flight status of the UAV can be adjusted according to the actual flight conditions of the UAV group, thereby ensuring that the UAV group can perform the inspection task in a coordinated and consistent manner.

[0122] In some embodiments, step 110 may include:

[0123] Based on multiple drones and multiple inspection areas, a cost matrix is ​​constructed;

[0124] The Hungarian algorithm is used to process the cost matrix and obtain the target inspection area corresponding to the target UAV.

[0125] In this embodiment, the cost matrix is ​​used to characterize at least one of the flight distance and flight time of each drone to reach each area to be inspected.

[0126] For example, the distance, duration, and complexity of a mission that a drone needs to fly to complete a certain mission can be calculated to obtain the elements in the cost matrix and construct the cost matrix.

[0127] The cost of each drone to each area to be inspected can be established to construct a cost matrix.

[0128] The Hungarian algorithm can be used to process the cost matrix to assign the optimal inspection area to each drone.

[0129] In the actual implementation process, Figure 3 As shown, after determining multiple areas to be inspected, the cost of each drone to each mission point can be calculated, and then a cost matrix can be constructed based on all the costs.

[0130] The drone group and multiple task points (i.e., multiple inspection areas) can be represented as two fixed point sets on the bipartite graph. Assuming there are n drones and m inspection areas, in the cost matrix C, c ij The cost for the i-th aircraft to perform the j-th mission can be determined based on factors such as the distance, duration, and complexity of the mission required to complete the mission when the drone is operating. ij , the above factors can be quantitatively given in the actual implementation process.

[0131] The cost matrix C is represented as follows:

[0132]

[0133] Among them, c m, The cost required for the nth drone to perform the mth mission.

[0134] Then the rows and columns of the cost matrix can be reset to zero. The element with the smallest value in each row of the cost matrix C can be found, and then this minimum value can be subtracted from all the elements in the row so that at least one element in each row is 0 (the 0 element represents the lowest cost point); then the above-mentioned zeroing operation is also performed on the columns of the cost matrix C so that at least one 0 element appears in each column.

[0135] When the 0 elements are distributed in different rows and columns, it means that the optimal solution is obtained; otherwise, it means that the drone swarm assignment problem is not completed and it is necessary to continue to seek the optimal solution.

[0136] When there are at least two 0 elements distributed in the same row or column, a cross can be drawn as the center of each 0 element, and the remaining 0 elements covered by the cross are no longer drawn with a cross. If there are still elements not covered after all 0s are drawn with a cross, it means that the drone swarm assignment problem is not completed and it is necessary to continue to seek the optimal solution.

[0137] The value of the uncovered element can be subtracted from the smallest element value among the uncovered elements, and the smallest element value can be added to the element that has been covered twice. Then, a cross can be drawn with each 0 element as the center, and the above operation can be repeated until all zero elements can be covered with different rows and columns, thus obtaining the optimal match between multiple inspection areas and the drone swarm.

[0138] According to the multi-UAV collaborative control method based on the flocking algorithm provided in the embodiment of the present application, a cost matrix is ​​constructed by establishing the cost of each UAV to each area to be inspected, and the cost matrix is ​​processed using the Hungarian algorithm, so that each UAV can be matched to the area to be inspected that is most suitable for the UAV to handle, thereby improving the inspection efficiency of the UAV group.

[0139] In some embodiments, step 110 may include:

[0140] The target path planning algorithm is used to obtain the target path between the target UAV and the target inspection area.

[0141] In this embodiment, the target path planning algorithm may include: A-star algorithm or Dijkstra algorithm, or may also include other algorithms, which may be selected based on user needs and are not limited in this application.

[0142] For example, after obtaining the specific task assignment of the drone group, the A-star algorithm can be used to plan the optimal path for each drone to reach the corresponding inspection area according to the inspection area corresponding to each drone.

[0143] In some embodiments, using a target path planning algorithm to obtain a target path between a target drone and a target area to be inspected may include:

[0144] A first set is constructed based on the take-off position corresponding to the target UAV; and a second set is constructed based on the first position information corresponding to the plurality of areas to be inspected;

[0145] Based on the distance information between the take-off position and each first position information, obtaining the target first position information with the smallest distance information from the multiple first position information, deleting the target first position information from the second set, and adding the target first position information to the first set;

[0146] When the sum of the first path between the take-off position and the first position information of the target and the second path between the first position information of the target and the first position information corresponding to the target area to be inspected is less than the distance information between the take-off position and the first position information corresponding to the target area to be inspected, the sum of the first path and the second path is determined as the distance information between the take-off position and the first position information corresponding to the target area to be inspected;

[0147] When the first set is not empty, repeat the steps of "based on the distance information between the take-off position and each first position information, obtaining the target first position information with the smallest distance information from multiple first position information, deleting the target first position information from the second set, and adding the target first position information to the first set; when the sum of the first path between the take-off position and the target first position information and the second path between the target first position information and the first position information corresponding to the target area to be inspected is less than the distance information between the take-off position and the first position information corresponding to the target area to be inspected, determining the sum of the first path and the second path as the distance information between the take-off position and the first position information corresponding to the target area to be inspected" until the first set is empty and the target path between the target UAV and the target area to be inspected is obtained.

[0148] In this embodiment, if Figure 4 As shown, S1: a first set S and a second set U can be established. The initial first set includes the take-off point vs of the drone, that is, S = {vs}, and the distance of vs is 0. All other points are in the set U, that is, U = {other points except vs}. The first set S is used to store the vertices for which the shortest path has been calculated, and the length of the shortest path; the second set U is used to store the vertices for which the shortest path has not been calculated, and the distance from the vertex to the starting point vs.

[0149] S2: You can select a vertex k from U that is closest to the starting point vs, and add vertex k to the set S. At this time, the distance from the starting point to vertex k is the shortest path length.

[0150] S3: Take vertex k as the middle point and update the distances of other fixed points in set U. That is, if the distance from the starting point vs through vertex k to vertex u is less than the distance directly from the starting point vs vertex u (without vertex k), update the distance value of u. That is, the distance value corresponding to u is:

[0151] dist[u] = min(dist[u], shortest path length + w[k][u])

[0152] Among them, dist[u] is the distance value corresponding to u, and w[k][u] is the weight of the edge from vertex k to vertex u.

[0153] S4: When the second set U is not empty, repeat steps S2 and S3 until the second set U is empty.

[0154] The multi-UAV collaborative control device based on the flocking algorithm provided in the present application is described below. The multi-UAV collaborative control device based on the flocking algorithm described below and the multi-UAV collaborative control method based on the flocking algorithm described above can be referenced to each other.

[0155] The multi-UAV cooperative control method based on the flocking algorithm provided in the embodiment of the present application can be executed by a multi-UAV cooperative control device based on the flocking algorithm. In the embodiment of the present application, a multi-UAV cooperative control method based on the flocking algorithm is executed by a multi-UAV cooperative control device based on the flocking algorithm as an example to illustrate the multi-UAV cooperative control device based on the flocking algorithm provided in the embodiment of the present application.

[0156] The embodiment of the present application also provides a multi-UAV collaborative control device based on the flocking algorithm.

[0157] like Figure 5 As shown, the multi-UAV collaborative control device based on the flocking algorithm is applied to a substation, which includes multiple areas to be inspected, each of which is equipped with multiple electrical equipment to be inspected; the device includes: a first processing module 510, a second processing module 520, a third processing module 530 and a fourth processing module 540.

[0158] The first processing module 510 is used to obtain a target inspection area corresponding to a target drone among the multiple drones, and a target path between the target drone and the target inspection area.

[0159] The second processing module 520 is used to obtain at least one of the position information and the speed information of each UAV in the process of controlling the target UAV to move along the target path;

[0160] The third processing module 530 is used to obtain at least one of new position information and new speed information corresponding to the target drone based on at least one of the position relationship and speed relationship between the target drone and each other drone; the other drones are drones other than the target drone among the multiple drones; the position relationship and speed relationship are determined based on the position information and speed information of each drone;

[0161] The fourth processing module 540 is used to control the target drone to move to the target inspection area based on at least one of the new position information and the new speed information corresponding to the target drone, so as to detect multiple electrical devices to be inspected in the target inspection area.

[0162] According to the multi-UAV collaborative control device based on the flocking algorithm provided in the embodiment of the present application, the position and speed of the UAV are updated in real time according to the position relationship and speed relationship between the UAV and other UAVs during the movement of the UAV to the area to be inspected, and then the UAV is controlled to fly based on the updated position and speed, thereby effectively reducing the collision of multiple UAVs during the process of inspecting substations, thereby improving the safety of UAVs in the process of inspecting substations; and by allocating a corresponding area to be inspected to each UAV, missed inspections or repeated inspections during the process of inspecting substations are avoided, thereby improving the utilization rate of detection resources and detection efficiency.

[0163] In some embodiments, the third processing module 530 may also be used to:

[0164] Based on at least one of a position relationship and a speed relationship between the target UAV and each of the other UAVs, new speed information corresponding to the target UAV is obtained;

[0165] Based on the new speed information corresponding to the target UAV and the position information corresponding to the target UAV, the new position information corresponding to the target UAV is obtained.

[0166] In some embodiments, the third processing module 530 may also be used to:

[0167] Based on the difference between the position information of the target UAV and the position information of each other UAV, a separation vector corresponding to the target UAV is obtained; based on the average speed of multiple other UAVs, an alignment vector corresponding to the target UAV is obtained; based on the average position of multiple other UAVs, a gathering vector corresponding to the target UAV is obtained;

[0168] The separation vector, aggregation vector and alignment vector are weighted and summed to obtain the new speed information corresponding to the target UAV.

[0169] In some embodiments, the fourth processing module 540 may also be used to:

[0170] Based on at least one of the new position information and the new speed information corresponding to the target UAV in the current update period and at least one of the new position information and the new speed information corresponding to each other UAV in the current update period, obtaining at least one of the new position information and the new speed information corresponding to the target UAV in the next update period of the current update period;

[0171] Based on at least one of the new position information and the new speed information corresponding to the target UAV in the next update cycle, the target UAV is controlled to move to the target inspection area.

[0172] In some embodiments, the first processing module 510 may also be used to:

[0173] Based on multiple drones and multiple inspection areas, a cost matrix is ​​constructed; the cost matrix is ​​used to represent at least one of the flight distance and flight time of each drone to each inspection area;

[0174] The Hungarian algorithm is used to process the cost matrix and obtain the target inspection area corresponding to the target UAV.

[0175] In some embodiments, the first processing module 510 may also be used to:

[0176] A target path planning algorithm is used to obtain a target path between the target UAV and the target area to be inspected; the target path planning algorithm includes: A star algorithm or Dijkstra algorithm.

[0177] The multi-UAV cooperative control device based on the flocking algorithm in the embodiment of the present application can be an electronic device or a component in the electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices other than a terminal. Exemplarily, the electronic device can be a mobile phone, a tablet computer, a laptop computer, a PDA, a vehicle-mounted electronic device, a mobile Internet device (Mobile Internet Device, MID), an augmented reality (augmentedreality, AR) / virtual reality (virtual reality, VR) device, a robot, a wearable device, an ultra-mobile personal computer (ultra-mobile personal computer, UMPC), a netbook or a personal digital assistant (personaldigital assistant, PDA), etc. It can also be a server, a network attached storage (Network AttachedStorage, NAS), a personal computer (personal computer, PC), a television (television, TV), a teller machine or a self-service machine, etc., which is not specifically limited in the embodiment of the present application.

[0178] The multi-UAV cooperative control device based on the flocking algorithm in the embodiment of the present application can be a device with an operating system. The operating system can be an Android operating system, an IOS operating system, or other possible operating systems, which are not specifically limited in the embodiment of the present application.

[0179] The multi-UAV cooperative control device based on the flocking algorithm provided in the embodiment of the present application can achieve Figures 1 to 4 To avoid repetition, the various processes implemented by the method embodiment are not described here.

[0180] In some embodiments, Figure 6 As shown, an embodiment of the present application also provides an electronic device 600, including a processor 601, a memory 602, and a computer program stored in the memory 602 and executable on the processor 601. When the program is executed by the processor 601, each process of the embodiment of the multi-UAV collaborative control method based on the flocking algorithm is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0181] It should be noted that the electronic devices in the embodiments of the present application include the mobile electronic devices and non-mobile electronic devices mentioned above.

[0182] On the other hand, the present application also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the various processes of the above-mentioned multi-UAV collaborative control method embodiment based on the flocking algorithm, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0183] On the other hand, the present application also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it is implemented to execute the various processes of the above-mentioned multi-UAV collaborative control method embodiment based on the flocking algorithm, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0184] On the other hand, an embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned multi-UAV collaborative control method embodiment based on the flocking algorithm, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0185] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0186] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0187] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, by hardware. Based on this understanding, the above technical solution can be essentially or in other words, the part that contributes to the relevant technology can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiment.

[0188] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A multi-UAV cooperative control method based on flocking algorithm, characterized in that: Applied to a substation, the substation includes a plurality of areas to be inspected, each of which is provided with a plurality of electrical equipment to be inspected; the method includes: Acquire a target area to be inspected corresponding to a target drone among the multiple drones, and a target path between the target drone and the target area to be inspected; In the process of controlling the target UAV to move along the target path, obtaining at least one of position information and speed information of each UAV; Based on at least one of the position relationship and the speed relationship between the target UAV and each other UAV, obtaining at least one of the new position information and the new speed information corresponding to the target UAV; the other UAVs are UAVs other than the target UAV among the multiple UAVs; the position relationship and the speed relationship are determined based on the position information and the speed information of each UAV; Based on at least one of the new position information and the new speed information corresponding to the target UAV, the target UAV is controlled to move to the target inspection area to inspect a plurality of electrical devices to be inspected in the target inspection area.

2. The multi-UAV cooperative control method based on flocking algorithm according to claim 1 is characterized in that: The acquiring at least one of new position information and new speed information corresponding to the target UAV based on at least one of a position relationship and a speed relationship between the target UAV and each other UAV includes: Based on at least one of the position relationship and the speed relationship between the target UAV and each of the other UAVs, acquiring new speed information corresponding to the target UAV; Based on the new speed information corresponding to the target UAV and the position information corresponding to the target UAV, the new position information corresponding to the target UAV is acquired.

3. The multi-UAV cooperative control method based on flocking algorithm according to claim 2 is characterized in that: The acquiring new speed information corresponding to the target UAV based on at least one of the position relationship and the speed relationship between the target UAV and each of the other UAVs includes: Based on the difference between the position information of the target UAV and the position information of each of the other UAVs, a separation vector corresponding to the target UAV is obtained; based on the average speeds corresponding to the multiple other UAVs, an alignment vector corresponding to the target UAV is obtained; based on the average positions corresponding to the multiple other UAVs, a gathering vector corresponding to the target UAV is obtained; A weighted sum process is performed on the separation vector, the aggregation vector and the alignment vector to obtain new speed information corresponding to the target UAV.

4. The multi-UAV cooperative control method based on flocking algorithm according to any one of claims 1 to 3, characterized in that: The controlling the target UAV to move to the target inspection area based on at least one of the new position information and the new speed information corresponding to the target UAV includes: Based on at least one of the new position information and the new speed information corresponding to the target UAV in the current update period, and at least one of the new position information and the new speed information corresponding to each of the other UAVs in the current update period, obtaining at least one of the new position information and the new speed information corresponding to the target UAV in the next update period of the current update period; Based on at least one of the new position information and the new speed information of the target UAV corresponding to the next update period, the target UAV is controlled to move to the target inspection area.

5. The multi-UAV cooperative control method based on flocking algorithm according to any one of claims 1 to 3, characterized in that: The acquiring of a target area to be inspected corresponding to a target drone among the plurality of drones among the plurality of drones comprises: Based on the plurality of drones and the plurality of areas to be inspected, a cost matrix is ​​constructed; the cost matrix is ​​used to characterize at least one of the flight distance and flight duration of each drone to reach each area to be inspected; The cost matrix is ​​processed by using the Hungarian algorithm to obtain the target inspection area corresponding to the target UAV.

6. The multi-UAV cooperative control method based on flocking algorithm according to any one of claims 1 to 3, characterized in that: Obtaining a target path between the target UAV and the target area to be inspected, including: A target path planning algorithm is used to obtain the target path between the target UAV and the target area to be inspected; the target path planning algorithm includes: an A-star algorithm or a Dijkstra algorithm.

7. A multi-UAV cooperative control device based on flocking algorithm, characterized in that: Applied to a substation, the substation includes a plurality of areas to be inspected, each of which is provided with a plurality of electrical equipment to be inspected; the device includes: A first processing module is used to obtain a target area to be inspected corresponding to a target drone among the multiple drones, and a target path between the target drone and the target area to be inspected; A second processing module is used to obtain at least one of position information and speed information of each of the UAVs in the process of controlling the target UAV to move along the target path; A third processing module is used to obtain at least one of new position information and new speed information corresponding to the target drone based on at least one of a position relationship and a speed relationship between the target drone and each other drone; the other drones are drones other than the target drone among the plurality of drones; the position relationship and the speed relationship are determined based on the position information and the speed information of each drone; The fourth processing module is used to control the target UAV to move to the target inspection area based on at least one of the new position information and the new speed information corresponding to the target UAV, so as to detect multiple electrical devices to be inspected in the target inspection area.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the multi-UAV collaborative control method based on the flocking algorithm as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the multi-UAV cooperative control method based on the flocking algorithm as described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the multi-UAV cooperative control method based on the flocking algorithm as described in any one of claims 1 to 6 is implemented.

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