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

Abstract

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

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of substations, and particularly relates to a multi-unmanned aerial vehicle cooperative control method based on a flocking algorithm. BACKGROUND

[0002] With the development of industry, electricity becomes more and more important in people's life, and the safe and stable operation of substations as key nodes in power transmission and distribution systems is also a prerequisite for ensuring the normal operation of power systems. In the related art, there is a method of inspecting substations by multiple unmanned aerial vehicles. However, in this method, the unmanned aerial vehicles are prone to collision during the inspection of substations, thereby damaging the detection equipment, and there are areas prone to missed detection or repeated detection during the inspection of substations, which wastes detection resources and has low detection efficiency. SUMMARY

[0003] The present application aims to at least solve one of the technical problems in the related art. To this end, the present application provides a multi-unmanned aerial vehicle cooperative control method based on a flocking algorithm, which effectively reduces the collision of multiple unmanned aerial vehicles during the inspection of substations, thereby improving the safety of unmanned aerial vehicles during the inspection of substations. In addition, the method avoids the situation of missed detection or repeated detection during the inspection of substations, thereby improving the utilization rate of detection resources and the detection efficiency.

[0004] In a first aspect, the present application provides a multi-unmanned aerial vehicle cooperative control method based on a flocking algorithm, applied to a substation, wherein the substation includes multiple areas to be inspected, and each of the areas to be inspected is provided with multiple electrical equipment to be inspected; the method comprises:

[0005] obtaining a target area to be inspected corresponding to a target unmanned aerial vehicle among the multiple unmanned aerial vehicles in the multiple areas to be inspected, and a target path between the target unmanned aerial vehicle and the target area to be inspected;

[0006] obtaining at least one of position information and speed information of each of the unmanned aerial vehicles during control of the target unmanned aerial vehicle moving along the target path;

[0007] obtaining at least one of new position information and new speed information of the target unmanned aerial vehicle based on at least one of a position relationship and a speed relationship between the target unmanned aerial vehicle and each of the other unmanned aerial vehicles; the other unmanned aerial vehicles are unmanned aerial vehicles other than the target unmanned aerial vehicle among the multiple unmanned aerial vehicles; the position relationship and the speed relationship are determined based on the position information and the speed information of each of the unmanned aerial vehicles;

[0008] Control the target UAV to move to the target area to be detected based on at least one of new position information and new speed information corresponding to the target UAV, so as to detect a plurality of electrical equipment to be detected in the target area to be detected.

[0009] According to the multi-UAV cooperative control method based on the flocking algorithm provided in the embodiments of the present application, the position and speed of the UAV are updated in real time according to the positional relationship and speed relationship between the UAV and other UAVs during the movement of the UAV to the area to be detected, and then the UAV is controlled to fly based on the updated position and speed, which effectively reduces the collision of the multi-UAV during the inspection of the substation, thereby improving the safety during the inspection of the substation by the UAV. In addition, by assigning each UAV with a corresponding area to be detected, the situation of missing detection or repeated detection during the inspection of the substation is avoided, and the detection resource utilization rate and detection efficiency are improved.

[0010] The multi-UAV cooperative control method based on the flocking algorithm of one embodiment of the present application, based on at least one of the positional relationship and speed relationship between the target UAV and each other UAV, obtains at least one of new position information and new speed information corresponding to the target UAV, including:

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

[0013] The multi-UAV cooperative control method based on the flocking algorithm of one embodiment of the present application, based on at least one of the positional relationship and speed relationship between the target UAV and each other UAV, obtains new speed information corresponding to the target UAV, including:

[0014] Based on the difference between the position information of the target UAV and the position information of each other UAV, obtain a separation vector corresponding to the target UAV; based on the average speed corresponding to a plurality of other UAVs, obtain an alignment vector corresponding to the target UAV; based on the average position corresponding to a plurality of other UAVs, obtain a gathering vector corresponding to the target UAV;

[0015] Weighted sum processing is performed on the separation vector, the gathering vector and the alignment vector to obtain new speed information corresponding to the target UAV.

[0016] The multi-unmanned aerial vehicle cooperative control method based on the flocking algorithm of one embodiment of the application controls the target unmanned aerial vehicle to move to the target area to be inspected based on at least one of new position information and new speed information corresponding to the target unmanned aerial vehicle, and includes the following steps:

[0017] Based on at least one of new position information and new speed information corresponding to the target unmanned aerial vehicle in a current update cycle and at least one of new position information and new speed information corresponding to each of the other unmanned aerial vehicles in the current update cycle, new position information and new speed information corresponding to the target unmanned aerial vehicle in a next update cycle of the current update cycle are obtained.

[0018] Based on at least one of new position information and new speed information corresponding to the target unmanned aerial vehicle in the next update cycle, the target unmanned aerial vehicle is controlled to move to the target area to be inspected.

[0019] The multi-unmanned aerial vehicle cooperative control method based on the flocking algorithm of one embodiment of the application obtains the target area to be inspected corresponding to a target unmanned aerial vehicle from among a plurality of unmanned aerial vehicles in a plurality of areas to be inspected, and includes the following steps:

[0020] Based on a plurality of unmanned aerial vehicles and a plurality of areas to be inspected, a cost matrix is constructed; the cost matrix is used to represent at least one of flight distance and flight time of each unmanned aerial vehicle to each area to be inspected;

[0021] The cost matrix is processed by using the Hungarian algorithm to obtain the target area to be inspected corresponding to the target unmanned aerial vehicle.

[0022] The multi-unmanned aerial vehicle cooperative control method based on the flocking algorithm of one embodiment of the application obtains a target path between the target unmanned aerial vehicle and the target area to be inspected, and includes the following steps:

[0023] The target path between the target unmanned aerial vehicle and the target area to be inspected is obtained by using a target path planning algorithm; the target path planning algorithm includes an A* algorithm or a Dijkstra algorithm.

[0024] In a second aspect, the application provides a multi-unmanned aerial vehicle cooperative control device based on a flocking algorithm, which is applied to a substation, the substation includes a plurality of areas to be inspected, and a plurality of electrical equipment to be inspected are arranged in each of the areas to be inspected; the device includes:

[0025] A first processing module is configured to obtain a target area to be inspected corresponding to a target unmanned aerial vehicle from among a plurality of unmanned aerial vehicles in a plurality of areas to be inspected, and a target path between the target unmanned aerial vehicle and the target area to be inspected.

[0026] The second processing module is configured to acquire at least one of position information and speed information of each of the unmanned aerial vehicles during control of the target unmanned aerial vehicle moving along the target path.

[0027] The third processing module is configured to acquire at least one of new position information and new speed information corresponding to the target unmanned aerial vehicle based on at least one of a position relationship and a speed relationship between the target unmanned aerial vehicle and each of other unmanned aerial vehicles, the other unmanned aerial vehicles being unmanned aerial vehicles other than the target unmanned aerial vehicle among the plurality of unmanned aerial vehicles, the position relationship and the speed relationship being determined based on the position information and the speed information of each of the unmanned aerial vehicles.

[0028] The fourth processing module is configured to control the target unmanned aerial vehicle to move to the target detection area to detect a plurality of electrical equipment to be detected in the target detection area based on at least one of the new position information and the new speed information corresponding to the target unmanned aerial vehicle.

[0029] According to the multi-unmanned aerial vehicle cooperative control method based on the flocking algorithm provided in the embodiments of the present application, the position and speed of an unmanned aerial vehicle are updated in real time according to the position relationship and the speed relationship between the unmanned aerial vehicle and other unmanned aerial vehicles during movement of the unmanned aerial vehicle to a detection area, and then the unmanned aerial vehicle is controlled to fly based on the updated position and speed, which effectively reduces the collision of the plurality of unmanned aerial vehicles during the inspection of the substation, thereby improving the safety during the inspection of the substation by the unmanned aerial vehicle. In addition, each unmanned aerial vehicle is allocated a corresponding detection area, which avoids the situation of missing detection or repeated detection during the inspection of the substation, and improves the detection resource utilization rate and the detection efficiency.

[0030] In a third aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the multi-unmanned aerial vehicle cooperative control method based on the flocking algorithm when executing the computer program.

[0031] In a fourth aspect, the present application provides a non-transitory computer readable storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement the multi-unmanned aerial vehicle cooperative control method based on the flocking algorithm.

[0032] In a fifth aspect, the present application provides a computer program product including a computer program, and the computer program is executed by a processor to implement the multi-unmanned aerial vehicle cooperative control method based on the flocking algorithm.

[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 unmanned aerial vehicle in real time according to the positional relationship and speed relationship between the unmanned aerial vehicle and other unmanned aerial vehicles during the movement of the unmanned aerial vehicle to the to-be-inspected area, and then controlling the unmanned aerial vehicle to fly based on the updated position and speed, the collision of the multiple unmanned aerial vehicles during the inspection of the substation is effectively reduced, thereby improving the safety during the inspection of the substation by the unmanned aerial vehicle; and by assigning each unmanned aerial vehicle to a corresponding to-be-inspected area, the situation of missing inspection or repeated inspection during the inspection of the substation is avoided, and the detection resource utilization rate and detection efficiency are improved.

[0035] Further, by the positional relationship and speed relationship between the target unmanned aerial vehicle and other unmanned aerial vehicles, a separation vector, an alignment vector and a cohesion vector are calculated and weighted summed to update the speed information of the target unmanned aerial vehicle, and then the position information of the target unmanned aerial vehicle is updated, thereby realizing the cooperative control among the multiple unmanned aerial vehicles and ensuring the safety of the inspection operation of the unmanned aerial vehicle group.

[0036] Still further, by updating the position information and speed information of the unmanned aerial vehicle in real time during the inspection of the substation by the unmanned aerial vehicle, the flight state of the unmanned aerial vehicle can be adjusted according to the actual flight condition of the unmanned aerial vehicle group, thereby ensuring that the unmanned aerial vehicle group can uniformly and coordinately perform the inspection task.

[0037] Still further, by establishing the cost of each unmanned aerial vehicle to each to-be-inspected area to construct a cost matrix, and processing the cost matrix by using the Hungarian algorithm, each unmanned aerial vehicle can be matched to the to-be-inspected area most suitable for the unmanned aerial vehicle to process, thereby improving the inspection efficiency of the unmanned aerial vehicle group.

[0038] Additional aspects and advantages of the application will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following and / or can be learned by practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0039] The above and / or additional aspects and advantages of the application will become apparent and be readily appreciated from the description of the embodiments, which follows, including the accompanying drawings.

[0040] Figure 1 is one of the flowcharts of the method for cooperative control of multiple unmanned aerial vehicles based on the flocking algorithm provided by the embodiments of the application;

[0041] Figure 2 is another of the flowcharts of the method for cooperative control of multiple unmanned aerial vehicles based on the flocking algorithm provided by the embodiments of the application;

[0042] Figure 3 is a third of the flowcharts of the method for cooperative control of multiple unmanned aerial vehicles based on the flocking algorithm provided by the embodiments of the application;

[0043] Figure 4 FIG. 4 is a fourth flowchart illustrating a method for cooperative control of multiple unmanned aerial vehicles based on a flocking algorithm according to an embodiment of the present application;

[0044] Figure 5 FIG. 5 is a structural diagram of a device for cooperative control of multiple unmanned aerial vehicles based on a flocking algorithm according to an embodiment of the present application;

[0045] Figure 6 FIG. 6 is a structural diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present application will be clearly described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art belong to the scope of protection of the present application.

[0047] The terms "first", "second", and the like 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 interchanged under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than that illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally a category and do not limit the number of objects, for example, the first object can be one or more. In addition, "and / or" in the specification and claims means at least one of the connected objects, and the character " / ", generally represents a "or" relationship between the front and rear associated objects.

[0048] The method for cooperative control of multiple unmanned aerial vehicles based on a flocking algorithm, the device for cooperative control of multiple unmanned aerial vehicles based on a flocking algorithm, the electronic device, and the readable storage medium provided by the embodiments of the present application will be described in detail below with reference to the drawings, specific embodiments and application scenarios.

[0049] The method for cooperative control of multiple unmanned aerial vehicles based on a flocking algorithm can be applied to a terminal, and can be specifically executed by 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 a tablet computer having a touch-sensitive surface (for example, a touchscreen display and / or a touchpad). It should also be understood that in some embodiments, the terminal can not be a portable communication device, but a desktop computer having a touch-sensitive surface (for example, a touchscreen display and / or a touchpad).

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

[0052] The multi-UAV cooperative control method based on the flopcking algorithm provided in this application embodiment can be executed by an electronic device or a functional module or entity in an electronic device that can implement the multi-UAV cooperative control method based on the flopcking algorithm. The electronic devices mentioned in this application embodiment include, but are not limited to, mobile phones, tablets, computers, cameras and wearable devices. The following uses an electronic device as the execution subject to illustrate the multi-UAV cooperative control method based on the flopcking algorithm provided in this application embodiment.

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

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

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

[0056] The substation includes multiple inspection areas, and each inspection area is equipped with multiple electrical devices to be inspected.

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

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

[0059] Drones can acquire information such as the appearance and operating status of electrical equipment, and then inspect the electrical equipment to be inspected based on its appearance and operating status.

[0060] Step 110: Obtain the target inspection area corresponding to the target drone among multiple drones in multiple inspection areas, and the target path between the target drone and the target inspection area;

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

[0062] The target inspection area is one or more of a plurality of inspection areas.

[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 areas to be inspected to each unmanned aerial vehicle, wherein the user can select a corresponding algorithm based on the requirements to obtain the target area to be inspected corresponding to the target unmanned aerial vehicle, which is not limited in the present application.

[0064] The target path is a path from the target unmanned aerial vehicle to the target area to be inspected. For example, the target path can be the shortest path from the target unmanned aerial vehicle to the target area to be inspected, or can be the path that is easiest to fly (such as the path with the fewest obstacles), or can be other paths.

[0065] For example, an optimal path from the starting point to the corresponding area to be inspected can be planned for each unmanned aerial vehicle, and in the planning process, flight restrictions, obstacles, and weather conditions, etc. can be considered comprehensively.

[0066] In actual execution, an A-star algorithm, Dijkstra algorithm, or genetic algorithm, etc. can be used to plan the corresponding target path for each unmanned aerial vehicle.

[0067] Step 120, during the process of controlling the target unmanned aerial vehicle to move along the target path, at least one of the position information and the speed information of each unmanned aerial vehicle is obtained.

[0068] In this step, the position information and the speed information of each unmanned aerial vehicle can be obtained in real time during the flight of the unmanned aerial vehicle, for example, through GPS positioning, radar, or LiDAR sensors, etc.

[0069] Based on the at least one of the position information and the speed information of each unmanned aerial vehicle obtained, at least one of the relative position and the relative speed relationship between the target unmanned aerial vehicle and other unmanned aerial vehicles can be calculated.

[0070] Step 130, based on at least one of the position relationship and the speed relationship between the target unmanned aerial vehicle and each other unmanned aerial vehicle, at least one of the new position information and the new speed information corresponding to the target unmanned aerial vehicle is obtained.

[0071] In this step, the other unmanned aerial vehicles are unmanned aerial vehicles other than the target unmanned aerial vehicle in the plurality of unmanned aerial vehicles.

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

[0073] The position relationship between the target unmanned aerial vehicle and each other unmanned aerial vehicle is used to represent the relative position relationship between the target unmanned aerial vehicle and each other unmanned aerial vehicle, for example, the position relationship can include the distance difference and the height difference between the target unmanned aerial vehicle and each other unmanned aerial vehicle, etc.

[0074] The speed relationship between the target UAV and each of the other UAVs is used to represent the relative speed relationship between the target UAV and each of the other UAVs. For example, the speed relationship can include a speed difference between the target UAV and each of the other UAVs.

[0075] Based on at least one of the position relationship and the speed relationship between the target UAV and each of the other UAVs, the position information and the 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, in the case of detecting a potential flight conflict, the flight height, speed, or path of the UAV can be adjusted to avoid collision between the UAVs.

[0076] In step 140, 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 detection area to detect a plurality of electrical equipment in the target detection area.

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

[0078] After the UAV reaches the detection area, the corresponding detection equipment (such as an infrared thermal imager and a camera) can be started to detect a plurality of electrical equipment corresponding to the detection area.

[0079] In this application, during the process of multiple UAVs patrolling the substation, by adjusting the flight path and task allocation of the UAVs in real time, the changes in the environment and abnormal situations can be responded to, and the problem that the areas patrolled by the UAVs are repeated or missed, resulting in waste of resources and missing of possible abnormal situations, can be effectively avoided.

[0080] According to the multi-UAV cooperative control method based on the flocking algorithm provided in the embodiments of this application, by updating the position and speed of the UAV in real time according to the position relationship and the speed relationship between the UAV and the other UAVs during the movement of the UAV to the detection area, and then controlling the UAV to fly based on the updated position and speed, the collision between the multiple UAVs during the process of patrolling the substation is effectively reduced, thereby improving the safety during the process of the UAVs patrolling the substation. Moreover, by allocating a corresponding detection area to each UAV, the problem of missing detection or repeated detection during the process of patrolling the substation is avoided, and the detection resource utilization rate and the detection efficiency are improved.

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

[0082] acquire new speed information corresponding to the target UAV based on at least one of a positional relationship and a speed relationship between the target UAV and each of the other UAVs;

[0083] acquire new position 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.

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

[0085] In some embodiments, acquiring new speed information corresponding to the target UAV based on at least one of a positional relationship and a speed relationship between the target UAV and each of the other UAVs can include:

[0086] acquiring a separation vector corresponding to the target UAV based on a difference degree between the position information of the target UAV and the position information of each of the other UAVs, acquiring an alignment vector corresponding to the target UAV based on average speeds corresponding to the plurality of other UAVs, and acquiring a gathering vector corresponding to the target UAV based on average positions corresponding to the plurality of other UAVs;

[0087] performing weighted sum processing on the separation vector, the gathering vector, and the alignment vector to acquire new speed information corresponding to the target UAV.

[0088] In this embodiment, the difference degree between the position information of the target UAV and the position information of each of the other UAVs can include a distance difference between the target UAV and each of the other UAVs.

[0089] In a case where the distance between the target UAV and all the other UAVs is less than a set safety range, the target UAV can be controlled to move away from the UAVs that are too close.

[0090] The average speed of all the other UAVs in the plurality of UAVs except the target UAV can be acquired, and then the target UAV can be controlled to align with the average speed and direction.

[0091] The average position of all the other UAVs in the plurality of UAVs except the target UAV can be acquired, and then the target UAV can be controlled to move to the average position.

[0092] In a case where the separation vector, the gathering vector, and the alignment vector are acquired, the three vectors can be integrated with different weights to update the speed information of the target UAV.

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

[0094] The position information P i of each UAV can be obtained i , i , o , and the speed information V

[0095] For each UAV, the distance between it and all other UAVs can be calculated, i.e., the relative position between the target UAV and each other UAV is calculated, and then a separation vector S

[0096] In the case where the distance is less than a set safety range, the target UAV is controlled to move away from the UAV with which the distance is too close, and the separation vector S i corresponding to the target UAV is calculated according to the following formula:

[0097]

[0098] where S i is the separation vector corresponding to the i-th UAV (target UAV), P j is the position information of each UAV other than the target UAV, P i is the position information of the i-th UAV, and N is the number of UAVs.

[0099] For the target UAV, the average speed of all UAVs other than the target UAV is calculated, and then the target UAV is controlled to align with the average speed and direction, where the alignment vector A i corresponding to the target UAV is calculated according to the following formula:

[0100]

[0101] where A i is the alignment vector corresponding to the i-th UAV (target UAV), V j is the speed information of each UAV other than the target UAV, and N is the number of UAVs.

[0102] For the target UAV, the average position of all UAVs other than the target UAV is calculated, and then the target UAV is controlled to move to the average position, where the aggregation vector C i corresponding to the target UAV is calculated according to the following formula:

[0103]

[0104] where C iis the alignment vector corresponding to the i-th UAV (target UAV), P j is the position information of other UAVs in the plurality of UAVs except the target UAV, P i is the position information of the i-th UAV, and N is the number of the plurality of UAVs.

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

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

[0107] wherein w1, w2 and w3 are weights, respectively representing the influence degree of the separation vector, the alignment vector and the aggregation vector on the speed update, V i is the speed information of the target UAV before updating, V i ′ is the speed information of the target UAV after updating, 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 aggregation vector corresponding to the target UAV.

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

[0109] P i ′ = P i + V i ′ *Δt

[0110] wherein P i ′ is the position information of the target UAV after updating, P i is the position information of the target UAV before updating, V i ′ is the speed information of the target UAV after updating, and 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 requirement, which is not limited in the present application.

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

[0112] According to the multi-UAV cooperative control method based on the flocking algorithm provided in the embodiments of the present application, the position relationship and the speed relationship between the target UAV and other UAVs are used to calculate a separation vector, an alignment vector and a cohesion vector, and the three vectors are weighted and summed to update the speed information of the target UAV, and then the position information of the target UAV is updated, thereby realizing cooperative control among the multi-UAVs and ensuring the safety of the UAV group inspection operation.

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

[0114] Based on at least one of the new position information and the new speed information of the target UAV corresponding to the current update period and at least one of the new position information and the new speed information of each of the other UAVs corresponding to the current update period, at least one of the new position information and the new speed information of the target UAV corresponding to the next update period of the current update period is obtained.

[0115] 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.

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

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

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

[0119] In the case where the target UAV has reached the target inspection area or meets other termination conditions (such as time limit or energy limit, etc.), the iteration is stopped.

[0120] In the case where the termination condition is not met, the next update period can be determined as the current update period, and then the next update period of the current update period is entered.

[0121] According to the multi-unmanned aerial vehicle cooperative control method based on the flocking algorithm provided in the embodiments of the present application, the position information and the speed information of the unmanned aerial vehicles are updated in real time during the process of the unmanned aerial vehicles inspecting the transformer substation, the flight state of the unmanned aerial vehicles can be adjusted according to the actual flight condition of the unmanned aerial vehicle group, and therefore the unmanned aerial vehicle group can be ensured to perform the inspection task in a coordinated manner.

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

[0123] Based on the plurality of unmanned aerial vehicles and the plurality of areas to be inspected, a cost matrix is constructed.

[0124] The cost matrix is processed by using the Hungarian algorithm to obtain the target area to be inspected corresponding to the target unmanned aerial vehicle.

[0125] In this embodiment, the cost matrix is used to represent at least one of the flight distance and the flight time of each unmanned aerial vehicle to each area to be inspected.

[0126] For example, the distance, the time and the task complexity of the unmanned aerial vehicle when completing a task can be calculated, and therefore each element in the cost matrix is obtained to construct the cost matrix.

[0127] The cost of each unmanned aerial vehicle to each area to be inspected can be established to construct the cost matrix.

[0128] The cost matrix can be processed by using the Hungarian algorithm to assign the optimal area to be inspected to each unmanned aerial vehicle.

[0129] In the actual execution process, as shown in FIG. Figure 3 After the plurality of areas to be inspected is determined, the cost of each unmanned aerial vehicle to each task point can be calculated, and then the cost matrix is constructed according to the total cost.

[0130] The unmanned aerial vehicle group and the plurality of task points (i.e., the plurality of areas to be inspected) can be respectively represented as two fixed point sets on a bipartite graph, assuming that there are n unmanned aerial vehicles and m areas to be inspected, in the cost matrix C, c ij is the cost required for the i th unmanned aerial vehicle to perform the j th task, which can be determined based on the distance, the time and the task complexity of the unmanned aerial vehicle when completing a task. ij In the actual execution process, the above factors can be quantitatively given.

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

[0132]

[0133] wherein c m, is the cost required for the n th unmanned aerial vehicle to perform the m th task.

[0134] Then, the rows and columns of the cost matrix can be zeroed, the element with the minimum value in each row of the cost matrix C can be found, and then all elements in the row can be subtracted by the minimum value, so that there is at least one element of 0 (0 element represents the lowest cost point) in each row; and then the above zeroing operation is performed on the columns of the cost matrix C, so that there is at least one 0 element in each column.

[0135] In the case that the 0 elements are distributed in different rows and columns, it is indicated that the optimal solution is obtained; otherwise, the unmanned aerial vehicle group assignment problem is not completed, and the optimal solution needs to be continuously sought.

[0136] In the case that there are at least two 0 elements distributed in the same row or column, a cross can be drawn around each 0 element, and the remaining 0 elements covered by the cross are no longer drawn. In the case that there are still elements not covered after all 0 elements are crossed, it is indicated that the unmanned aerial vehicle group assignment problem is not completed, and the optimal solution needs to be continuously sought.

[0137] The value of the element not covered can be subtracted by the minimum value of the elements not covered, and the minimum value is added to the element covered twice, and then the cross is drawn around each 0 element, and the above operation is repeated until all zero elements can be covered with different rows and columns, that is, the optimal matching of multiple areas to be inspected and the unmanned aerial vehicle group is obtained.

[0138] According to the multi-unmanned aerial vehicle cooperative control method based on the flocking algorithm provided in the embodiments of the present application, by establishing the cost of each unmanned aerial vehicle to each area to be inspected, a cost matrix is constructed, and the cost matrix is processed by using the Hungarian algorithm, so that each unmanned aerial vehicle can be matched to the area to be inspected that is most suitable for the unmanned aerial vehicle to process, and the patrol efficiency of the unmanned aerial vehicle group is improved.

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

[0140] The target path planning algorithm is used to obtain the target path between the target unmanned aerial vehicle and the target area to be inspected.

[0141] In this embodiment, the target path planning algorithm can include A-star algorithm or Dijkstra algorithm, or can also include other algorithms, which can be selected based on user demand, and the present application does not make any limitation.

[0142] For example, after obtaining the specific task allocation of the unmanned aerial vehicle group, the A-star algorithm can be used to plan the best path for each unmanned aerial vehicle to reach the corresponding area to be inspected.

[0143] In some embodiments, the target path planning algorithm is used to obtain the target path between the target unmanned aerial vehicle and the target area to be inspected, which can include:

[0144] constructing a first set based on the take-off position corresponding to the target UAV, and constructing a second set based on the first position information corresponding to the plurality of inspection areas;

[0145] acquiring target first position information with minimum distance information from the plurality of first position information based on the distance information between the take-off position and each 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] 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 inspection area in a case where the sum of a first path between the take-off position and the target first position information and a second path between the target first position information and the first position information corresponding to the target inspection area is less than the distance information between the take-off position and the first position information corresponding to the target inspection area;

[0147] repeating the steps of acquiring target first position information with minimum distance information from the plurality of first position information based on the distance information between the take-off position and each first position information, deleting the target first position information from the second set, and adding the target first position information to the first set, and 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 inspection area in a case where the sum of a first path between the take-off position and the target first position information and a second path between the target first position information and the first position information corresponding to the target inspection area is less than the distance information between the take-off position and the first position information corresponding to the target inspection area, until the first set is empty, to obtain the target path between the target UAV and the target inspection area.

[0148] In this embodiment, as shown in FIG. 1, Figure 4 S1: a first set S and a second set U can be established, the initial first set includes a take-off point vs of the UAV, i.e., S={vs}, and the distance of vs is 0, and all other points are in the set U, i.e., U={other points except vs}, the first set S is used to store vertices for which the shortest path has been found and the length of the shortest path, and the second set U is used to store vertices for which the shortest path has not been found and the distance of the vertex to the starting point vs.

[0149] S2: a vertex k closest to the starting point vs can be selected from U, and the vertex k is added to the set S, and the distance from the starting point to the vertex k is the length of the shortest path.

[0150] S3: taking vertex k as an intermediate point, updating distances of other points in the set U, i.e. if the distance from the starting point vs to vertex u through vertex k is less than the distance from the starting point vs to vertex u without passing through vertex k, updating the distance value of u, i.e. the distance value corresponding to u is:

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

[0152] where 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: repeating steps S2 and S3 until the second set U is empty, in the case that the second set U is not empty.

[0154] The multi-robot cooperative control device based on the flocking algorithm provided in the present application will be described below. The multi-robot cooperative control device based on the flocking algorithm described below can be correspondingly referred to the multi-robot cooperative control method based on the flocking algorithm described above.

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

[0156] The present application also provides a multi-robot cooperative control device based on the flocking algorithm.

[0157] As shown in Figure 5 The multi-robot cooperative control device based on the flocking algorithm is applied to a substation, the substation includes a plurality of to-be-inspected areas, and a plurality of to-be-inspected electrical equipment are arranged in each to-be-inspected area; 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 configured to acquire a target to-be-inspected area corresponding to a target robot in the plurality of robots in the plurality of to-be-inspected areas, and a target path between the target robot and the target to-be-inspected area.

[0159] The second processing module 520 is configured to acquire at least one of position information and speed information of each robot during control of the target robot moving along the target path.

[0160] The third processing module 530 is configured to acquire 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 of the other UAVs; the other UAVs are the UAVs other than the target UAV in the plurality of UAVs; the position relationship and the speed relationship are determined based on the position information and the speed information of each of the UAVs;

[0161] The fourth processing module 540 is configured to control the target UAV to move to the target detection 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 the plurality of electrical equipment in the target detection area.

[0162] According to the multi-UAV cooperative control apparatus based on the flocking algorithm provided in the embodiments of the present application, the position and the speed of the UAV are updated in real time according to the position relationship and the speed relationship between the UAV and the other UAVs during the movement of the UAV to the detection area, and then the UAV is controlled to fly based on the updated position and speed, which effectively reduces the collision of the plurality of UAVs during the inspection of the substation, thereby improving the safety during the inspection of the substation by the UAV. In addition, each UAV is allocated with a corresponding detection area, which avoids the situation of missing detection or repeated detection during the inspection of the substation, and improves the detection resource utilization rate and the detection efficiency.

[0163] In some embodiments, the third processing module 530 can be further configured to:

[0164] acquire the 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;

[0165] acquire the new position 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.

[0166] In some embodiments, the third processing module 530 can be further configured to:

[0167] acquire a separation vector corresponding to the target UAV based on a difference degree between the position information of the target UAV and the position information of each of the other UAVs; acquire an alignment vector corresponding to the target UAV based on an average speed corresponding to the plurality of other UAVs; and acquire a gathering vector corresponding to the target UAV based on an average position corresponding to the plurality of other UAVs;

[0168] perform weighted sum processing on the separation vector, the gathering vector and the alignment vector to acquire the new speed information corresponding to the target UAV.

[0169] In some embodiments, the fourth processing module 540 can be further configured to:

[0170] based on at least one of the new position information and the new speed information of the target UAV in the next update period, control the target UAV to move to the target inspection area.

[0171] based on at least one of the new position information and the new speed information of the target UAV in the next update period, control the target UAV to move to the target inspection area.

[0172] In some embodiments, the first processing module 510 can be further configured to:

[0173] based on the plurality of UAVs and the plurality of inspection areas, construct a cost matrix; the cost matrix is used to represent at least one of flight distances and flight times of the UAVs to the inspection areas;

[0174] using the Hungarian algorithm, process the cost matrix to obtain the target inspection area corresponding to the target UAV.

[0175] In some embodiments, the first processing module 510 can be further configured to:

[0176] using the target path planning algorithm, obtain a target path between the target UAV and the target inspection area; the target path planning algorithm includes an A* algorithm or a Dijkstra algorithm.

[0177] The multi-UAV cooperative control device based on the flocking algorithm in the embodiments of the present applicationapplicationbe an electronic device or a component in an electronic device, such as an integrated circuit or a chip. The electronic deviceapplicationbe a terminal or other devices other than a terminal. For example, the electronic deviceapplicationbe a mobile phone, a tablet computer, a notebook computer, a palm computer, a vehicle-mounted electronic device, a Mobile Internet Device (MID), an augmented reality (AR) / virtual reality (VR) device, a robot, a wearable device, an ultra-mobile personal computer (UMPC), a netbook, or a personal digital assistant (PDA), etc., andapplicationbe a server, a Network Attached Storage (NAS), a personal computer (PC), a television (TV), a teller machine, or a self-service machine, etc., and the embodiments of the present application do not make specific limitations.

[0178] The multi-robot cooperative control device based on the flocking algorithm in the embodiments of the present applicationapplicationbe a device with an operating system. The operating systemapplicationbe an Android operating system, an IOS operating system, or other possible operating systems, and the embodiments of the present application do not make specific limitations.

[0179] The multi-robot cooperative control device based on the flocking algorithm provided in the embodiments of the present applicationapplicationbe able to achieve Figures 1 to 4 The processes achieved by the method embodimentsapplicationbe described above, and thus will not be described here again to avoid repetition.

[0180] In some embodiments, as shown in Figure 6 The embodiments of the present application also provide an electronic device 600, which includes a processor 601, a memory 602, and a computer program stored in the memory 602 and capable of running on the processor 601. The programapplicationbe executed by the processor 601 to achieve the processes of the above-mentioned multi-robot cooperative control method based on the flocking algorithm, and achieve the same technical effects. The processesapplicationnot be described here again to avoid repetition.

[0181] It should be noted that the electronic device in the embodiments of the present applicationapplicationinclude the mobile electronic device and the non-mobile electronic device described above.

[0182] On the other hand, the present application also provides a computer program product, which includes a computer program stored in a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computerapplicationbe able to execute the processes of the above-mentioned multi-robot cooperative control method based on the flocking algorithm, and achieve the same technical effects. The processesapplicationnot be described here again to avoid repetition.

[0183] In yet another aspect, the present application also provides a non-transitory computer-readable storage medium having a computer program stored thereon. The computer programapplicationbe executed by a processor to achieve the processes of the above-mentioned multi-robot cooperative control method based on the flocking algorithm, and achieve the same technical effects. The processesapplicationnot be described here again to avoid repetition.

[0184] In yet another aspect, the embodiments of the present application also provide a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor, and the processorapplicationbe used to run a program or instructions to achieve the processes of the above-mentioned multi-robot cooperative control method based on the flocking algorithm, and achieve the same technical effects. The processesapplicationnot be described here again to avoid repetition.

[0185] It should be understood that the chip mentioned in the embodiments of the present application can also be referred to as 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 can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed to multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiments according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0187] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software and the necessary general hardware platform, and of course can also be realized by hardware. Based on such understanding, the above technical solutions or the part that contributes to the related art can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0188] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate 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 the flopcking algorithm, characterized in that, The method is applied to a substation, which includes multiple inspection areas, and each inspection area is equipped with multiple electrical devices to be inspected; the method includes: Obtain the target inspection area corresponding to the target UAV among the multiple UAVs in the multiple inspection areas, and the target path between the target UAV and the target inspection area; During the process of controlling the target UAV to move along the target path, at least one of the position information and speed information of each UAV is acquired; Based on at least one of the positional and velocity relationships between the target drone and other drones, at least one of the new positional and velocity information corresponding to the target drone is obtained; the other drones are drones other than the target drone among a plurality of drones; the positional and velocity relationships are determined based on the positional and velocity information of each drone. Based on at least one of the new position information and new speed information corresponding to the target UAV, the target UAV is controlled to move to the target inspection area in order to inspect multiple electrical devices in the target inspection area.

2. The multi-UAV cooperative control method based on the flopping algorithm according to claim 1, characterized in that, The step of obtaining at least one of new position information and new speed information corresponding to the target UAV based on at least one of the positional and speed relationships between the target UAV and other UAVs includes: Based on at least one of the positional relationship and the speed relationship between the target drone and each of the other drones, new speed information corresponding to the target drone is obtained; Based on the new speed information and the new position information of the target drone, the new position information of the target drone is obtained.

3. The multi-UAV cooperative control method based on the flopping algorithm according to claim 2, characterized in that, The step of obtaining new speed information corresponding to the target drone based on at least one of the positional relationship and speed relationship between the target drone and the other drones includes: Based on the difference between the position information of the target UAV and the position information of the other UAVs, a separation vector corresponding to the target UAV is obtained; based on the average speed of the other UAVs, an alignment vector corresponding to the target UAV is obtained; based on the average position of the other UAVs, a clustering vector corresponding to the target UAV is obtained. The separation vector, the aggregation vector, and the alignment vector are weighted and summed to obtain new velocity information corresponding to the target UAV.

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

5. The multi-UAV cooperative control method based on the flopping algorithm according to any one of claims 1-3, characterized in that, The step of obtaining the target inspection area corresponding to the target drone among the multiple drones in the multiple inspection areas includes: Based on the multiple drones and the multiple 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 time of each drone to reach each area to be inspected. The cost matrix is ​​processed using the Hungarian algorithm to obtain the target inspection area corresponding to the target UAV.

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

7. A multi-UAV cooperative control device based on the flopcking algorithm, characterized in that, Applied to substations, the substation includes multiple inspection areas, and each inspection area is equipped with multiple electrical devices to be inspected; the device includes: The first processing module is used to obtain the target inspection area corresponding to the target UAV among the multiple UAVs in the multiple inspection areas, and the target path between the target UAV and the target inspection area; The second processing module is used to acquire at least one of the position information and speed information of each of the drones during the process of controlling the target drone to move along the target path; The 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 the positional relationship and speed relationship between the target drone and other drones; the other drones are drones other than the target drone among a plurality of drones; the positional 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 drone to move to the target inspection area based on at least one of the new position information and new speed information corresponding to the target drone, so as to inspect multiple electrical devices 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, it implements the multi-UAV cooperative control method based on the flopcking algorithm as described in any one of claims 1-6.

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

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

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