Automatic placing method of unmanned aerial vehicle cluster

Abstract

The invention discloses an automatic placement method for an unmanned aerial vehicle cluster, and the method comprises the following steps: S1, determining a preparation region and a placement region of the unmanned aerial vehicle cluster, the unmanned aerial vehicle cluster comprising a plurality of unmanned aerial vehicles located in the preparation region; s2, determining a take-off position of the unmanned aerial vehicle in the preparation area, and distributing a target position for the unmanned aerial vehicle based on the placement area; s3, performing path planning on the unmanned aerial vehicle according to the take-off position and the target position to determine a target path of the unmanned aerial vehicle, and storing the target path of the unmanned aerial vehicle into a path library; s4, according to a target path stored in the path library, controlling the unmanned aerial vehicle to execute a placement task according to the target path; and S5, repeating the steps S2 to S4 until all the unmanned aerial vehicles in the unmanned aerial vehicle cluster are traversed. According to the invention, the unmanned aerial vehicles in the unmanned aerial vehicle cluster are sequentially placed based on the planned target path, so that manpower and material resources are greatly saved, and the deployment efficiency is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of drones, and in particular to a method for automatically placing a drone cluster. Background Art

[0002] With the rapid development of UAV swarm collaborative formation technology, demand for its application in logistics inspection, dynamic light shows, disaster monitoring, and other fields is growing. However, the deployment efficiency of large-scale swarms has become a core bottleneck restricting its practical application.

[0003] Traditional deployment models rely on manual calibration of the takeoff position and attitude of each aircraft, a cumbersome and time-consuming process. When faced with formation missions involving dozens or even hundreds of aircraft, manual intervention not only significantly increases manpower and time costs, but also makes it difficult to meet the rapid response requirements in emergency scenarios. To address this issue, existing technologies have proposed an automated solution called "dense in-situ deployment and synchronized vertical takeoff." While this simplifies the deployment process, the initial compact spatial distribution of the aircraft leads to an exponential increase in the probability of path conflicts during the launch phase. In this case, the obstacle avoidance algorithm must solve the high-density dynamic intersecting trajectories in real time within three-dimensional space, significantly increasing computational complexity and communication latency, forcing some aircraft into a hovering wait state. The resulting "waiting chain reaction" not only prolongs the ineffective hold time before the formation is formed, but also squeezes the available flight time during the mission execution phase, significantly restricting application scenarios such as monitoring, logistics, and performances that rely on long-range flight. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an automatic placement method for a drone cluster to solve the problem of low efficiency of the existing drone cluster placement method.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for automatically placing a drone cluster, which includes the following steps: S1: Determine a preparation area and a placement area for a drone cluster, where the drone cluster includes multiple drones located in the preparation area; S2: Determine a take-off position of the UAV in the preparation area, and assign a target position to the UAV based on the placement area; S3: performing path planning for the UAV according to the take-off position and the target position to determine a target path for the UAV, and storing the target path of the UAV in a path library; S4: According to the target path stored in the path library, controlling the UAV to perform the placement task according to the target path; S5: Repeat steps S2 to S4 until all drones in the drone cluster are traversed.

[0006] Furthermore, in the automatic placement method of the present invention, in step S2, assigning a target position to the drone based on the placement area includes: Obtaining a placement map corresponding to the placement area, wherein the placement map includes a plurality of coordinate points set according to placement spacing; Unoccupied coordinate points in the placement diagram are determined, and a coordinate point is selected from the unoccupied coordinate points according to a selection rule to serve as the target position.

[0007] Furthermore, in the automatic placement method of the present invention, in step S3, path planning is performed on the drone based on the take-off position and the target position to determine the target path of the drone, specifically comprising the following steps: S31: performing an initial path planning operation on the UAV based on the take-off position and the target position to determine a flight path of the UAV; S32: performing a path conflict check on the flight path to determine whether the flight path conflicts with a target path stored in a path library; if there is no path conflict, the flight path is used as the target path; if there is a path conflict, proceeding to step S33; S33: Replacing the target position and re-performing the path planning operation to generate a new flight path; performing a path conflict check on the new flight path to determine whether there is a path conflict; if there is no path conflict, the new flight path is used as the target path; if there is a path conflict, proceeding to step S34; S34: Repeat step S33 until there is no path conflict in the generated new flight path.

[0008] Furthermore, in the automatic placement method of the present invention, in step S32, a path conflict detection is performed on the flight path to determine whether the flight path conflicts with the target path stored in the path library, including the following steps: S321: Dividing the placement task of the UAV into flight time slots based on a reference time; S322: Determine a first time slot position of the flight path within each flight time slot, and determine a second time slot position of the target path stored in the path library within each flight time slot; S323: Determine whether the UAV has a collision risk based on the first time slot position and the second time slot position; S324: If there is a collision risk, it is determined that there is a path conflict in the flight path; if there is no collision risk, it is determined that there is no path conflict in the flight path.

[0009] Furthermore, in the automatic placement method of the present invention, in step S323, the following steps are included: Calculating the time slot distance between the first time slot position and the second time slot position of each target path in the path library within the same flight time slot until all flight time slots are traversed; Determine whether there is a situation in one or more flight time slots where the time slot distance is less than a safety threshold. If so, determine that the drone has a collision risk; if not, determine that the drone does not have a collision risk.

[0010] Furthermore, in the automatic placement method described in the present invention, in step S3, the target path includes a vertical ascent segment, a horizontal translation segment and a vertical landing segment; wherein the vertical ascent segment includes a flight trajectory that rises vertically from a take-off position to a predetermined height, the horizontal translation segment includes a flight trajectory that flies from the predetermined height to directly above the target position, and the vertical landing segment includes a flight trajectory that descends vertically from directly above the target position to the target position.

[0011] Furthermore, in the automatic placement method described in the present invention, the area of the preparation area is smaller than the area of the placement area, and the preparation area and the placement area are in the same plane or the height difference between the two does not exceed a preset threshold.

[0012] Furthermore, in the automatic placement method described in the present invention, in step S3, the following steps are also included: when storing the target path of the drone into the path library, the coordinate points corresponding to the target path are marked as occupied.

[0013] Furthermore, in the automatic placement method of the present invention, in step S2, determining the take-off position of the UAV in the preparation area includes: After the UAV is powered on, the positioning information of the UAV is obtained, and the initial position of the UAV is determined according to the positioning information; The initial position is coordinate-converted to obtain the take-off position of the UAV.

[0014] Furthermore, in the automatic placement method of the present invention, performing coordinate transformation on the initial position to obtain the take-off position of the drone includes: According to a preset conversion formula, the coordinates of the initial position are converted into local coordinates relative to the coordinate origin, where the local coordinates are the take-off position, wherein the preset conversion formula is:

[0015]

[0016]

[0017] Where xi is the X-axis coordinate of the takeoff position, yi is the Y-axis coordinate of the takeoff position; zi is the Z-axis coordinate of the takeoff position; loni represents the longitude value of the initial position, lati represents the latitude value of the initial position, and hi represents the altitude value of the initial position; lon0 represents the longitude value of the coordinate origin, lat0 represents the latitude value of the coordinate origin, h0 represents the altitude value of the coordinate origin, and Re represents the radius of the earth; , .

[0018] The beneficial effects of the present invention are as follows: the present invention provides a method for automatic placement of a drone cluster. Before automatic placement, all drones in the drone cluster are placed in a preparation area, and then path planning is performed for each drone in turn, and the placement task is performed according to the target path until all drones are traversed. Specifically: the take-off position of the drone is determined based on the position information of the drone in the preparation area, and then the placement area of the drone (i.e., the area for formation performance) is determined, and corresponding target positions are assigned to the drones in the placement area. Based on this, according to the take-off position and target position of each drone, path planning is performed separately for each drone to determine the target path of each drone. After the target path is determined, the target path corresponding to the drone is stored in a path library, and then the placement task can be performed on the drone based on the target path stored in the path library.

[0019] To sum up, based on the target path planned for each drone, the present invention can realize the automatic and dispersed placement of drones in sequence, thereby effectively saving manpower and material resources, and has the advantages of both deployment efficiency and high robustness, providing a reliable solution for the rapid deployment of drone clusters in dense cluster scenarios such as logistics inspections and dynamic light shows. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 The present invention is a flowchart of the steps of the automatic placement method of the drone cluster in one embodiment.

[0021] Figure 2 A coordinate diagram of a placement area and a preparation area in one embodiment of the automatic placement method of a drone cluster according to the present invention. DETAILED DESCRIPTION

[0022] To illustrate the technical content, achieved objectives and effects of the present invention in detail, the following description is given in conjunction with the embodiments and accompanying drawings.

[0023] Please refer to Figure 1 as well as Figure 2 The present invention provides a method for automatically placing a drone cluster, which includes the following steps: S1: Determine a preparation area and a placement area for a drone cluster, where the drone cluster includes multiple drones located in the preparation area; S2: Determine a take-off position of the UAV in the preparation area, and assign a target position to the UAV based on the placement area; S3: performing path planning for the UAV according to the take-off position and the target position to determine a target path for the UAV, and storing the target path of the UAV in a path library; S4: According to the target path stored in the path library, controlling the UAV to perform the placement task according to the target path; S5: Repeat steps S2 to S4 until all drones in the drone cluster are traversed.

[0024] As can be seen from the above description, the beneficial effects of the present invention are: the present invention provides a method for automatic placement of a drone cluster. Before the automatic placement is performed, all drones in the drone cluster are placed in a preparation area, and then the path planning is performed for each drone in turn and the placement task is performed according to the target path until all drones are traversed. Specifically: the take-off position of the drone is determined based on the position information of the drone in the preparation area, and then the placement area of the drone (that is, the area for the formation performance) is determined, and the corresponding target position is assigned to the drone in the placement area. Based on this, according to the take-off position and target position of each drone, path planning is performed separately for each drone to determine the target path of each drone. After the target path is determined, the target path corresponding to the drone is stored in the path library, and then the placement task can be performed on the drone based on the target path stored in the path library.

[0025] To sum up, based on the target path planned for each drone, the present invention can realize the automatic and dispersed placement of drones in sequence, thereby effectively saving manpower and material resources, and has the advantages of both deployment efficiency and high robustness, providing a reliable solution for the rapid deployment of drone clusters in dense cluster scenarios such as logistics inspections and dynamic light shows.

[0026] The following combination Figure 2 , each step in steps S1 to S5 and other optional steps are described in detail.

[0027] Step S1: Determine a preparation area and a placement area of a drone cluster, where the drone cluster includes multiple drones located in the preparation area.

[0028] The placement area is the area where the drone cluster finally performs the formation performance, such as Figure 2 As shown, area B is the placement area.

[0029] The preparation area is the initial positioning area of the drone cluster before performing the placement task. It mainly provides a starting point for the placement deployment of the drone cluster. After the drone is powered on, it will be placed in the preparation area. In actual application, the area of the preparation area will be smaller than the area of the placement area, and the two must be in the same plane or maintain a height difference of no more than 5 meters. Figure 2 As shown, the preparation area can be A / C / D area, which is not limited here.

[0030] Step S2: Determine the take-off position of the UAV in the preparation area, and assign a target position to the UAV based on the placement area.

[0031] The take-off position is the position information of the UAV in the preparation area, which can be used as the starting point for the path planning of the UAV to perform the placement task. It can be determined based on the corresponding positioning information, as follows: In an optional embodiment, in step S2, determining the take-off position of the UAV in the preparation area includes the following steps: after the UAV is powered on, obtaining the positioning information of the UAV, and determining the initial position of the UAV based on the positioning information; performing coordinate conversion on the initial position to obtain the take-off position of the UAV.

[0032] In practical applications, when a drone in a drone swarm is powered on, it communicates with the control terminal via wired or wireless means, transmitting data such as status information and positioning information back to the control terminal. Upon receiving this information, the control terminal can then determine the initial position of each drone based on the positioning information. For example, assuming drone 3 is drone 3, after drone 3 is powered on, the control terminal will obtain drone 3's positioning information and then determine drone 3's initial position based on this positioning information. It should be noted that since the drone swarm includes multiple drones, the process of assigning target positions to each drone in the swarm to determine the target path can be performed sequentially according to the order in which the drones were powered on, i.e., the drone that powered on first and is in a standby state will be the first to perform target path planning.

[0033] Coordinate transformation is the process of mapping the drone's global geographic coordinates to a local coordinate system based on a specific origin. It's important to note that the drone's initial position and takeoff position describe the same physical location (i.e., the drone's actual position in the preparation area). The difference between the two lies solely in the way the coordinate systems are expressed. The initial position represents the drone's actual location in the global geographic coordinate system, while the takeoff position represents the logical starting point in the local coordinate system obtained through coordinate transformation.

[0034] In practical applications, when performing coordinate conversion on the initial position, the coordinates of the initial position can be converted into local coordinates relative to the coordinate origin according to a preset conversion formula, where the local coordinates are the take-off position. The preset conversion formula is:

[0035]

[0036]

[0037] Where xi is the X-axis coordinate of the takeoff position, yi is the Y-axis coordinate of the takeoff position; zi is the Z-axis coordinate of the takeoff position; loni represents the longitude value of the initial position, lati represents the latitude value of the initial position, and hi represents the altitude value of the initial position; ; lon0 is the longitude value of the coordinate origin, lat0 is the latitude value of the coordinate origin, h0 is the altitude value of the coordinate origin, and Re is the radius of the earth; .

[0038] From the above description, it can be seen that by performing coordinate transformation on the initial position and converting the global coordinates into local coordinates based on the coordinate origin, the reference system can be unified, the logical consistency of path planning, conflict detection and collaborative control can be ensured, and the calculation can be simplified to improve the calculation efficiency.

[0039] The above describes in detail how to determine the takeoff position. The following describes how to assign a target position to the drone.

[0040] The target position is a coordinate point assigned to the drone based on the corresponding allocation rules. Each drone is assigned a corresponding coordinate point. The specific allocation rules can be set according to actual conditions. An optional embodiment is provided below.

[0041] In an optional embodiment, in step S2, assigning a target position to the drone based on the placement area includes the following steps: Obtaining a placement map corresponding to the placement area, wherein the placement map includes a plurality of coordinate points set according to placement spacing; Unoccupied coordinate points in the layout diagram are determined, and a coordinate point (excluding the origin) is selected from the unoccupied coordinate points according to a selection rule to serve as the target position.

[0042] The placement diagram is a coordinate grid diagram generated based on the placement area, and includes multiple coordinate points set according to the placement spacing in the placement diagram. The coordinate points are specific position points for placing drones generated on the two-dimensional plane (X-axis and Y-axis directions) of the placement area based on the preset placement spacing. In actual applications, after selecting the coordinate origin, multiple coordinate points can be generated in the placement area according to the corresponding placement spacing. The placement spacing can be selected according to actual conditions, such as 1 meter, which is not limited here. Figure 2 As shown, the points on area B are coordinate points, which together form a placement diagram. In practical applications, to facilitate the distinction of coordinate points in the placement diagram, the coordinate points can be numbered and assigned number information. The numbers are then used to assign target locations to avoid duplicate occupancy of target locations.

[0043] Unoccupied coordinate points are those that have not yet been assigned to another drone (i.e., not marked as occupied) or marked as having a path conflict. In practical applications, when assigning target locations to drones in a drone cluster, suppose drone 3 is the first drone, and coordinate points 1 and 2 in the coordinate point set are occupied by drones 1 and 2, respectively. In this case, when assigning coordinate points, the smallest coordinate point can be selected from the remaining coordinate points after removing coordinate points 1 and 2 as the target location. This prevents conflicts among multiple drones and improves mission safety.

[0044] The selection rule refers to the logical strategy for allocating the order of coordinate points, that is, the order of allocating coordinate points in the coordinate graph. In practical applications, the selection rule of selecting coordinate points from small to large on the X and Y axes can be used, such as: (1) X-axis priority rule: fix the Y-axis value, and after traversing all the coordinate points in the current row from small to large on the X axis, increase the Y-axis value to continue to the next row. For example: the origin is (0, 0), and the coordinate point allocation order is (1, 0) → (2, 0) → ... → (1, 1) → (2, 1) → .... (2) Y-axis priority rule: fix the X-axis value, and after traversing all the coordinate points in the current column from small to large on the Y axis, increase the X-axis value to enter the next column. For example: the coordinate point allocation order is: (0, 1) → (0, 2) → ... → (1, 1) → (1, 2) → .... (3) Alternating selection: dynamically adjust the coordinate point allocation order based on the priorities of the X and Y axes. For example, odd columns are prioritized by the X axis, and even columns are prioritized by the Y axis.

[0045] From the above description, we can see that through the preset coordinate traversal order (i.e., selection rule), the coordinate points in the placement map are allocated to the drones in an orderly manner to optimize the path planning efficiency and reduce the probability of conflict.

[0046] Step S3: performing path planning on the UAV according to the take-off position and the target position to determine the target path of the UAV, and storing the target path of the UAV in a path library.

[0047] The path planning is to generate a flight trajectory based on the take-off position of the UAV (i.e., the starting point of the local coordinate system) and the target position (i.e., the end point of the local coordinate system) to achieve the precise deployment of the UAV from the preparation area to the placement area.

[0048] The target path is a complete flight trajectory from the take-off position to the target position, and the path library refers to a database or cache area for storing the target path of the drone, supporting dynamic updates and conflict detection.

[0049] In an optional embodiment, in step S3, path planning is performed on the drone based on the take-off position of the drone and the target position to determine the target path of the drone, specifically comprising the following steps: S31: performing an initial path planning operation on the UAV based on the take-off position and the target position to determine a flight path of the UAV; S32: performing a path conflict check on the flight path to determine whether the flight path conflicts with a target path stored in a path library; if there is no path conflict, the flight path is used as the target path; if there is a path conflict, proceeding to step S33; S33: Replacing the target position and re-performing the path planning operation to generate a new flight path; performing a path conflict check on the new flight path to determine whether there is a path conflict; if there is no path conflict, the new flight path is used as the target path; if there is a path conflict, proceeding to step S34; S34: Repeat step S33 until there is no path conflict in the generated new flight path.

[0050] Path conflict detection involves analyzing a drone's flight trajectory to determine whether it overlaps or is too close to the flight paths of other drones, potentially leading to a collision. Path conflict occurs when two or more drones in a swarm occupy the same or adjacent spatial regions within the same timeframe when planning or executing a mission, resulting in a separation below a safety threshold and a potential collision risk.

[0051] In actual applications, if the flight path generated by the first path planning operation contains a path conflict, the target location is changed and the path planning operation is repeated (i.e., the second path planning operation) to generate a new flight path. Path conflict detection is then performed on this new flight path. Similarly, if the new flight path does not contain path conflicts, it can be used as the target path. If the new flight path does contain path conflicts, a second new flight path is generated (i.e., the third path planning operation), and path conflict detection is performed again to determine whether there are any path conflicts. Based on the results of the path conflict detection, it is determined whether a new flight path needs to be generated, until a new flight path free of path conflicts is obtained. This new flight path free of path conflicts is then used as the target path. In other words, the target path ultimately stored in the path library for each drone is free of path conflicts. Furthermore, when the target path of the drone is stored in the path library, the coordinate points corresponding to the target path are marked as occupied to prevent reuse.

[0052] It should be noted that when changing the target location (i.e., coordinate point), the above selection rules will also be followed. That is, a coordinate point (excluding the origin) will be selected from the unoccupied coordinate points in the layout according to the selection rules to serve as the target location. Before changing the target location, it is necessary to mark the target location as having a path conflict to prevent reuse.

[0053] As can be seen from the above description, when determining the target path for each drone, a three-step process of initial path generation, path conflict detection, and conflict resolution is implemented to ensure that each resulting target path is free of path conflicts. This prevents the risk of collisions caused by path conflicts and mission failures due to single-position conflicts, thereby improving system robustness and mission success rates. Furthermore, this invention only triggers target path replanning when a path conflict is detected, which reduces computational complexity and improves efficiency compared to global real-time obstacle avoidance.

[0054] In an optional embodiment, in step S3, the target path includes a vertical ascent segment, a horizontal translation segment, and a vertical landing segment; wherein the vertical ascent segment includes a flight trajectory that vertically rises from a take-off position to a predetermined height, the horizontal translation segment includes a flight trajectory that flies from the predetermined height to directly above the target position, and the vertical landing segment includes a flight trajectory that vertically descends from directly above the target position to the target position.

[0055] In the invention, the target path (e.g., flight path / new flight path) generated by the path planning operation for the UAV is composed of three flight trajectories: (1) Vertical ascent segment: vertical ascent from the take-off position to a preset height directly above the take-off position. The preset height range is 5-20 meters. Each UAV can gradually increase or decrease the preset height in sequence during path planning. Taking a vertical spacing of 5 meters as an example, the preset heights are 5 meters, 10 meters, 15 meters, and 20 meters, and the same applies to gradually decreasing heights. (2) Horizontal translation segment: horizontal movement from a preset height directly above the take-off position to directly above the target position. (3) Vertical landing segment: vertical descent from directly above the target position to the target position.

[0056] From the above description, it can be seen that the flight trajectory of the drone is decomposed into three stages, namely, the vertical ascent stage, the horizontal translation stage, and the vertical landing stage. Obviously, the present invention does not add a curved trajectory to the flight trajectory, which can avoid complex curve calculations, thereby effectively simplifying the path planning algorithm, reducing the amount of path planning calculations, and improving deployment efficiency.

[0057] In an optional embodiment, in step S32, a path conflict detection is performed on the flight path to determine whether the flight path conflicts with a target path stored in a path library, including the following steps: S321: Dividing the placement task of the UAV into flight time slots based on a reference time; S322: Determine a first time slot position of the flight path within each flight time slot, and determine a second time slot position of the target path stored in the path library within each flight time slot; S323: Determine whether the UAV has a collision risk based on the first time slot position and the second time slot position; S324: If there is a collision risk, it is determined that there is a path conflict in the flight path; if there is no collision risk, it is determined that there is no path conflict in the flight path.

[0058] The reference time is a time standard uniformly followed by all drones in a swarm, used to coordinate the synchronization of formation movements. Time synchronization can be achieved through GPS or ground stations. The reference time is a globally unified clock reference point (e.g., t=0), from which the placement times of all drones in the swarm are calculated. It should be noted that during takeoff, the drone in the swarm that has planned its target path first will take off first.

[0059] A flight slot is a continuous, independent time period formed by dividing the drone placement task into time segments based on a reference time. Each flight slot represents an independent time segment. The first slot position refers to the coordinate position of the drone within the corresponding flight slot, and the second slot position refers to the coordinate position of the stored drone corresponding to the target path in the path library within the corresponding flight slot.

[0060] In practical applications, when performing flight path conflict detection, it is first necessary to divide the placement task into flight time slots based on a unified time reference. In specific implementation, the flight time in the total task duration of the placement task can be divided into N flight time slots (for example, each flight time slot is set to 0.1 seconds). Based on the division result, the time slot position of the flight path and the target path stored in the path library in each flight time slot is determined, even if the flight path and each target path stored in the path library can correspond to a spatial position information in each flight time slot. Specifically, the continuous flight trajectory (i.e., the flight path / target path stored in the path library) can be divided based on the flight time slot to determine the position coordinates of the flight path in each flight time slot (i.e., the first time slot position) and the position coordinates of the target path stored in the path library in each flight time slot (i.e., the second time slot position).

[0061] For example, assuming the base time is t=0, and drone A generates a 5-second flight path at t=1.5 seconds (this t=1.5 seconds can be considered drone A's takeoff time). Based on the base time, drone A takes off at t=1.5 seconds and arrives at the preparation area at t=6.5 seconds, for a total flight duration of 5 seconds. Assuming each flight slot is 0.5 seconds, drone A can be divided into 10 flight slots. Specifically, based on the base time, the flight times corresponding to these 10 flight slots are: t=1.5 seconds to t=2.0 seconds; t=2.0 seconds to t=2.5 seconds; t=2.5 seconds to t=3.0 seconds; ..., t=6.0 seconds to t=6.5 seconds. Based on drone A's flight path and these 10 flight slots, drone A's position (i.e., the first slot position) at each flight slot (i.e., flight moment) is determined.

[0062] Assume that the path library stores multiple target paths (such as target paths 1, 2, etc.), among which target path 1 corresponds to drone 1. Taking target path 1 as an example, similarly, target path 1 is divided into multiple flight time slots of 0.5 seconds, and then the second time slot position is obtained.

[0063] It should be noted that the step of performing path conflict detection on the new flight path is similar to the step of performing path conflict detection on the above-mentioned flight path, and the flight path is simply replaced with the new flight path.

[0064] The collision risk refers to a potential collision state where the time slot distance between two drones is less than a preset safety threshold (e.g., 1 meter). When determining whether a drone has a collision risk, a distance calculation can be performed to determine whether the drone's flight path is too close to the target path in the path library within a certain flight time slot, as follows: In an optional embodiment, step S323 may include the following steps: calculating the time slot distance between the first time slot position and the second time slot position of each target path in the path library within the same flight time slot (i.e., within the same flight time), until all flight time slots are traversed; determining whether there is a situation in one or more flight time slots where the time slot distance is less than a safety threshold; if so, determining that the drone is at risk of collision; if not, determining that the drone is not at risk of collision.

[0065] The distance calculation refers to calculating the time slot distance between the drone and the drones stored in the path library within the same flight time slot, that is, calculating the time slot distance (i.e., the minimum distance) between the first time slot position within the same flight time slot and the second time slot position of each target path in the path library. Assuming there are N flight time slots, within the same flight time slot, the coordinates of the first flight time slot position are ( X j , Y j , Z j ), the position coordinates of the second time slot position of each target path in the path library are ( X k , Y k , Z k ), the calculation formula of the time slot distance L is:

[0066] Among them, 1≤j≤k≤N; X j is the X-axis coordinate of the first time slot position, Y j is the Y-axis coordinate of the first time slot position, Z j The Z-axis coordinate of the first time slot position; X k is the X-axis coordinate of the second time slot position, Y k is the Y-axis coordinate of the second time slot position, Z k The Z-axis coordinate of the second time slot position.

[0067] It should be noted that when the corresponding target path is stored in the path library, the identification information of the drone will be correspondingly associated and stored in the path library. When the distance between the current drone and the drones in the path library is subsequently calculated, the drones in the path library can be determined based on the identification information.

[0068] From the above description, it can be seen that based on the division of flight time slots, the spatial position of the target path in each flight time slot is determined, so that path conflict detection can be performed on segmented data, significantly reducing the complexity of real-time calculations and improving the efficiency of conflict detection.

[0069] Step S4: According to the target path stored in the path library, the UAV is controlled to perform the placement task according to the target path.

[0070] In actual applications, drones can perform placement tasks according to the target path in the path library, that is, take off from the preparation area to the placement area according to the target path to achieve automatic placement and deployment of drones, thereby improving deployment efficiency and saving manpower and material resources.

[0071] Step S5: Repeat steps S2 to S4 until all drones in the drone cluster are traversed.

[0072] In actual applications, each drone in the drone cluster will execute the above steps S2 to S4 accordingly. After each drone executes the above steps S2 to S3, it can obtain the corresponding target path, thereby ensuring that each drone in the drone cluster is assigned a unique target path.

[0073] In summary, the automatic placement method of a drone cluster provided by the present invention realizes the automatic placement of each drone in the drone cluster, saving manpower and material resources, and simplifies the path planning algorithm in the process, combining the advantages of efficient operation and high robustness, providing a reliable solution for dense cluster scenarios such as logistics inspections and dynamic light shows.

[0074] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's description and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for automatically placing a drone cluster, characterized in that: The following steps are involved: S1: Determine a preparation area and a placement area for a drone cluster, where the drone cluster includes multiple drones located in the preparation area; S2: Determine a take-off position of the UAV in the preparation area, and assign a target position to the UAV based on the placement area; S3: performing path planning for the UAV according to the take-off position and the target position to determine a target path for the UAV, and storing the target path of the UAV in a path library; S4: According to the target path stored in the path library, controlling the UAV to perform the placement task according to the target path; S5: Repeat steps S2 to S4 until all drones in the drone cluster are traversed.

2. The automatic placement method according to claim 1, characterized in that: In step S2, assigning a target position to the drone based on the placement area includes: Obtaining a placement map corresponding to the placement area, wherein the placement map includes a plurality of coordinate points set according to placement spacing; Unoccupied coordinate points in the placement diagram are determined, and a coordinate point is selected from the unoccupied coordinate points according to a selection rule to serve as the target position.

3. The automatic placement method according to claim 1, characterized in that: In step S3, path planning is performed on the UAV according to the take-off position and the target position to determine the target path of the UAV, which specifically includes the following steps: S31: performing an initial path planning operation on the UAV based on the take-off position and the target position to determine a flight path of the UAV; S32: performing a path conflict check on the flight path to determine whether the flight path conflicts with a target path stored in a path library; if there is no path conflict, the flight path is used as the target path; if there is a path conflict, proceeding to step S33; S33: Replacing the target position and re-performing the path planning operation to generate a new flight path; performing a path conflict check on the new flight path to determine whether there is a path conflict; if there is no path conflict, the new flight path is used as the target path; if there is a path conflict, proceeding to step S34; S34: Repeat step S33 until there is no path conflict in the generated new flight path.

4. The automatic placement method according to claim 3, characterized in that: In step S32, a path conflict detection is performed on the flight path to determine whether the flight path conflicts with the target path stored in the path library, including the following steps: S321: Dividing the placement task of the UAV into flight time slots based on a reference time; S322: Determine a first time slot position of the flight path within each flight time slot, and determine a second time slot position of the target path stored in the path library within each flight time slot; S323: Determine whether the UAV has a collision risk based on the first time slot position and the second time slot position; S324: If there is a collision risk, it is determined that there is a path conflict in the flight path; if there is no collision risk, it is determined that there is no path conflict in the flight path.

5. The automatic placement method according to claim 4, characterized in that: In step S323, the following steps are included: Calculating the time slot distance between the first time slot position and the second time slot position of each target path in the path library within the same flight time slot until all flight time slots are traversed; Determine whether there is a situation in one or more flight time slots where the time slot distance is less than a safety threshold. If so, determine that the drone has a collision risk; if not, determine that the drone does not have a collision risk.

6. The automatic placement method according to claim 3, characterized in that: In step S3, the target path includes a vertical ascent segment, a horizontal translation segment, and a vertical landing segment; wherein the vertical ascent segment includes a flight trajectory that vertically rises from the take-off position to a predetermined height, the horizontal translation segment includes a flight trajectory that flies from the predetermined height to directly above the target position, and the vertical landing segment includes a flight trajectory that vertically descends from directly above the target position to the target position.

7. The automatic placement method according to claim 1, characterized in that: The area of the preparation area is smaller than that of the placement area, and the preparation area and the placement area are in the same plane or the height difference between the two does not exceed a preset threshold.

8. The automatic placement method according to claim 1, characterized in that: In step S3, the following steps are also included: when storing the target path of the UAV into the path library, the coordinate points corresponding to the target path are marked as occupied.

9. The automatic placement method according to claim 1, characterized in that: In step S2, determining the take-off position of the UAV in the preparation area includes: After the UAV is powered on, the positioning information of the UAV is obtained, and the initial position of the UAV is determined according to the positioning information; The initial position is coordinate-converted to obtain the take-off position of the UAV.

10. The automatic placement method according to claim 9, characterized in that: Performing coordinate transformation on the initial position to obtain a take-off position of the UAV includes: According to a preset conversion formula, the coordinates of the initial position are converted into local coordinates relative to the coordinate origin, where the local coordinates are the take-off position, wherein the preset conversion formula is: Where xi is the X-axis coordinate of the takeoff position, yi is the Y-axis coordinate of the takeoff position; zi is the Z-axis coordinate of the takeoff position; loni represents the longitude value of the initial position, lati represents the latitude value of the initial position, and hi represents the altitude value of the initial position; lon0 represents the longitude value of the coordinate origin, lat0 represents the latitude value of the coordinate origin, h0 represents the altitude value of the coordinate origin, and Re represents the radius of the earth; , .

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