Unmanned aerial vehicle cluster formation obstacle avoidance control method based on distance measurement and sector scanning and related device

Abstract

The invention discloses an unmanned aerial vehicle cluster formation obstacle avoidance control method based on distance measurement and sector scanning and a related device, and relates to the field of unmanned aerial vehicle cluster cooperative formation collision avoidance, the method comprises the following steps: periodically obtaining obstacle distance measurement information of an unmanned aerial vehicle, and when the obstacle distance measurement information is less than a first distance threshold, outputting an obstacle detection instruction; when an obstacle detection mode is entered, determining an obstacle avoidance direction according to the obstacle distance measurement information, the obtained current position information of the unmanned aerial vehicle and a sector scanning mode, generating an obstacle avoidance trajectory by using the obstacle avoidance direction and the obtained current position information of the unmanned aerial vehicle, the target point position information and the obstacle distance measurement information, and when the obstacle avoidance trajectory is generated, outputting an obstacle avoidance execution instruction; when the unmanned aerial vehicle enters the obstacle avoidance execution mode, obstacle avoidance operation is carried out according to the obstacle avoidance track, whether a recovery condition is met or not is judged, if yes, a route recovery instruction is output, and high-consistency and high-reliability cooperative obstacle avoidance and formation keeping of the unmanned aerial vehicle cluster in the complex dynamic environment with the perception limited are achieved.

Description

Technical Field

[0001] This application relates to the field of collaborative formation collision avoidance in unmanned aerial vehicle (UAV) swarms, and in particular to a method and related apparatus for UAV swarm obstacle avoidance control based on ranging and sector scanning. Background Technology

[0002] With the continued development and utilization of low-altitude airspace, and the widespread deployment of UAVs in various scenarios such as military, emergency response, environmental protection, and transportation, autonomous collaboration and intelligent cooperation among UAV swarms are gradually becoming a key direction for the development of low-altitude systems.

[0003] Currently, multi-drone swarms are widely used in scenarios such as ecological monitoring, emergency search and rescue, and logistics delivery. However, in dynamic environments with obstacles, especially in collaborative swarm missions, their collision avoidance capabilities still face significant technical bottlenecks. Most existing obstacle avoidance algorithms heavily rely on high-precision sensor devices such as LiDAR and depth cameras for perception information. However, these methods perform poorly in environments without GPS, in low light, or with severe occlusion, failing to achieve their intended performance. Furthermore, the lack of efficient information exchange and obstacle avoidance strategy coordination mechanisms among drones within the swarm leads to asynchronous reactions, often resulting in independent local planning, path conflicts, or resource waste, thus affecting the overall swarm performance.

[0004] Therefore, there is an urgent need for a drone swarm formation obstacle avoidance control method with strong environmental adaptability and collaborative consistency, which can achieve stable and reliable formation collaboration and autonomous obstacle avoidance in complex, dynamic, and perception-limited environments. Summary of the Invention

[0005] The purpose of this application is to provide a method and related device for obstacle avoidance control of UAV swarm formation based on ranging and sector scanning, which can achieve high consistency and high reliability of UAV swarm collaborative obstacle avoidance and formation maintenance in complex dynamic environments with limited perception and no GPS.

[0006] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for obstacle avoidance control in UAV swarm formation based on ranging and sector scanning, including: During the flight of the UAV swarm, for each UAV, the UAV swarm formation obstacle avoidance control method based on ranging and sector scanning is executed; the UAV swarm formation obstacle avoidance control method based on ranging and sector scanning includes: The system periodically acquires obstacle ranging information from the UAV, and when the ranging value in the obstacle ranging information is less than a first distance threshold, it outputs an obstacle detection command; the obstacle detection command is used to control the UAV to enter the obstacle detection mode. When the UAV enters obstacle detection mode, the obstacle avoidance direction is determined based on the obstacle ranging information, the acquired current position information of the UAV, and the sector scanning method. An obstacle avoidance trajectory is generated using the obstacle avoidance direction, the acquired current position information of the UAV, the target point position information, and the obstacle ranging information. When the obstacle avoidance trajectory is generated, an obstacle avoidance execution command is output. The obstacle avoidance execution command is used to control the UAV to enter the obstacle avoidance execution mode. The target point is a preset mission waypoint.

[0007] When the UAV enters the obstacle avoidance execution mode, it performs obstacle avoidance operations according to the generated obstacle avoidance trajectory and determines whether the recovery conditions are met. If the recovery conditions are met, a route recovery command is output. The route recovery command is used to control the UAV to enter the main route return mode. The main route is the expected flight path preset by the UAV cluster before performing the obstacle avoidance operation.

[0008] Secondly, this application provides a drone swarm formation obstacle avoidance control system based on ranging and sector scanning, including: The drone swarm includes multiple drones; the drone swarm formation obstacle avoidance control system based on ranging and sector scanning includes: UWB module, RTK module and control module installed on each of the aforementioned UAVs; The UWB module is used to periodically collect obstacle ranging information; The RTK module is used to periodically collect the location information of the UAV; The control module is used to execute the UAV swarm formation obstacle avoidance control method based on ranging and sector scanning described in any of the above-mentioned methods.

[0009] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a method and related apparatus for obstacle avoidance control in UAV swarm formation based on ranging and sector scanning. First, it uses periodic acquisition of obstacle ranging information as the core perception method, combined with sector scanning to determine the obstacle avoidance direction. This method does not rely on sensors susceptible to environmental interference, such as vision or lidar, and is particularly suitable for dynamic scenarios without GPS, in low light, or with severe obstruction. The UWB module ranging has strong penetration and good anti-interference capabilities, enabling stable and real-time distance perception in complex environments, thus compensating for the perception blind spots of traditional sensors in specific environments.

[0010] Furthermore, the combination of state-based scheduling and sector scanning enables lightweight and highly consistent obstacle avoidance decisions. Sector scanning simplifies environmental perception into distance assessment in several directions, resulting in low computational complexity and suitability for real-time processing. The state machine mechanism ensures that the UAV can quickly switch behavior modes (such as obstacle detection - obstacle avoidance execution - route recovery) according to environmental changes during flight. Each UAV follows the same state machine logic, avoiding path conflicts caused by inconsistent individual decisions. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A flowchart illustrating an embodiment of this application provides a method for obstacle avoidance control in UAV swarm formation based on ranging and sector scanning. Figure 2 A flowchart illustrating the transition conditions between each state; Figure 3 A schematic diagram of the functional modules of a drone swarm formation obstacle avoidance control system based on ranging and sector scanning, provided for an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0014] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0015] This application primarily considers UAV swarm mission scenarios. In three-dimensional space, it ensures that UAVs, when performing collaborative formation missions within a swarm system, can accurately and promptly perceive their surroundings and avoid collisions. Throughout the flight, the UAV's flight state is categorized into six states: normal flight, emergency braking, risk assessment, path planning, detour execution, and route recovery. Each UAV continuously switches between these states and takes corresponding behavioral measures at different stages, thereby achieving unified scheduling of collaborative formation and dynamic obstacle avoidance.

[0016] Before the mission begins, the UAVs must complete system initialization, including RTK module initialization, UWB module initialization, and acquisition of a positioning source. Each UAV is equipped with a UWB module, which can obtain the ranging distance between it and other UAVs. After initialization, it enters takeoff mode, ascends to the designated flight altitude, and stabilizes into the airborne route preparation state. At this time, the controller enters normal flight mode and awaits mission execution. The UAV swarm flies along the preset mission waypoints in an unobstructed environment.

[0017] In one exemplary embodiment, such as Figure 1 As shown, a method for obstacle avoidance control in UAV swarm formation based on ranging and sector scanning is provided. In this embodiment, the method includes steps 101 to 103. Wherein: Step 101: Periodically acquire obstacle ranging information of the UAV, and when the ranging value in the obstacle ranging information is less than the first distance threshold, output an obstacle detection command; the obstacle detection command is used to control the UAV to enter the obstacle detection mode.

[0018] Step 102: When the UAV enters obstacle detection mode, the obstacle avoidance direction is determined based on the obstacle ranging information, the acquired current position information of the UAV, and the sector scanning method. An obstacle avoidance trajectory is generated using the obstacle avoidance direction, the acquired current position information of the UAV, the target point position information, and the obstacle ranging information. When the obstacle avoidance trajectory is generated, an obstacle avoidance execution command is output. The obstacle avoidance execution command is used to control the UAV to enter the obstacle avoidance execution mode. The target point is a preset mission waypoint.

[0019] Step 103: When the UAV enters the obstacle avoidance execution mode, it performs obstacle avoidance operation according to the generated obstacle avoidance trajectory and determines whether the recovery conditions are met. If the recovery conditions are met, a route recovery command is output. The route recovery command is used to control the UAV to enter the main route return mode. The main route is the expected flight path preset by the UAV cluster before performing the obstacle avoidance operation.

[0020] In another exemplary embodiment of this application, step 102, determining the obstacle avoidance direction based on the obstacle ranging information, the acquired UAV current position information, and the sector scanning method, is replaced by steps 1021 to 1024: Step 1021: Based on the acquired current location information of the UAV, construct a circular sensing area and divide the circular sensing area into several identical sectors; the center of the circular sensing area is the current location of the UAV.

[0021] Step 1022: Scan and evaluate each sector to determine the center direction angle of each sector and the center direction angle of the sector where the target point's heading is located.

[0022] Step 1023: Based on the center direction angle of each sector, the center direction angle of the sector where the target point's heading is located, and the obstacle ranging information, obtain the comprehensive cost factor of each sector.

[0023] Step 1024: Based on the comprehensive cost factor of each sector, the smallest comprehensive cost factor is selected, and the smallest comprehensive cost factor is compared with a first preset obstacle avoidance threshold. If the smallest comprehensive cost factor is less than the first preset obstacle avoidance threshold, the formula is: The obstacle avoidance direction is then determined based on the current velocity vector of the drone and the comprehensive cost factor of each sector.

[0024] In another exemplary embodiment of this application, in step 1023, based on the center direction angle of each sector, the center direction angle of the sector where the target point's heading is located, and the obstacle ranging information, a comprehensive cost factor for each sector is obtained. For each sector, the following steps 1025-1026 are performed: Step 1025: Based on the center direction angle of the current evaluation sector and the center direction angle of the sector where the target point's heading is located, calculate the heading correction angle of the current evaluation sector; the current evaluation sector can be any sector; the formula for calculating the heading correction angle of the current evaluation sector is: ; in, The heading correction angle for the current evaluation sector. The center direction angle of the current evaluation sector. The direction angle is the center direction angle of the sector where the target point's heading is located. To obtain and The minimum value between.

[0025] Step 1026: Based on the obstacle ranging information and the heading correction angle of the current evaluation sector, calculate the comprehensive cost factor of the current evaluation sector; the calculation formula for the comprehensive cost factor of the current evaluation sector is: ; in, This serves as the overall cost factor for the current evaluation sector. To determine the distance between the current assessment sector and the nearest obstacle, Weight decay coefficient (used to control) (attenuation rate) This is the angle correction factor (used to adjust the degree of influence of the angle difference on the distance correction). This represents the weighting coefficient, ranging from [0,1]. This indicates the distance after correction by the heading angle. It is used to appropriately increase the distance value when the obstacle is not directly in front of the current sector to avoid the risk of misjudgment.

[0026] In another exemplary embodiment of this application, step 1024, determining the obstacle avoidance direction based on the acquired current velocity vector of the UAV and the comprehensive cost factor of each sector, is replaced by steps 1027-1028: Step 1027: Using the current velocity vector of the UAV as the reference direction, the sector is divided into a left sector group and a right sector group. The sum of the comprehensive cost factors of each sector in the left sector group and the right sector group is calculated respectively to obtain the total comprehensive cost factor of the left sector group and the total comprehensive cost factor of the right sector group.

[0027] Step 1028: Compare the total comprehensive cost factor of the left sector group with the total comprehensive cost factor of the right sector group. If the total comprehensive cost factor of the left sector group is greater than the total comprehensive cost factor of the right sector group, then the vertical direction to the left of the reference direction is determined as the obstacle avoidance direction; otherwise, the vertical direction to the right of the reference direction is determined as the obstacle avoidance direction. In this step, first define... Then we have the following formula: ;in, The total comprehensive cost factor representing the left sector group, The total comprehensive cost factor represents the right sector group.

[0028] In another exemplary embodiment of this application, step 102, which involves generating an obstacle avoidance trajectory using the obstacle avoidance direction and the acquired current location information of the UAV, the target point location information, and the obstacle ranging information, is replaced by steps 1029 to 1033: Step 1029: Based on the current location information of the UAV and the location information of the target point, obtain the target attraction unit velocity vector; the calculation formula for the target attraction unit velocity vector is: ; in, The target's unit velocity vector is the force of attraction. Let the target point's position vector be... This is the current position vector of the drone. For mold length operation, The value is a local constant. The current position vector of the UAV is determined by the current position information of the UAV, and the position vector of the target point is determined by the position information of the target point.

[0029] Step 1030: Calculate the total obstacle avoidance speed vector based on the current position vector of each obstacle and the current position vector of the UAV.

[0030] Step 1031: Using a dynamic weighting function, the target attraction unit velocity vector and the total obstacle avoidance velocity vector are fused to obtain the desired velocity vector; the formula for calculating the desired velocity vector is: ; in, For the desired velocity vector, The fusion coefficient (controlling the weighted relationship between the target's attractive unit velocity vector and the total obstacle avoidance velocity vector; the closer to the obstacle, the higher the fusion coefficient). The larger ( This represents the total obstacle avoidance velocity vector.

[0031] Step 1032: Based on the desired velocity vector and the preset maximum horizontal flight speed of the UAV, the final horizontal control velocity vector is obtained. The formula for calculating the final horizontal control velocity vector is as follows: ; in, For the final horizontal control velocity vector, The maximum horizontal flight speed of the drone. Let be the desired velocity vector.

[0032] Step 1033: Generate an obstacle avoidance trajectory based on the obstacle avoidance direction and the final horizontal control velocity vector.

[0033] In another exemplary embodiment of this application, step 1030, calculating the total obstacle avoidance speed vector based on the current position vector of each obstacle and the current position vector of the UAV, is replaced by the following steps: For each obstacle, perform the first operation to obtain the repulsion velocity vector of each obstacle, and then sum the weighted repulsion velocity vectors of each obstacle to obtain the total obstacle avoidance velocity vector.

[0034] The first operation is specifically as follows: The current position vector of the target obstacle is determined based on the obstacle ranging information; the target obstacle can be any obstacle.

[0035] Based on the current position vector of the UAV and the current position vector of the target obstacle, the vector pointing from the target obstacle to the UAV and its magnitude are obtained; the formula for the vector pointing from the target obstacle to the UAV is: ,in, Let the target obstacle's current position vector be... Let be the current position vector of the UAV; the formula for the magnitude of the vector pointing from the target obstacle to the UAV is: .

[0036] The repulsion velocity vector of the target obstacle is calculated based on the vector pointing from the target obstacle to the UAV and its magnitude.

[0037] The formula for calculating the repulsion velocity vector of the target obstacle is: ; in, Let V be the repulsion velocity vector of the target obstacle. This is the vector pointing from the target obstacle to the drone. Let be the magnitude of the vector pointing from the target obstacle to the UAV.

[0038] In another exemplary embodiment of this application, step 1033, which generates an obstacle avoidance trajectory based on the obstacle avoidance direction and the final horizontal control velocity vector, is replaced by steps 1034-1035: Step 1034: Generate an initial obstacle avoidance trajectory based on the obstacle avoidance direction and the final horizontal control velocity vector.

[0039] Step 1035: Use a Bezier curve to smooth the initial obstacle avoidance trajectory and generate the final obstacle avoidance trajectory.

[0040] In another exemplary embodiment of this application, when the UAV enters the main flight path return mode, a second-order Bézier curve recovery strategy is adopted to generate the main flight path recovery trajectory. The second-order Bézier curve recovery strategy is specifically as follows: ; in, This represents the real-time position vector of the UAV on the recovered trajectory of the main flight path. For proportional parameters, This refers to the position vector of the drone when it finishes obstacle avoidance maneuvers and begins preparing to resume its main flight path. Let the target point's position vector be... This is the position vector of the control point, that is, the position vector of the point in the direction of the current heading extension.

[0041] Specifically, the selection of control points and the determination of their extension distances require consideration of multiple factors: On the one hand, the extension distance is determined based on the drone's motion characteristics: In some studies involving vehicle trajectory planning, parameters for control points are determined based on the vehicle's (analogous to a drone) turning ability, speed, and other kinematic constraints. For drones, if the flight speed is high, the extension distance of the control point should be relatively large to ensure a smooth recovery process without sharp turns. Assuming the drone's current speed is... v Its minimum turning radius is r It can be based on the relationship between turning radius and speed r=2ωv (ω is the angular velocity), estimate the appropriate extension distance. For example, if the drone speed v = 10 m / s and the maximum allowable angular velocity ω = 0.5 rad / s, then the minimum turning radius r = 20 m. The extension distance can be set with reference to this turning radius, such as 15-25 m. The specific value still needs to be adjusted based on actual flight tests.

[0042] On the other hand, the extension distance is determined based on the mission scenario and obstacle distribution: if the UAV is in an environment with dense obstacles, the extension distance of the control point can be appropriately reduced in order to avoid obstacles and return to the main flight path as quickly as possible, making the recovery trajectory more compact. For example, when performing missions in densely built-up urban areas, the extension distance can be set to 5-10m; if in an open field environment, the extension distance can be increased to 20-30m to reduce unnecessary path detours and improve flight efficiency.

[0043] On the other hand, the extension distance is determined by combining the properties of Bézier curves with flight accuracy requirements: According to the characteristics of Bézier curves, control points significantly affect the curve shape. If high flight accuracy is required and it is desired to restore the trajectory closer to the original flight path, the extension distance can be appropriately shortened to make the curve closer to the line connecting the start and end points. If accuracy requirements are relatively low and flight efficiency is more important, the extension distance can be increased to make the curve smoother. For example, when performing high-precision surveying tasks, the extension distance is set to 8-12m; when performing tasks with high efficiency requirements, such as logistics and distribution, the extension distance can be set to 15-20m.

[0044] In another exemplary embodiment of this application, in step 203, the recovery condition is: Determine whether the following conditions are met simultaneously: (a) The overall cost factor of the current evaluation sector of the UAV is greater than or equal to the first preset obstacle avoidance threshold; (b) The distance measured to the obstacle is greater than or equal to the first distance threshold for M consecutive periods; where M is a positive integer greater than 1; (c) The angle between the current velocity vector of the UAV and the unit velocity vector of the target attraction is less than the recovery angle threshold.

[0045] In another exemplary embodiment of this application, the drone state is defined as shown in Table 1: Table 1 UAV Status Definitions

[0046] like Figure 2 As shown, the transition conditions between each state are as follows: S1→S2 (Entering obstacle avoidance state): The distance value in the obstacle ranging information is less than the first distance threshold, and the minimum comprehensive cost factor is less than the first preset obstacle avoidance threshold.

[0047] S2→S3 (Entering the obstacle avoidance trajectory): The planner successfully generates the obstacle avoidance trajectory or calculates the detour direction vector.

[0048] S3→S4 (Prepare for recovery): Recovery command satisfied.

[0049] S4→S1 (Recovery End): When the recovery end condition is met, the route recovery status is determined to be complete; the recovery end condition is specifically: ,in, The real-time position vector of the UAV on the main flight path recovery trajectory. Let the target point's position vector be... This is the position accuracy threshold, typically set to 0.5m.

[0050] Throughout the process, the drones are controlled to perform unified scheduling of formation and obstacle avoidance through continuous switching between state machines. The goal of the state machine design is primarily to enable the drones to possess the following functions in dynamic environments: (a) Actively enter obstacle avoidance mode and detect obstacles based on sensors; (b) Track the obstacle avoidance trajectory or adjust the direction; (c) Resume the main route after the conditions are met; (d) High robustness and real-time responsiveness.

[0051] This application also provides an application scenario in which the above-described UAV swarm formation obstacle avoidance control method based on ranging and sector scanning is applied. Specifically, the UAV swarm formation obstacle avoidance control method based on ranging and sector scanning provided in this embodiment can be applied in UAV swarm collaborative operation and dynamic obstacle avoidance scenarios. This scenario includes formation maintenance and trajectory tracking, real-time dynamic obstacle perception and risk assessment, and multi-UAV collaborative obstacle avoidance and route recovery. The UAV swarm formation obstacle avoidance control method based on ranging and sector scanning provided in this embodiment belongs to the real-time dynamic obstacle perception and risk assessment stage in the autonomous collaborative control link of UAV swarms.

[0052] In addition, this application also provides a complete operating procedure: Step 1: System Initialization and Takeoff Phase Before the mission begins, the UAV needs to complete system initialization, including acquiring a positioning source, initializing the RTK module, and initializing the UWB module. Each UAV is equipped with a UWB module, which can obtain the ranging distance between it and other UAVs. After initialization, it enters takeoff mode, ascends to the designated flight altitude, and stabilizes into the airborne route preparation state. At this time, the controller enters state S1 to wait for mission execution.

[0053] Step 2: Normal Flight and Formation Maintenance Drone swarms fly along set waypoints or desired paths in unobstructed environments.

[0054] Step 3: Obstacle Detection and Condition Judgment During flight, the UAV periodically receives obstacle ranging information (obtained via UWB module). If the detected ranging value is less than the threshold, it switches to S2 state to perform obstacle detection.

[0055] Step 4: Sector Detection In S2 state, the system obtains the obstacle avoidance direction through sector scanning, generates the obstacle avoidance trajectory, and then smooths it using a Bezier curve.

[0056] Step 5: Detour and restore the flight path based on obstacle avoidance direction. After obstacle avoidance is triggered, the local obstacle avoidance direction is determined by combining the obstacle direction information, and the movement is carried out in this direction. During the obstacle avoidance process, the obstacle distance is continuously detected. When the recovery conditions are met, the smooth trajectory is restored according to the second-order Bézier curve.

[0057] Step 6: State Machine Scheduling Throughout the process, the drones are controlled to complete the unified scheduling of the entire formation and obstacle avoidance by constantly switching between various state machines.

[0058] Based on the same inventive concept, this application also provides a system for implementing the above-mentioned obstacle avoidance control method for UAV swarm formation based on ranging and sector scanning. The solution provided by this system is similar to the implementation scheme described in the above method. Therefore, the specific limitations of one or more embodiments of the UAV swarm formation obstacle avoidance control system based on ranging and sector scanning provided below can be found in the limitations of the UAV swarm formation obstacle avoidance control method based on ranging and sector scanning described above, and will not be repeated here.

[0059] The drone swarm includes multiple drones, as in an exemplary embodiment, such as Figure 3 As shown, a drone swarm formation obstacle avoidance control system based on ranging and sector scanning is provided, including: The UWB module, RTK module, and control module are installed on each of the aforementioned UAVs.

[0060] The UWB module is used to periodically collect obstacle ranging information.

[0061] The RTK module is used to periodically collect the location information of the UAV.

[0062] The control module is used to execute the UAV swarm formation obstacle avoidance control method based on ranging and sector scanning described in any of the above-mentioned methods.

[0063] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 4 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores obstacle ranging information for the UAV. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a UAV swarm formation obstacle avoidance control method based on ranging and sector scanning.

[0064] Those skilled in the art will understand that Figure 4The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0065] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0066] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0067] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0068] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0069] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0070] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0071] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0072] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for obstacle avoidance control in UAV swarm formation based on ranging and sector scanning, characterized in that, During the flight of the UAV swarm, the UAV swarm formation obstacle avoidance control method based on ranging and sector scanning is executed for each UAV. The UAV swarm formation obstacle avoidance control method based on ranging and sector scanning includes: The system periodically acquires obstacle ranging information of the UAV, and when the ranging value in the obstacle ranging information is less than a first distance threshold, it outputs an obstacle detection command; the obstacle detection command is used to control the UAV to enter the obstacle detection mode. When the UAV enters obstacle detection mode, the obstacle avoidance direction is determined based on the obstacle ranging information, the acquired current position information of the UAV, and the sector scanning method. An obstacle avoidance trajectory is generated using the obstacle avoidance direction, the acquired current position information of the UAV, the target point position information, and the obstacle ranging information. When the obstacle avoidance trajectory is generated, an obstacle avoidance execution command is output. This command controls the UAV to enter obstacle avoidance execution mode. The target point is a preset mission waypoint. When the UAV enters the obstacle avoidance execution mode, it performs obstacle avoidance operations according to the generated obstacle avoidance trajectory and determines whether the recovery conditions are met. If the recovery conditions are met, a route recovery command is output. The route recovery command is used to control the UAV to enter the main route return mode. The main route is the expected flight path preset by the UAV cluster before performing the obstacle avoidance operation.

2. The UAV swarm formation obstacle avoidance control method based on ranging and sector scanning according to claim 1, characterized in that, The obstacle avoidance direction is determined based on the obstacle ranging information, the acquired current position information of the UAV, and the sector scanning method, specifically including: Based on the acquired current location information of the drone, a circular sensing region is constructed, and the circular sensing region is divided into several identical sectors; the center of the circular sensing region is the current location of the drone. Each sector is scanned and evaluated to determine the center direction angle of each sector and the center direction angle of the sector where the target point's heading is located; Based on the center direction angle of each sector, the center direction angle of the sector where the target point's heading is located, and the obstacle ranging information, the comprehensive cost factor of each sector is obtained; Based on the comprehensive cost factor of each sector, the minimum comprehensive cost factor is selected and compared with a first preset obstacle avoidance threshold. If the minimum comprehensive cost factor is less than the first preset obstacle avoidance threshold, the obstacle avoidance direction is determined based on the obtained current speed vector of the UAV and the comprehensive cost factor of each sector.

3. The UAV swarm formation obstacle avoidance control method based on ranging and sector scanning according to claim 2, characterized in that, Based on the center direction angle of each sector, the center direction angle of the sector where the target point's heading is located, and the obstacle ranging information, the comprehensive cost factor for each sector is obtained, specifically including: For each sector, perform the following operations: Based on the center direction angle of the current evaluation sector and the center direction angle of the sector where the target point's heading is located, the heading correction angle of the current evaluation sector is calculated; the current evaluation sector can be any sector; the formula for calculating the heading correction angle of the current evaluation sector is: ； in, The heading correction angle for the current evaluation sector. The center direction angle of the current evaluation sector. The direction angle is the center direction angle of the sector where the target point's heading is located. To obtain and The minimum value between; Based on the obstacle ranging information and the heading correction angle of the current evaluation sector, the comprehensive cost factor of the current evaluation sector is calculated; the formula for calculating the comprehensive cost factor of the current evaluation sector is as follows: ； in, This serves as the overall cost factor for the current evaluation sector. To determine the distance between the current assessment sector and the nearest obstacle, This is the weight decay coefficient. This is the angle correction factor.

4. The UAV swarm formation obstacle avoidance control method based on ranging and sector scanning according to claim 2, characterized in that, The process of determining the obstacle avoidance direction based on the acquired current velocity vector of the UAV and the comprehensive cost factor for each sector specifically includes: Using the current velocity vector of the UAV as the reference direction, the sector is divided into a left sector group and a right sector group. The sum of the comprehensive cost factors of each sector in the left sector group and the right sector group is calculated respectively to obtain the total comprehensive cost factor of the left sector group and the total comprehensive cost factor of the right sector group. The total comprehensive cost factor of the left sector group and the total comprehensive cost factor of the right sector group are compared. If the total comprehensive cost factor of the left sector group is greater than the total comprehensive cost factor of the right sector group, then the vertical direction to the left of the reference direction is determined as the obstacle avoidance direction; otherwise, the vertical direction to the right of the reference direction is determined as the obstacle avoidance direction.

5. The UAV swarm formation obstacle avoidance control method based on ranging and sector scanning according to claim 1, characterized in that, An obstacle avoidance trajectory is generated using the obstacle avoidance direction, the acquired current position information of the UAV, the position information of the target point, and the obstacle ranging information, specifically including: Based on the current location information of the UAV and the location information of the target point, the target attraction unit velocity vector is obtained; the formula for calculating the target attraction unit velocity vector is: ； in, The target's unit velocity vector is the force of attraction. For the target point location information, This is the current location information of the drone. For mold length operation, It is the minimum constant; Based on the current position vector of each obstacle and the current position information of the UAV, the total obstacle avoidance speed vector is calculated; A dynamic weighting function is used to fuse the target attraction unit velocity vector and the total obstacle avoidance velocity vector to obtain the desired velocity vector; the formula for calculating the desired velocity vector is: ； in, For the desired velocity vector, The fusion coefficient is... This represents the total obstacle avoidance velocity vector; Based on the desired velocity vector and the preset maximum horizontal flight speed of the UAV, the final horizontal control velocity vector is obtained; An obstacle avoidance trajectory is generated based on the obstacle avoidance direction and the final horizontal control velocity vector.

6. The UAV swarm formation obstacle avoidance control method based on ranging and sector scanning according to claim 5, characterized in that, The formula for calculating the final horizontal control velocity vector is as follows: ； in, For the final horizontal control velocity vector, The maximum horizontal flight speed of the drone. Let be the desired velocity vector.

7. The UAV swarm formation obstacle avoidance control method based on ranging and sector scanning according to claim 5, characterized in that, The total obstacle avoidance speed vector is calculated based on the current position vector of each obstacle and the current position information of the UAV, specifically including: For each obstacle, perform the first operation to obtain the repulsion velocity vector of each obstacle, and then sum the weighted repulsion velocity vectors of each obstacle to obtain the total obstacle avoidance velocity vector. The first operation is specifically as follows: The current position vector of the target obstacle is determined based on obstacle ranging information; the target obstacle can be any obstacle. Based on the current position information of the UAV and the current position vector of the target obstacle, the vector pointing from the target obstacle to the UAV and its magnitude are obtained; the formula for the vector pointing from the target obstacle to the UAV is: ,in, Let the target obstacle's current position vector be... This provides the current location information of the drone; the formula for the magnitude of the vector pointing from the target obstacle to the drone is: ; The repulsion velocity vector of the target obstacle is calculated based on the vector pointing from the target obstacle to the UAV and its magnitude. The formula for calculating the repulsion velocity vector of the target obstacle is: ； in, Let V be the repulsion velocity vector of the target obstacle. This is the vector pointing from the target obstacle to the drone. Let be the magnitude of the vector pointing from the target obstacle to the UAV.

8. The UAV swarm formation obstacle avoidance control method based on ranging and sector scanning according to claim 5, characterized in that, The obstacle avoidance trajectory is generated based on the obstacle avoidance direction and the final horizontal control velocity vector, specifically including: Based on the obstacle avoidance direction and the final horizontal control velocity vector, an initial obstacle avoidance trajectory is generated; The initial obstacle avoidance trajectory is smoothed using a Bézier curve to generate the final obstacle avoidance trajectory.

9. The UAV swarm formation obstacle avoidance control method based on ranging and sector scanning according to claim 1, characterized in that, Also includes: When the UAV enters the main flight path return mode, a second-order Bézier curve recovery strategy is adopted to generate the main flight path recovery trajectory. The second-order Bézier curve recovery strategy is as follows: ； in, This represents the real-time position vector of the UAV on the recovered trajectory of the main flight path. For proportional parameters, This refers to the position vector of the drone when it finishes obstacle avoidance maneuvers and begins preparing to resume its main flight path. For the target point location information, This is the control point position vector.

10. A drone swarm formation obstacle avoidance control system based on ranging and sector scanning, characterized in that, The drone swarm includes multiple drones; the drone swarm formation obstacle avoidance control system based on ranging and sector scanning includes: UWB module, RTK module and control module installed on each of the aforementioned UAVs; The UWB module is used to periodically collect obstacle ranging information; The RTK module is used to periodically collect the location information of the UAV; The control module is used to execute the UAV swarm formation obstacle avoidance control method based on ranging and sector scanning as described in any one of claims 1-9.

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