Unmanned aerial vehicle full-coverage path planning method, system and device and storage medium

Abstract

The invention discloses an unmanned aerial vehicle full-coverage path planning method, system and device and a storage medium, and relates to the field of unmanned aerial vehicles. The method comprises the following steps: acquiring preset map information of a first target area, and determining a second target area for delivering materials according to the preset map information; determining a relay unmanned aerial vehicle set according to the data information, a preset starting point, a second target area, preset map information, a first communication distance of the first unmanned aerial vehicle and a second communication distance of the relay unmanned aerial vehicle, and determining a second initial path of the second unmanned aerial vehicle according to the first initial path; controlling the first unmanned aerial vehicle and the second unmanned aerial vehicle to fly according to a preset initial path, and adjusting the preset initial path according to the environmental data collected by the first unmanned aerial vehicle; and when the first unmanned aerial vehicle arrives at a preset terminal point, determining flight paths of other unmanned aerial vehicles according to the actual load weights and types of the other unmanned aerial vehicles in the transportation unmanned aerial vehicle group and the environment data. By implementing the technical scheme provided by the invention, the task execution efficiency of the unmanned aerial vehicle group is improved.

Description

Technical Field

[0001] The present application relates to the technical field of drones, and in particular to a method, system, device and storage medium for full coverage path planning of drones. Background Art

[0002] Drone technology has developed rapidly in recent years and has shown great potential in various application scenarios, especially in the fields of logistics distribution, emergency rescue, etc. With the advancement of drone swarm technology, there is a growing demand for collaborative operations of multiple drones, which helps improve operational efficiency and reliability and reduce labor costs. In order to ensure that drone swarms can collaborate efficiently to complete tasks, path planning has become one of the key technologies.

[0003] At present, most drone route planning in disaster relief still relies on manual presets or simple automatic path planning algorithms, which cannot adapt to the actual changing conditions in the disaster area in real time, especially in the "three cutoffs (road cutoff, network cutoff, power cutoff)" environment, making it difficult to effectively deploy and utilize drone resources.

[0004] Therefore, a method is needed to plan the path of a drone swarm based on actual conditions. Summary of the invention

[0005] The present application provides a method, system, device and storage medium for full-coverage path planning of drones, which realizes the effective integration and optimization of drone resources by comprehensively considering the data information of each drone in the drone swarm and the map information of the target area; the collaborative operation of the transport drone swarm and the relay drone swarm further improves the overall transportation efficiency.

[0006] In a first aspect of the present application, a method for full coverage path planning of a UAV is provided, which is applied to a UAV path planning platform, and the method comprises: Acquire data information of all drones in the drone group, and acquire preset map information of the first target area, and determine the second target area for delivering materials according to the preset map information, wherein the data information includes a maximum load and a maximum flight time, and the preset map information is map information of the first target area before a preset time, and the drone group includes a transport drone group and a relay drone group; Determine a flight radius of a first drone according to the data information, determine a first preset end point according to a preset starting point, the flight radius and the second target area, and determine a first initial path of the first drone according to the preset starting point, the first preset end point and preset map information, wherein the first drone is any one of the transport drones; Determine a relay drone set according to the first initial path, the first communication distance of the first drone, and the second communication distance of the relay drone, and determine a second initial path of a second drone according to the first initial path, where the second drone is any one of the relay drones; sending a takeoff command to the first UAV so that the first UAV flies along the first initial path, sending a takeoff command to the second UAV so that the second UAV flies along the second initial path, receiving environmental data collected by the first UAV, and adjusting the first initial path and the second initial path according to the environmental data, wherein the environmental data includes the location, shape, size, wind speed, wind direction, air pressure, and precipitation intensity of obstacles; When the first drone reaches the preset destination, the flight paths of the other drones in the transport drone group are determined according to the weight and type of actual loads of the other drones and the environmental data. The other drones are drones in the transport drone group other than the first drone.

[0007] Optionally, determining the relay drone set according to the first initial path, the first communication distance of the first drone, and the second communication distance of the relay drone includes: Calculate the communication relay capability using a preset path loss model and a preset signal interference model according to the first initial path and the second communication distance, and determine a first number of relay drones according to the communication relay capability and the first communication distance; The second number of redundant relay drones is determined based on the first number, and the number of drones in the relay drone set is determined based on the first number and the second number.

[0008] Optionally, determining a second initial path of the second UAV according to the first initial path includes: Determine a second preset destination of each relay drone in the set of relay drones according to the communication relay capability and the first initial path; The second initial path is determined according to the second preset end point and the preset starting point.

[0009] Optionally, the method further includes: Determining a preset calibration point according to the second preset end point, wherein the preset calibration point is located on the first initial path and the distance between the preset calibration point and the second preset end point is less than the first communication distance; Determining a second take-off time of the second UAV according to the first take-off time of the first UAV and the first communication distance; A first moment when the first UAV moves from the preset starting point to the preset calibration point is determined according to the first initial path, a second moment when the second UAV arrives at the preset calibration point is determined according to the second take-off moment and the first moment, and a flight speed of the second UAV is determined according to the second take-off moment and the second moment.

[0010] Optionally, the determining the flight path of the other drones in the transport drone group according to the weight and type of actual loads of the other drones and the environmental data includes: Calculate the flight performance parameters of each drone according to the actual load weight and type of the other drones, the flight performance parameters including maximum flight speed, maximum climb rate, minimum turning radius, and endurance time; generating an initial flight path for other drones in the transport drone group based on the environmental data and the flight performance parameters; The initial flight path is adjusted according to the second preset end point and the mutual safety distance to determine the flight path of the other UAVs.

[0011] Optionally, the method further includes: Detecting the real-time communication quality between the first UAV and each relay UAV in the relay UAV set, and selecting an optimal relay UAV from the relay UAV set as a temporary communication relay node according to the real-time communication quality and the current position and flight status of each relay UAV; Sending a relay instruction to the optimal relay UAV to allow the optimal relay UAV to adjust its flight attitude and connect the communication link between the first UAV and the central cloud platform; The flight speed, altitude or flight path of the first UAV is adjusted according to the remaining battery power of the first UAV and the estimated duration of the signal blocking.

[0012] Optionally, the receiving environmental data collected by the first drone and adjusting the first initial path and the second initial path according to the environmental data includes: Extracting image data from the environment data, calculating difference values ​​between depth values ​​of adjacent pixels in the image data, and constructing a mapping of relative positions and depth relationships between points in the environment based on the difference values; Determining the location, shape, size, and terrain changes of obstacles through the mapping; The first initial path is adjusted according to the current position of the first UAV and the first preset end point, as well as obstacles and terrain features in the environment, and the second initial path is adjusted according to the adjusted first initial path.

[0013] In a second aspect of the present application, a system for drone group path planning is provided, including a collection module, an initial module, a relay module, an adjustment module, and an execution module, wherein: A collection module is configured to obtain data information of all drones in the drone group, and obtain preset map information of the first target area, and determine the second target area for delivering materials according to the preset map information, wherein the data information includes a maximum load and a maximum flight time, and the preset map information is map information of the first target area before a preset time, and the drone group includes a transport drone group and a relay drone group; an initial module, configured to determine a flight radius of a first drone according to the data information, determine a first preset end point according to a preset starting point, the flight radius and the second target area, and determine a first initial path of the first drone according to the preset starting point, the first preset end point and preset map information, wherein the first drone is any one of the transport drone group; a relay module, configured to determine a relay drone set according to the first initial path, a first communication distance of the first drone, and a second communication distance of the relay drone, and determine a second initial path of a second drone according to the first initial path, wherein the second drone is any one of the relay drones; an adjustment module, configured to send a take-off instruction to the first UAV so that the first UAV flies according to the first initial path, send a take-off instruction to the second UAV so that the second UAV flies according to the second initial path, receive environmental data collected by the first UAV, and adjust the first initial path and the second initial path according to the environmental data, wherein the environmental data includes the location, shape, size, wind speed, wind direction, air pressure, and precipitation intensity of obstacles; The execution module is configured to determine the flight paths of the other drones in the transport drone group according to the weight and type of actual loads of the other drones in the transport drone group and the environmental data when the first drone reaches the preset destination. The other drones are drones in the transport drone group other than the first drone.

[0014] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods described above.

[0015] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any of the methods described above is executed.

[0016] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By comprehensively considering the data information of each drone in the drone group (such as maximum load, maximum flight time) and the map information of the target area, the drone resources are effectively integrated and optimized; the coordinated operation of the transport drone group and the relay drone group further improves the overall transportation efficiency; during the flight, the flight path is dynamically adjusted according to the real-time collected environmental data (such as the location, shape, size of obstacles, wind speed, wind direction, air pressure, and precipitation intensity), ensuring that the drone can complete the task safely and efficiently; this dynamic adjustment capability enables the drone group to cope with complex and changing environmental conditions, and improves the flexibility and adaptability of the overall operation; 2. By presetting the map information and the flight performance parameters of the drone group, the second target area for the delivery of materials is accurately determined, ensuring the accurate delivery of materials; this is of great significance for tasks that require precise strikes or delivery; the method takes into account the coordination of the drone group, and through reasonable task allocation and path planning, the waiting time between drones is reduced, and the overall operation efficiency is improved; when a drone completes the task, a new flight path can be quickly planned for it based on the actual load and environmental data of other drones, ensuring the continuous operation capability of the drone group; 3. When planning the flight path, the safe distance between drones and the impact of obstacles are fully considered to avoid collisions between drones and with obstacles; this is of great significance to ensure the safe operation of the drone swarm. The method introduces a mechanism for real-time environmental data collection and path adjustment, so that the drone swarm can respond to emergencies (such as weather changes, obstacle movement, etc.) and ensure the smooth completion of the mission; 4. By dynamically adjusting the flight path and considering environmental factors, the UAV can complete the task in the best way, reducing unnecessary flight time and energy consumption; this is of great significance for reducing operating costs and improving economic benefits; the method improves the utilization rate and operation efficiency of the UAV through reasonable task allocation and path planning; this means that under the same mission requirements, the number of UAVs can be reduced or the operating frequency of UAVs can be reduced, thereby further reducing operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of the full coverage path planning method of the UAV disclosed in the embodiment of the present application; Figure 2 It is a module schematic diagram of the drone full coverage path planning system disclosed in the embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device disclosed in an embodiment of the present application.

[0018] Explanation of the reference numerals: 201, acquisition module; 202, initial module; 203, relay module; 204, adjustment module; 205, execution module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0019] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0020] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0021] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0022] This embodiment discloses a method for planning a full coverage path for a UAV, which is applied to a UAV path planning platform. Figure 1 is a flow chart of the full coverage path planning method for UAV disclosed in the embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps: S110, obtaining data information of all drones in the drone group, and obtaining preset map information of the first target area, and determining a second target area for delivering materials according to the preset map information, wherein the data information includes a maximum load and a maximum flight time, and the preset map information is map information of the first target area before a preset time, and the drone group includes a transport drone group and a relay drone group; Obtain data information of all drones in the drone swarm. This data information is crucial for subsequent path planning and task allocation. Specifically, the data information that needs to be collected includes maximum load and maximum flight time. Maximum load: the maximum weight of materials that each drone can carry. This parameter determines the upper limit of the drone's ability to perform transportation tasks. Maximum flight time: the maximum time each drone can fly continuously when fully charged or fully fueled. This parameter is crucial for planning the flight path of the drone and determining the time to execute the task. After obtaining the data information of the drone swarm, the system also needs to obtain the preset map information of the first target area. This map information describes the geographical features of the target area before the preset time (such as the time when the disaster occurred), including terrain, landforms, buildings, roads, etc. This information is very important for determining the target area for material delivery and planning the flight path of the drone. The accuracy of the map determines the number and accuracy of geographical features that the drone can identify and use during flight. High-precision maps can provide more detailed information, which helps drones better plan flight paths and avoid obstacles. Because the geographical environment may change (such as the construction or demolition of buildings), map information needs to be updated regularly. The system needs to obtain the latest version of map information to ensure that the drone can accurately identify and utilize geographic features during flight. After obtaining the data information of the drone group and the map information of the target area, the system can determine the second target area for material delivery based on this information. This process usually involves the following steps: the specific requirements of the mission need to be analyzed, including the type and quantity of materials to be delivered and the precise location of delivery; the ability of the drone group to perform the mission needs to be evaluated based on the data information of the drone (such as maximum load and maximum flight time), which helps to ensure that the mission can be completed within the specified time and does not exceed the performance limitations of the drone; based on the evaluation results of the mission requirements and drone capabilities, the system can determine one or more suitable material delivery areas, which should be within the flight range of the drone and can meet the specific requirements of the mission; when determining the delivery area, the system also needs to consider the impact of environmental factors on drone flight and material delivery. For example, weather conditions such as wind speed, wind direction and precipitation may affect the flight performance of the drone and the accuracy of material delivery. Therefore, the system needs to take these factors into account in the planning stage and take corresponding measures to reduce their impact on mission execution.

[0023] S120, determining a flight radius of a first drone according to the data information, determining a first preset end point according to a preset starting point, the flight radius, and the second target area, and determining a first initial path of the first drone according to the preset starting point, the first preset end point, and preset map information, wherein the first drone is any one of the transport drones; The determination of the flight radius is mainly based on the data information of the first UAV, especially its maximum flight distance or maximum flight time. These data are usually provided by the manufacturer of the UAV or obtained through actual testing. When determining the flight radius, a certain safety margin must also be considered to cope with unexpected situations that may occur during the flight (such as wind speed changes, obstacles, etc.). This means that the actual planned flight path length should be less than half of the maximum flight distance of the UAV to ensure that the UAV still has enough energy or fuel to return to the base or perform other operations after completing the mission. The preset starting point is usually the take-off location of the UAV, which may be a UAV base, a temporary helipad or other designated take-off point. After determining the flight radius, the system needs to select a suitable preset end point based on the distance between the second target area (i.e., the target area for material delivery) and the preset starting point. This preset end point should be within the flight radius and as close to the second target area as possible to reduce the flight time and energy consumption of the UAV. When selecting the preset end point, the influence of terrain and obstacles must also be considered. For example, the preset end point should be avoided near mountains, lakes or other areas where landing is not possible. When the preset starting point and preset end point are determined, the system can use the preset map information to plan the initial flight path of the UAV. The preset map information usually includes terrain data, obstacle locations, road networks, etc., which helps the system plan a safe and efficient flight path for the drone. When planning the initial flight path, the system may use various optimization algorithms (such as Dijkstra algorithm, genetic algorithm, etc.) to find the optimal or suboptimal path. These algorithms take into account the flight performance of the drone (such as speed, acceleration, maneuverability, etc.), environmental factors (such as wind speed, wind direction, precipitation, etc.), and the impact of obstacles to ensure that the drone can reach the preset destination safely and efficiently. It should be noted that the initial flight path is only a reference during the flight of the drone. During the actual flight, the system may dynamically adjust the flight path based on the real-time collected environmental data (such as wind speed changes, obstacle movement, etc.).

[0024] S130, determining a relay drone set according to the first initial path, the first communication distance of the first drone, and the second communication distance of the relay drone, and determining a second initial path of a second drone according to the first initial path, where the second drone is any drone in the relay drone set; Communication distance is a key factor in the path planning of drone swarms. Both the first drone and the relay drone need to be within each other's communication range to ensure real-time data transmission and coordinated execution of tasks. The first communication distance of the first drone and the second communication distance of the relay drone are important bases for determining the set of relay drones. These communication distances are usually determined by the performance of the drone's communication equipment and antenna design. After determining the first initial path of the first drone, the system needs to analyze the location where the relay drone needs to be set on the first initial path to ensure communication between the control station and the first drone. The system will select appropriate relay drone locations around the first initial path of the first drone based on the second communication distance of the relay drone to form a set of relay drones. The relay drones in this set will be responsible for transmitting data between the first drone and the control station or other drones. When determining the set of relay drones, the system may consider multiple factors to optimize the layout of the relay drones. These factors include the number, location, communication coverage of the relay drones, and their relative positions. The goal of the optimization is to ensure that the first drone can maintain communication with the command center or other drones throughout the flight, while minimizing the number of relay drones used and the flight time to reduce the overall operating cost. When the set of relay drones is determined, the system can plan a second initial path for each relay drone (here, the second drone is taken as an example). The second initial path will ensure that the second drone can reach the designated relay location and maintain communication with the first drone and the command center or other drones there. When planning the second initial path of the second drone, the system needs to fully consider its flight performance (such as speed, acceleration, maneuverability, etc.) and mission requirements (such as communication coverage, data transmission rate, etc.). These factors will affect the selection of parameters such as the flight altitude, speed, and heading of the second drone, thereby ensuring that it can efficiently complete the relay mission. It should be noted that the second initial path of the second drone is not isolated. It needs to be planned in coordination with the first initial path of the first drone and the flight paths of other drones. This means that the system needs to comprehensively consider the flight performance, mission requirements, and environmental factors (such as wind speed, wind direction, precipitation, etc.) of all drones to ensure that the entire drone group can collaboratively and efficiently complete the mission.

[0025] Optionally, determining the relay drone set according to the first initial path, the first communication distance of the first drone, and the second communication distance of the relay drone includes: Calculate the communication relay capability using a preset path loss model and a preset signal interference model according to the first initial path and the second communication distance, and determine a first number of relay drones according to the communication relay capability and the first communication distance; The second number of redundant relay drones is determined based on the first number, and the number of drones in the relay drone set is determined based on the first number and the second number.

[0026] The preset path loss model is used to describe the signal strength attenuation caused by distance, obstacles, atmosphere and other factors during the transmission process. In UAV communication, path loss is an important consideration because it directly affects the communication quality between UAVs. The system uses the preset path loss model to calculate the signal loss during the transmission process based on the initial flight path of the first UAV and the second communication distance of the relay UAV. This helps the system evaluate the communication relay capability of the relay UAV at different locations. The preset signal interference model is used to describe the interference that the signal may receive from other signal sources during the transmission process. In UAV communication, interference may come from other UAVs, ground equipment or noise in the natural environment. The system will analyze the interference that the first UAV and the relay UAV may receive during the communication process in combination with the preset signal interference model, and adjust the evaluation results of the communication relay capability accordingly. After considering path loss and signal interference, the system will conduct a comprehensive evaluation of the communication relay capability of the relay UAV. This includes evaluating key indicators such as signal strength, data transmission rate, bit error rate, etc. of the relay UAV at different locations. After comprehensively evaluating the communication relay capabilities of the relay drones, the system will determine the required number of relay drones (first number) based on the first communication distance of the first drone and the communication relay capabilities of the relay drones. This number should ensure that the first drone can maintain communication with the command center or other drones throughout the flight. In order to improve the reliability and stability of the communication system, the system usually considers setting up a certain number of redundant relay drones (second number). These redundant drones can take over the tasks of the main relay drone when it fails or fails to work properly to ensure the continuity of communication. The number of redundant drones is usually determined based on factors such as the reliability requirements of the system, the availability of relay drones, and maintenance costs. Finally, the system will combine the first number and the second number to determine the total number of drones in the relay drone set. This total number should meet the communication needs of the first drone and take into account the needs of redundancy and backup. After determining the number of drones in the relay drone set, the system will specifically select the location and flight path of the relay drone based on the first initial path of the first drone, the communication relay capability of the relay drone, and environmental factors such as terrain and obstacles. These relay drones will form a relay network to ensure that the first drone can maintain communication with the control center or other drones throughout the flight.

[0027] By using a preset path loss model and a preset signal interference model, the system can more accurately calculate the communication relay capability of the relay UAV on the first initial path. The path loss model takes into account the fading of electromagnetic waves propagating in a wireless environment, including large-scale fading (such as path loss and shadow fading) and small-scale fading (such as multipath effect). These models help the system predict the signal strength and quality of the UAV at different locations. The signal interference model takes into account the potential interference of other wireless devices or environmental factors to the UAV communication, thereby further improving the accuracy of the communication relay capability calculation. Based on the calculated communication relay capability and the first communication distance of the first UAV, the system can determine the first number of relay UAVs required. This number not only ensures that the first UAV can maintain communication with the command center or other UAVs throughout the flight, but also avoids unnecessary use of relay UAVs, thereby reducing the overall operating cost. By determining the second number of redundant relay UAVs, the system can further improve the reliability and fault tolerance of the entire UAV group. The redundant relay UAV can take over its work in a timely manner when the main relay UAV fails or cannot perform the task, ensuring the continuity of communication and the smooth completion of the task. At the same time, the presence of redundant relay drones also increases the system's ability to respond to emergencies and improves the robustness of the overall system. After determining the number of drones in the relay drone set, the system can further collaboratively plan the path of the drone swarm. This includes planning appropriate flight paths and altitudes for relay drones to ensure that they can complete their missions efficiently and maintain communication with other drones. Collaborative planning also helps avoid collisions and interference between drones, improving the operational efficiency and safety of the entire drone swarm.

[0028] Optionally, determining a second initial path of the second UAV according to the first initial path includes: Determine a second preset destination of each relay drone in the set of relay drones according to the communication relay capability and the first initial path; The second initial path is determined according to the second preset end point and the preset starting point.

[0029] When planning the flight path of a relay drone, the first thing to consider is its communication relay capability. This includes factors such as the coverage of the relay drone on the first initial path, signal strength, and communication quality with other drones. Based on the communication relay capability and the specific information of the first initial path, the system can calculate the appropriate location that the relay drone needs to reach to ensure that the first drone can maintain stable communication throughout the flight. Based on the above analysis, the system can determine the second preset end point of each relay drone in the relay drone set. These end points are usually located on a parallel line to the first initial path, and the communication range of the relay drone can cover a part of the first initial path. The selection of the second preset end point needs to comprehensively consider multiple factors, including the flight performance, mission requirements, environmental factors, etc. of the relay drone, to ensure that the relay drone can efficiently complete the mission and maintain communication with other drones. When the second preset end point of the relay drone is determined, the system can plan the second initial path of the relay drone based on these end points and the preset starting point. During the planning process, the system needs to fully consider the impact of the flight performance (such as speed, acceleration, maneuverability, etc.) of the relay drone and environmental factors (such as wind speed, wind direction, terrain, etc.) on the flight path. When planning the second initial path, the system may use various optimization algorithms to find the optimal or suboptimal path. These algorithms will consider multiple factors such as the flight performance of the relay drone, mission requirements, and environmental factors to ensure that the relay drone can reach the second preset destination safely and efficiently. At the same time, the system also needs to consider the collaborative relationship between the relay drone and the first drone and other drones to ensure that the entire drone group can complete the task collaboratively and efficiently. It should be noted that the second initial path is only a reference during the flight of the relay drone. During the actual flight, the system may dynamically adjust the flight path based on real-time collected environmental data (such as wind speed changes, obstacle movement, etc.).

[0030] By comprehensively considering the communication relay capability and the first initial path, the system can accurately determine the second preset endpoint for each relay drone in the relay drone set. These second preset endpoints not only take into account the communication coverage requirements, but also combine the flight performance and mission requirements of the drone, ensuring that the relay drone can provide stable communication relay services at the appropriate location. After determining the second preset endpoint of each relay drone, the system can further plan the second initial path of the second drone. These paths not only take into account the flight performance and mission requirements of the relay drone, but also avoid potential conflicts with other drones, thereby optimizing the path planning of the entire drone group. Through this collaborative planning, the drone group can complete the task more efficiently while reducing operating costs and safety risks. By accurately planning the location and path of the relay drone, the system can ensure that the first drone can maintain stable communication with the command center or other drones throughout the flight. This not only improves the communication efficiency, but also enhances the stability and reliability of the system. This communication guarantee is particularly important in complex or harsh environments. The process also takes into account the changes in the communication relay capability, allowing the system to flexibly adjust the number and location of the relay drones according to actual conditions. This flexibility helps to cope with different mission requirements and environmental changes, and improves the scalability and adaptability of the system.

[0031] Optionally, the method further includes: Determining a preset calibration point according to the second preset end point, wherein the preset calibration point is located on the first initial path and the distance between the preset calibration point and the second preset end point is less than the first communication distance; Determining a second take-off time of the second UAV according to the first take-off time of the first UAV and the first communication distance; A first moment when the first UAV moves from the preset starting point to the preset calibration point is determined according to the first initial path, a second moment when the second UAV arrives at the preset calibration point is determined according to the second take-off moment and the first moment, and a flight speed of the second UAV is determined according to the second take-off moment and the second moment.

[0032] According to the second preset end point (i.e., the target location that the relay drone needs to reach), a preset calibration point is determined on the first initial path (i.e., the flight path of the first drone). This preset calibration point needs to meet a condition: its distance from the second preset end point is less than the first communication distance of the first drone. The purpose of determining the preset calibration point is to ensure that when the subsequent transport drone flies to this point, it can establish communication with the relay drone at the preset calibration point. According to the first take-off time and the first communication distance of the first drone, the second take-off time of the second drone is determined. This second take-off time needs to ensure that before the first drone flies to the preset calibration point, the second drone has taken off and can establish communication with the first drone at an appropriate time. According to the first initial path, the first moment of the first drone from the preset starting point to the preset calibration point is determined. Combined with the second take-off time and the first moment of the second drone, the second moment of the second drone arriving at the preset calibration point is determined. According to the second take-off time and the second moment, and the distance that the second drone needs to fly (i.e., the distance from the second preset end point to the preset calibration point), the flight speed of the second drone is determined. This flight speed needs to ensure that the second drone can arrive at the preset calibration point at an appropriate time and establish a stable communication with the first drone.

[0033] By determining the take-off time of the second drone based on the take-off time of the first drone and the first communication distance, the system can ensure that the relay drone has taken off and is on standby before the relay demand arises. This precise time control helps reduce the risk of communication interruption and improves the coordination efficiency of the entire drone group. By determining a preset calibration point (located on the first initial path and the distance from the second preset end point is less than the first communication distance), the system can calculate the time when the first drone arrives at the point. Combining the take-off time of the second drone and the time when the first drone arrives at the preset calibration point, the system can further determine the time when the second drone arrives at the same point. Through the difference between these two times, the system can calculate the required flight speed of the second drone to ensure that it can arrive at the preset calibration point on time and provide relay services for the first drone. This speed optimization not only helps to ensure the continuity of communication, but also reduces the flight time and energy consumption of the relay drone. By accurately controlling the take-off time and flight speed of the relay drone, the system can ensure that the relay service is provided in a timely manner when needed, thereby improving the reliability of communication relay. At the same time, by optimizing the flight speed, the system can also reduce the flight time and energy consumption of the relay drone and improve the operating efficiency of the entire drone group. This method takes into account a variety of factors (such as the take-off time of the first UAV, the first communication distance, the location of the preset calibration point, etc.), so that the system can flexibly adjust the take-off time and flight speed of the relay UAV according to actual conditions. This flexibility helps to cope with different mission requirements and environmental changes, and improves the adaptability and robustness of the system.

[0034] S140, sending a take-off instruction to the first UAV so that the first UAV flies along the first initial path, sending a take-off instruction to the second UAV so that the second UAV flies along the second initial path, receiving environmental data collected by the first UAV, and adjusting the first initial path and the second initial path according to the environmental data, wherein the environmental data includes the location, shape, size, wind speed, wind direction, air pressure, and precipitation intensity of obstacles; When the drone swarm is ready to perform a mission, the ground control station or command center will send a takeoff command to the first drone. The command includes the takeoff time, takeoff location, and instructions for flying along the first initial path of the first drone. At the same time, the ground control station will also send a takeoff command to the second drone (i.e., the relay drone), which includes the takeoff time, takeoff location, and instructions for flying along the second initial path of the second drone. After receiving the takeoff command, the first drone and the second drone will fly according to the predetermined initial paths (the first initial path and the second initial path) respectively. During the flight, the drone will obtain its own flight parameters such as position, speed, and altitude in real time, and send these data to the ground control station or command center through a wireless communication link. During the flight, the first drone will use its onboard sensors (such as cameras, radars, meteorological sensors, etc.) to collect data about the surrounding environment. These environmental data include the location, shape, and size of obstacles, as well as meteorological information such as wind speed, wind direction, air pressure, and precipitation intensity. These data are crucial for the path planning, obstacle avoidance strategy, and flight safety of the drone swarm. After receiving the environmental data collected by the first drone, the ground control station or command center will analyze and process these data. If obstacles or other potential risks are found on the initial path, the ground control station will promptly adjust the first initial path and the second initial path to ensure the safe flight of the drone group and the smooth completion of the mission. Path adjustment may include changing the flight altitude, bypassing obstacles, adjusting the flight speed, and other measures. During the path adjustment process, the ground control station will update the flight path and related information of the drone in real time, and send this updated information to the first drone and the second drone through a wireless communication link. At the same time, the ground control station also needs to ensure the coordinated flight between the first drone and the second drone to avoid collision and interference between them.

[0035] Optionally, the method further includes: Detecting the real-time communication quality between the first UAV and each relay UAV in the relay UAV set, and selecting an optimal relay UAV from the relay UAV set as a temporary communication relay node according to the real-time communication quality and the current position and flight status of each relay UAV; Sending a relay instruction to the optimal relay UAV to allow the optimal relay UAV to adjust its flight attitude and connect the communication link between the first UAV and the central cloud platform; The flight speed, altitude or flight path of the first UAV is adjusted according to the remaining battery power of the first UAV and the estimated duration of the signal blocking.

[0036] During the process of the drone group performing the task, the system will continuously detect the real-time communication quality between the first drone and each relay drone in the relay drone set. The real-time communication quality can be measured by indicators such as signal strength, bit error rate, and packet loss rate. According to the real-time communication quality and the current position and flight status of each relay drone, the system will select an optimal relay drone from the relay drone set as a temporary communication relay node. The selection of the optimal relay drone may be based on a comprehensive consideration of multiple factors, such as the best communication quality, the most appropriate position, and the most stable flight status. When the optimal relay drone is determined, the system will send a relay instruction to it, instructing it to adjust its flight attitude in order to better establish or maintain a communication link with the first drone and the central cloud platform. The relay instruction may include specific actions such as adjusting the flight altitude, speed, and direction. Continuously monitor the remaining power of the first drone and the expected duration of signal obstruction. Signal obstruction may be caused by obstacles, weather conditions, etc., and the expected duration can be estimated by historical data, model prediction, etc. According to the remaining power of the first drone and the expected duration of signal obstruction, the system will dynamically adjust the flight speed, altitude, or flight path of the first drone. For example, if the remaining battery power is insufficient or the signal obstruction is expected to last for a long time, the system may instruct the first drone to reduce the flight speed, increase the flight altitude, or change the flight path to save power or find better communication conditions.

[0037] By real-time detection of the communication quality between the first UAV and each relay UAV in the relay UAV set, the system can timely discover potential problems in the communication link, such as signal attenuation, interference, etc. According to the real-time communication quality and the current position and flight status of each relay UAV, the system can select the best relay UAV from the relay UAV set as a temporary communication relay node, thereby ensuring that the communication link between the first UAV and the control console is always stable and reliable. This flexible relay selection strategy helps to improve the communication efficiency and task execution success rate of the UAV group. After selecting the best relay UAV, the system sends a relay instruction to it, requiring it to adjust its flight attitude to optimize the communication link. This may include adjusting parameters such as flight altitude, direction or speed to ensure that the relay UAV can provide the best communication relay service. At the same time, the system also needs to manage the communication link between the first UAV and the best relay UAV to ensure real-time transmission and reception of data. According to the remaining power of the first UAV and the expected duration of signal obstruction, the system can dynamically adjust its flight speed, altitude or path. This adjustment is intended to balance the relationship between flight energy consumption, communication quality and mission requirements, and ensure that the first UAV can complete the mission with the best flight parameters while ensuring communication quality. Taking into account a variety of factors (such as communication quality, the location and flight status of the relay drone, the remaining battery power of the first drone, and signal shielding, etc.), the system can flexibly adjust flight parameters and relay strategies according to actual conditions. This adaptability and robustness helps to cope with different mission requirements and environmental changes, and improve the coordination efficiency of drone groups and the success rate of mission execution.

[0038] Optionally, the receiving environmental data collected by the first drone and adjusting the first initial path and the second initial path according to the environmental data includes: Extracting image data from the environment data, calculating difference values ​​between depth values ​​of adjacent pixels in the image data, and constructing a mapping of relative positions and depth relationships between points in the environment based on the difference values; Determining the location, shape, size, and terrain changes of obstacles through the mapping; The first initial path is adjusted according to the current position of the first UAV and the first preset end point, as well as obstacles and terrain features in the environment, and the second initial path is adjusted according to the adjusted first initial path.

[0039] During the flight, the first UAV will use its onboard camera or other image acquisition devices to collect image data of the surrounding environment in real time. These image data are transmitted to the ground control station or the management system of the UAV group. The system first extracts key information from the image data, especially the depth values ​​of adjacent pixels. The depth value refers to the distance between each pixel in the image relative to a reference plane. Then, the system calculates the difference between the depth values ​​of adjacent pixels. These difference values ​​reflect the relative position and depth relationship between each point in the environment. Based on the calculated depth value difference values, the system constructs a mapping that describes the relative position and depth relationship between each point in the environment. This mapping can be regarded as a simplified representation of a three-dimensional space, which contains the position and shape information of all important features in the environment. By analyzing the mapping relationship, the system can determine the position, shape and size of obstacles in the environment. At the same time, the system can also identify terrain changes, such as slopes, gullies, etc. This information is crucial for the path planning of the UAV group because they directly affect the flight safety and mission execution efficiency of the UAV. After obtaining information about obstacles and terrain features, the system begins to adjust the flight path of the first UAV (i.e., the first initial path). The purpose of the adjustment is to avoid obstacles while minimizing the flight distance and time. The system will calculate an optimal path based on the current position of the first drone, the first preset destination, and the obstacles and terrain features in the environment. Since the flight path of the second drone (relay drone) needs to maintain a certain coordination relationship with the path of the first drone, the system will also adjust the second initial path based on the adjusted first initial path. The adjusted path should ensure that all drones can complete the task safely and efficiently while maintaining the stability of the communication link.

[0040] By calculating the difference in depth values ​​between adjacent pixels in the image data, a mapping of the relative position and depth relationship between points in the environment can be accurately constructed. This fine mapping provides an accurate environmental model for subsequent path planning. Based on the depth map, the position, shape and size of obstacles, as well as terrain changes, can be accurately determined. This information is crucial for drones to plan paths in complex environments. The flight path can be adjusted in real time based on the current position of the drone, the preset end point, and the obstacles and terrain features in the environment. This dynamic adjustment capability enables drones to better adapt to complex and changing environmental conditions. When obstacles or terrain changes are identified, the drone can adjust its flight attitude and speed in time to avoid collisions or optimize the flight route. This improves the safety of the drone and the efficiency of mission completion. Through precise environmental perception and path planning, the drone can choose an optimal flight path to avoid obstacles, reduce flight time and save energy consumption. During the flight, the drone continuously collects environmental data and updates the path planning. This real-time feedback mechanism enables the drone to respond to emergencies and ensure flight safety.

[0041] S150. When the first drone reaches the preset destination, the flight paths of the other drones in the transport drone group are determined according to the weight and type of actual loads of the other drones in the transport drone group and the environmental data. The other drones are drones in the transport drone group other than the first drone.

[0042] Based on the actual load conditions and environmental data of other drones, the ground control station or central system will conduct a comprehensive analysis. The goal is to determine the optimal flight path for each drone from its current location to the target location. The optimal flight path may involve multiple factors, such as shortest flight time, lowest energy consumption, and highest safety. When determining the flight path, it is also necessary to consider the coordinated flight between drones to avoid mutual interference and collision. When the flight path is determined, the ground control station or central system will send this information to the corresponding drone via a wireless communication link. During the flight, the drone may need to adjust the path according to the actual situation, such as avoiding sudden obstacles and responding to weather changes. These adjustments need to be made based on real-time environmental data and the current status of the drone.

[0043] Optionally, the determining the flight path of the other drones in the transport drone group according to the weight and type of actual loads of the other drones and the environmental data includes: Calculate the flight performance parameters of each drone according to the actual load weight and type of the other drones, the flight performance parameters including maximum flight speed, maximum climb rate, minimum turning radius, and endurance time; generating an initial flight path for other drones in the transport drone group based on the environmental data and the flight performance parameters; The initial flight path is adjusted according to the second preset end point and the mutual safety distance to determine the flight path of the other UAVs.

[0044] The actual load weight and type of each drone will directly affect its flight performance. For example, a heavier load may result in slower flight speed, lower climb rate, shorter endurance, etc. Flight performance parameters: Based on the actual load of the drone, its key flight performance parameters such as maximum flight speed, maximum climb rate, minimum turning radius and endurance can be calculated. These parameters are the basis for subsequent path planning. Environmental data: Environmental data includes wind speed, wind direction, air pressure, precipitation intensity, terrain characteristics, etc., all of which affect the flight path of the drone. For example, strong winds may require the drone to adjust its flight direction or altitude to maintain stability. In a drone swarm, in order to avoid interference and collision with each other, drones need to maintain a certain safe distance from each other. Based on the second preset end point and the safe distance between each other, the initial flight path needs to be adjusted. This includes considering factors such as the arrival time, flight speed, direction, etc. of the drones to ensure that they can reach the target location in the predetermined order and time while maintaining a safe distance. In practical applications, path planning is a complex process that requires comprehensive consideration of multiple factors. For example, it may be necessary to optimize the flight path to reduce energy consumption and improve flight efficiency; at the same time, it is also necessary to ensure that the drone can avoid obstacles and respond to emergencies during flight. When generating and adjusting the flight path, it may be necessary to use advanced algorithms and technologies, such as genetic algorithms and particle swarm optimization algorithms, to find the optimal or near-optimal flight path.

[0045] By considering the actual load weight and type of the drones, the flight performance parameters of each drone, such as maximum flight speed, maximum climb rate, minimum turning radius, and endurance, can be accurately calculated. These parameters are the basis for developing flight plans and ensuring flight safety and efficiency. Combining environmental data with flight performance parameters, an initial flight path can be generated for each drone. This path takes into account multiple factors such as the drone's flight capabilities, terrain characteristics, obstacle locations, and weather conditions to ensure flight safety and efficiency. The initial flight path can be adjusted based on the second preset end point (i.e., the target location of the relay drone) and the safe distance between each other. This adjustment ensures that the drones do not interfere or collide with each other during flight, while ensuring that each drone is in a communicative state. By accurately calculating flight performance parameters, generating initial flight paths, and dynamically adjusting flight paths, the flight efficiency and safety of drones can be significantly improved. Drones can fly along the predetermined route more accurately, avoiding unnecessary detours and delays, while reducing losses caused by flight accidents. This method takes into account each drone in the drone swarm and ensures coordinated flight between them. By optimizing flight paths and maintaining safe distances, drone swarms can complete tasks more efficiently, improving overall work efficiency and coordination capabilities. This method can generate suitable flight paths for drones based on different environmental data and flight performance parameters. This enables drones to fly safely and efficiently in complex and changing environments, improving the adaptability and flexibility of drones.

[0046] This embodiment also discloses a UAV full coverage path planning system. Figure 2 Schematic diagram of the module of the UAV full coverage path planning system disclosed in the embodiment of the present application. Figure 2 As shown, the system includes a collection module 201, an initial module 202, a relay module 203, an adjustment module 204 and an execution module 205, wherein: The acquisition module 201 is configured to obtain data information of all drones in the drone group, and obtain preset map information of the first target area, and determine the second target area for delivering materials according to the preset map information, wherein the data information includes a maximum load and a maximum flight time, and the preset map information is map information of the first target area before a preset time, and the drone group includes a transport drone group and a relay drone group; The initial module 202 is configured to determine a flight radius of a first drone according to the data information, determine a first preset end point according to a preset starting point, the flight radius and the second target area, and determine a first initial path of the first drone according to the preset starting point, the first preset end point and preset map information, wherein the first drone is any one of the transport drones; The relay module 203 is configured to determine a relay drone set according to the first initial path, the first communication distance of the first drone, and the second communication distance of the relay drone, and determine a second initial path of a second drone according to the first initial path, where the second drone is any one of the relay drones; The adjustment module 204 is configured to send a take-off instruction to the first UAV so that the first UAV flies according to the first initial path, send a take-off instruction to the second UAV so that the second UAV flies according to the second initial path, receive environmental data collected by the first UAV, and adjust the first initial path and the second initial path according to the environmental data, wherein the environmental data includes the location, shape, size, wind speed, wind direction, air pressure, and precipitation intensity of obstacles; The execution module 205 is configured to determine the flight paths of the other drones in the transport drone group according to the weight and type of actual loads of the other drones in the transport drone group and the environmental data when the first drone reaches the preset destination. The other drones are drones in the transport drone group other than the first drone.

[0047] Optionally, the relay module 203 is configured to: Calculate the communication relay capability using a preset path loss model and a preset signal interference model according to the first initial path and the second communication distance, and determine a first number of relay drones according to the communication relay capability and the first communication distance; The second number of redundant relay drones is determined based on the first number, and the number of drones in the relay drone set is determined based on the first number and the second number.

[0048] Optionally, the relay module 203 is configured to: Determine a second preset destination of each relay drone in the set of relay drones according to the communication relay capability and the first initial path; The second initial path is determined according to the second preset end point and the preset starting point.

[0049] Optionally, the system further comprises a take-off module, wherein the take-off module is configured to: Determining a preset calibration point according to the second preset end point, wherein the preset calibration point is located on the first initial path and the distance between the preset calibration point and the second preset end point is less than the first communication distance; Determining a second take-off time of the second UAV according to the first take-off time of the first UAV and the first communication distance; A first moment when the first UAV moves from the preset starting point to the preset calibration point is determined according to the first initial path, a second moment when the second UAV arrives at the preset calibration point is determined according to the second take-off moment and the first moment, and a flight speed of the second UAV is determined according to the second take-off moment and the second moment.

[0050] Optionally, the execution module 205 is configured to: Calculate the flight performance parameters of each drone according to the actual load weight and type of the other drones, the flight performance parameters including maximum flight speed, maximum climb rate, minimum turning radius, and endurance time; generating an initial flight path for other drones in the transport drone group based on the environmental data and the flight performance parameters; The initial flight path is adjusted according to the second preset end point and the mutual safety distance to determine the flight path of the other UAVs.

[0051] Optionally, the system further comprises a communication module, wherein the communication module is configured to: Detecting the real-time communication quality between the first UAV and each relay UAV in the relay UAV set, and selecting an optimal relay UAV from the relay UAV set as a temporary communication relay node according to the real-time communication quality and the current position and flight status of each relay UAV; Sending a relay instruction to the optimal relay UAV to allow the optimal relay UAV to adjust its flight attitude and connect the communication link between the first UAV and the central cloud platform; The flight speed, altitude or flight path of the first UAV is adjusted according to the remaining battery power of the first UAV and the estimated duration of the signal blocking.

[0052] Optionally, the adjustment module 204 is configured to: Extracting image data from the environment data, calculating difference values ​​between depth values ​​of adjacent pixels in the image data, and constructing a mapping of relative positions and depth relationships between points in the environment based on the difference values; Determining the location, shape, size, and terrain changes of obstacles through the mapping; The first initial path is adjusted according to the current position of the first UAV and the first preset end point, as well as obstacles and terrain features in the environment, and the second initial path is adjusted according to the adjusted first initial path.

[0053] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0054] This embodiment also discloses an electronic device, referring to Figure 3 The electronic device may include: at least one processor 301 , at least one communication bus 302 , a user interface 303 , a network interface 304 , and at least one memory 305 .

[0055] The communication bus 302 is used to realize the connection and communication between these components.

[0056] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0057] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0058] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0059] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also be optionally at least one storage device located away from the aforementioned processor 301. As Figure 3 As shown, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of a method for planning a full coverage path of a drone.

[0060] exist Figure 3 In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 301 can be used to call the application program storing the drone full coverage path planning method in the memory 305. When executed by one or more processors 301, the electronic device executes one or more methods in the above-mentioned embodiments.

[0061] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0062] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0063] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0064] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0065] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0066] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory 305 and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory 305 includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0067] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for full coverage path planning of an unmanned aerial vehicle, characterized in that: Applied to a UAV path planning platform, the method includes: Acquire data information of all drones in the drone group, and acquire preset map information of the first target area, and determine the second target area for delivering materials according to the preset map information, wherein the data information includes a maximum load and a maximum flight time, and the preset map information is map information of the first target area before a preset time, and the drone group includes a transport drone group and a relay drone group; Determine a flight radius of a first drone according to the data information, determine a first preset end point according to a preset starting point, the flight radius and the second target area, and determine a first initial path of the first drone according to the preset starting point, the first preset end point and preset map information, wherein the first drone is any one of the transport drone group; Determine a relay drone set according to the first initial path, the first communication distance of the first drone, and the second communication distance of the relay drone, and determine a second initial path of a second drone according to the first initial path, where the second drone is any one of the relay drones; sending a takeoff command to the first UAV so that the first UAV flies along the first initial path, sending a takeoff command to the second UAV so that the second UAV flies along the second initial path, receiving environmental data collected by the first UAV, and adjusting the first initial path and the second initial path according to the environmental data, wherein the environmental data includes the location, shape, size, wind speed, wind direction, air pressure, and precipitation intensity of obstacles; When the first drone reaches the preset destination, the flight paths of the other drones in the transport drone group are determined according to the weight and type of actual loads of the other drones and the environmental data. The other drones are drones in the transport drone group other than the first drone.

2. The method for full coverage path planning of a UAV according to claim 1, characterized in that: The step of determining the relay drone set according to the first initial path, the first communication distance of the first drone, and the second communication distance of the relay drone comprises: Calculate the communication relay capability using a preset path loss model and a preset signal interference model according to the first initial path and the second communication distance, and determine a first number of relay drones according to the communication relay capability and the first communication distance; The second number of redundant relay drones is determined based on the first number, and the number of drones in the relay drone set is determined based on the first number and the second number.

3. The method for full coverage path planning of a UAV according to claim 2, characterized in that: Determining a second initial path of the second UAV according to the first initial path includes: Determine a second preset destination of each relay drone in the set of relay drones according to the communication relay capability and the first initial path; The second initial path is determined according to the second preset end point and the preset starting point.

4. The method for full coverage path planning of a UAV according to claim 3, characterized in that: The method further comprises: Determining a preset calibration point according to the second preset end point, wherein the preset calibration point is located on the first initial path and the distance between the preset calibration point and the second preset end point is less than the first communication distance; Determining a second take-off time of the second UAV according to the first take-off time of the first UAV and the first communication distance; A first moment when the first UAV moves from the preset starting point to the preset calibration point is determined according to the first initial path, a second moment when the second UAV arrives at the preset calibration point is determined according to the second take-off moment and the first moment, and a flight speed of the second UAV is determined according to the second take-off moment and the second moment.

5. The method for full coverage path planning of a UAV according to claim 3, characterized in that: The step of determining the flight paths of the other drones in the transport drone group according to the actual load weight and type of the other drones and the environmental data includes: Calculate the flight performance parameters of each UAV according to the actual load weight and type of the other UAVs, wherein the flight performance parameters include maximum flight speed, maximum climb rate, minimum turning radius, and endurance time; generating an initial flight path for other drones in the transport drone group based on the environmental data and the flight performance parameters; The initial flight path is adjusted according to the second preset end point and the mutual safety distance to determine the flight path of the other UAVs.

6. The method for full coverage path planning of a UAV according to claim 1, characterized in that: The method further comprises: Detecting the real-time communication quality between the first UAV and each relay UAV in the relay UAV set, and selecting the best relay UAV from the relay UAV set as a temporary communication relay node according to the real-time communication quality and the current position and flight status of each relay UAV; Sending a relay instruction to the optimal relay UAV to allow the optimal relay UAV to adjust its flight attitude and connect the communication link between the first UAV and the central cloud platform; The flight speed, altitude or flight path of the first UAV is adjusted according to the remaining battery power of the first UAV and the estimated duration of the signal blocking.

7. The method for full coverage path planning of a UAV according to claim 1, characterized in that: The receiving the environmental data collected by the first UAV and adjusting the first initial path and the second initial path according to the environmental data includes: Extracting image data from the environment data, calculating difference values ​​between depth values ​​of adjacent pixels in the image data, and constructing a mapping of relative positions and depth relationships between points in the environment based on the difference values; Determining the location, shape, size, and terrain changes of obstacles through the mapping; The first initial path is adjusted according to the current position of the first UAV and the first preset end point, as well as obstacles and terrain features in the environment, and the second initial path is adjusted according to the adjusted first initial path.

8. A UAV full coverage path planning system, characterized in that: It includes a collection module, an initial module, a relay module, an adjustment module and an execution module, among which: A collection module is configured to obtain data information of all drones in the drone group, and obtain preset map information of the first target area, and determine the second target area for delivering materials according to the preset map information, wherein the data information includes a maximum load and a maximum flight time, and the preset map information is map information of the first target area before a preset time, and the drone group includes a transport drone group and a relay drone group; an initial module, configured to determine a flight radius of a first drone according to the data information, determine a first preset end point according to a preset starting point, the flight radius and the second target area, and determine a first initial path of the first drone according to the preset starting point, the first preset end point and preset map information, wherein the first drone is any one of the transport drone group; a relay module, configured to determine a relay drone set according to the first initial path, a first communication distance of the first drone, and a second communication distance of the relay drone, and determine a second initial path of a second drone according to the first initial path, wherein the second drone is any one of the relay drones; an adjustment module, configured to send a take-off instruction to the first UAV so that the first UAV flies according to the first initial path, send a take-off instruction to the second UAV so that the second UAV flies according to the second initial path, receive environmental data collected by the first UAV, and adjust the first initial path and the second initial path according to the environmental data, wherein the environmental data includes the location, shape, size, wind speed, wind direction, air pressure, and precipitation intensity of obstacles; The execution module is configured to determine the flight paths of the other drones in the transport drone group according to the weight and type of actual loads of the other drones in the transport drone group and the environmental data when the first drone reaches the preset destination. The other drones are drones in the transport drone group other than the first drone.

9. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

Images