Wireless charging method for unmanned aerial vehicle group

Abstract

The invention discloses a wireless charging method for an unmanned aerial vehicle group, and the main points of the technical scheme are that the method comprises the following steps: S1, integrating a wireless charging receiving module, a communication module and an electric quantity monitoring module on each unmanned aerial vehicle; s2, when the electric quantity of a certain unmanned aerial vehicle is lower than a preset threshold value, the unmanned aerial vehicle sends a charging request to a nearby unmanned aerial vehicle and / or a charging base station through a communication module; s3, the charging scheduling system selects an optimal charging source (unmanned aerial vehicle or charging base station) and a charging path through an intelligent scheduling algorithm according to the real-time position, the electric quantity and the flight task state of the unmanned aerial vehicle group; s4, the unmanned aerial vehicle receives electric energy from the selected charging source through the wireless electric energy transmission technology, and charging is completed; according to the wireless charging method, efficient charging of the unmanned aerial vehicle group is supported, the optimal charging position is intelligently selected through the automatic moving charging base station, the wireless charging module adapts to different flight attitudes, the collision risk is reduced through the dynamic navigation function, and stable operation in various environments is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle charging, and in particular to a wireless charging method for a group of unmanned aerial vehicles. Background Art

[0002] Unmanned Aerial Vehicle (UAV) refers to an aircraft that can fly autonomously or remotely without an operator, including but not limited to multi-rotor UAVs, single-rotor UAVs, fixed-wing UAVs, tilt-rotor UAVs, etc. As aerial vehicles or aerial robots, it is well known that they have been used in many industries, including logistics, firefighting, inspection, exploration, photography, military inspection, etc. Existing UAVs are basically difficult to fly continuously in the air for more than one hour due to the limited continuous battery life of the battery. Therefore, UAVs need to perform endurance operations during continuous work. The existing charging methods mainly include wired charging and battery replacement, but both methods have obvious shortcomings.

[0003] At present, there are some wireless charging solutions for drones based on electromagnetic induction, radio wave transmission and other principles. However, these solutions are mostly limited to charging a single drone and fail to effectively solve the charging needs of drone swarms. In addition, the existing wireless charging system still has limitations in charging distance, efficiency, coverage and requirements for drone posture, and cannot achieve efficient and continuous power supply for drone swarms over a large area. To solve the above problems, we propose a wireless charging method for drone swarms. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a wireless charging method for a swarm of drones to solve the problems raised in the background technology.

[0005] The above technical objectives of the present invention are achieved through the following technical solutions:

[0006] A wireless charging method for a drone swarm comprises the following steps:

[0007] S1. Integrate a wireless charging receiving module, a communication module and a power monitoring module on each drone;

[0008] S2. When the power of a drone is lower than a preset threshold, the drone sends a charging request to nearby drones and / or charging base stations through the communication module;

[0009] S3. The charging scheduling system selects the optimal charging source (drone or charging base station) and charging path through an intelligent scheduling algorithm based on the real-time location, power level, and flight mission status of the drone group;

[0010] S4. The drone receives electrical energy from the selected charging source through wireless power transmission technology to complete charging.

[0011] Preferably, in step S1, the wireless charging receiving module adopts electromagnetic induction or magnetic resonance, and can perform short-distance wireless energy transmission between drones or between a drone and a charging base station, and the charging distance is 0.5 meters to 5 meters.

[0012] Preferably, in step S2, the charging base station is a mobile charging device with automatic movement capability, and the base station includes:

[0013] A1. The charging base station control unit is used to receive the battery status and location information of the drone group in real time, and determine the moving path of the charging base station through the path planning algorithm;

[0014] A2. Wireless charging transmitter module, used for wireless energy transmission to drone swarms;

[0015] A3. Autonomous navigation module, used to control the dynamic movement of the charging base station within the UAV flight area;

[0016] The charging base station control unit determines the moving path through the following path planning algorithm:

[0017] Path planning = Minimize (Σ distance_ij × charging requirement_i)

[0018] Among them, distance_ij is the distance from the charging base station to the i-th drone, and charging demand_i is the charging demand of the i-th drone.

[0019] Preferably, the mobile charging device achieves high-precision perception of the environment through laser radar, ultrasonic sensor and GPS positioning module, and determines the optimal charging position through control algorithm based on these data to reduce the risk of collision with the drone and maximize charging efficiency;

[0020] Optimal position formula: Optimal position = maximization (charging efficiency - collision risk);

[0021] Among them, the charging efficiency is the power receiving efficiency at the charging location, and the collision risk is the proximity of environmental obstacles.

[0022] Preferably, the drones in the drone group have a charging relay function, wherein the drones with sufficient power can provide power to the drones with low power through wireless power transmission technology, and the charging relay system includes:

[0023] B1. Charging relay control module, used to determine whether the drone is suitable for providing power;

[0024] Start condition: power ≥ threshold;

[0025] Among them, the power level is the current battery power level, and the threshold value is the set minimum power level;

[0026] B2. Wireless charging transmitter module, used to wirelessly transmit the power in the drone battery to other drones;

[0027] B3. Power regulation module, used to regulate power output to ensure transmission efficiency and safety;

[0028] Power regulation formula: P_out = P_in × efficiency factor;

[0029] Where P_out is the output power, P_in is the input power, and the efficiency factor is the adjusted efficiency percentage.

[0030] Preferably, the drone is equipped with an intelligent power management system, which dynamically adjusts the charging strategy according to the health status of the drone battery, mission priority, flight path and power consumption rate to ensure balanced power distribution of the drone group;

[0031] Among them, the charging strategy formula is: charging strategy = power health status × task priority × (1-power consumption rate / maximum consumption rate);

[0032] The battery health status is the current health status level of the battery, the task priority is the importance level of the task, the power consumption rate is the current consumption rate, and the maximum consumption rate is the maximum value in the historical record.

[0033] Preferably, the drone communicates with other drones and charging base stations via a self-organizing network, and the communication network allows real-time information sharing between drones, ensuring that charging requests can be quickly transmitted to drones with higher power in the drone group or the nearest charging base station.

[0034] Preferably, in step S3, the intelligent scheduling algorithm is dynamically adjusted through the following parameters:

[0035] C1. Real-time power level of the drone;

[0036] C2. The mission urgency of the drone;

[0037] C3. The relative position of the drone to the charging base station or other drones;

[0038] C4. The overall power distribution of the drone swarm;

[0039] Intelligent scheduling algorithm formula: Scheduling weight = power level × task urgency / distance.

[0040] Preferably, the charging base station can wirelessly charge at least two drones at the same time, and dynamically adjust the charging power of each wireless charging transmitting module according to the power and position status of the drones;

[0041] Power calculation formula: P_module = P_total / N × power adjustment factor;

[0042] Among them, P_module is the charging power of a single transmitting module, P_total is the total transmitting power of the charging base station, N is the number of drones charged at the same time, and the power adjustment factor is the adjustment factor set according to the drone power demand and charging efficiency.

[0043] In summary, the present invention mainly has the following beneficial effects:

[0044] 1. The present invention provides a wireless charging method that supports the charging needs of drone swarms. Through an intelligent scheduling system and dynamic charging path planning, drone swarms can achieve efficient charging in a wide flight area and are no longer limited to the charging of a single drone.

[0045] 2. The charging base station has the ability to move automatically, and combined with high-precision environmental perception technologies such as lidar, ultrasonic sensors and GPS, it can intelligently select the best charging location within the drone flight area, which solves the limitations of charging distance and coverage range and improves charging efficiency.

[0046] 3. The wireless charging receiving module supports electromagnetic induction and magnetic resonance technologies, which can adapt to certain posture changes and reduce the requirements for the drone posture. The drone group can complete charging in different flight postures, which improves the flexibility and adaptability of the system.

[0047] 4. The charging base station uses high-precision environmental perception technology (such as lidar and GPS) for dynamic navigation, reducing the risk of collision with drones. The charging method is suitable for various flight missions and complex environments, providing stable charging support and ensuring the continuous operation of the drone swarm. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flowchart diagram of the method of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0050] The following examples are used to illustrate the present invention, but they cannot be used to limit the scope of protection of the present invention. The conditions in the examples can be further adjusted according to specific conditions. Simple improvements to the method of the present invention under the premise of the concept of the present invention belong to the scope of protection claimed in the present invention.

[0051] refer to Figure 1 , a wireless charging method for a drone swarm, comprising the following steps:

[0052] S1. Integrate a wireless charging receiving module, a communication module and a power monitoring module on each drone;

[0053] S2. When the power of a drone is lower than a preset threshold, the drone sends a charging request to nearby drones and / or charging base stations through the communication module;

[0054] S3. The charging scheduling system selects the optimal charging source (drone or charging base station) and charging path through an intelligent scheduling algorithm based on the real-time location, power level, and flight mission status of the drone group;

[0055] S4. The drone receives electrical energy from the selected charging source through wireless power transmission technology to complete charging.

[0056] In step S1, the wireless charging receiving module uses electromagnetic induction or magnetic resonance to perform short-distance wireless energy transmission between drones or between a drone and a charging base station, and the charging distance is 0.5 meters to 5 meters.

[0057] In step S2, the charging base station is a mobile charging device with automatic movement capability, and the base station includes:

[0058] A1. The charging base station control unit is used to receive the battery status and location information of the drone group in real time, and determine the moving path of the charging base station through the path planning algorithm;

[0059] A2. Wireless charging transmitter module, used for wireless energy transmission to drone swarms;

[0060] A3. Autonomous navigation module, used to control the dynamic movement of the charging base station within the UAV flight area;

[0061] The charging base station control unit determines the moving path through the following path planning algorithm:

[0062] Path planning = Minimize (∑ distance_ij×charging requirement_i)

[0063] Among them, distance_ij is the distance from the charging base station to the i-th drone, and charging demand_i is the charging demand of the i-th drone.

[0064] Preferably, the mobile charging device achieves high-precision perception of the environment through laser radar, ultrasonic sensor and GPS positioning module, and determines the optimal charging position through control algorithm based on these data to reduce the risk of collision with the drone and maximize charging efficiency;

[0065] Optimal position formula: Optimal position = maximization (charging efficiency - collision risk);

[0066] Among them, the charging efficiency is the power receiving efficiency at the charging location, and the collision risk is the proximity of environmental obstacles.

[0067] The drones in the drone group have a charging relay function, wherein the drones with sufficient power can provide power to the drones with low power through wireless power transmission technology, and the charging relay system includes:

[0068] B1. Charging relay control module, used to determine whether the drone is suitable for providing power;

[0069] Start condition: power ≥ threshold;

[0070] Among them, the power level is the current battery power level, and the threshold value is the set minimum power level;

[0071] B2. Wireless charging transmitter module, used to wirelessly transmit the power in the drone battery to other drones;

[0072] B3. Power regulation module, used to regulate power output to ensure transmission efficiency and safety;

[0073] Power regulation formula: P_out = P_in × efficiency factor;

[0074] Where P_out is the output power, P_in is the input power, and the efficiency factor is the adjusted efficiency percentage.

[0075] The drones are equipped with an intelligent power management system, which dynamically adjusts the charging strategy according to the health status of the drone batteries, mission priority, flight path and power consumption rate to ensure a balanced power distribution in the drone swarm;

[0076] Among them, the charging strategy formula is: charging strategy = power health status × task priority × (1-power consumption rate / maximum consumption rate);

[0077] The battery health status is the current health status level of the battery, the task priority is the importance level of the task, the power consumption rate is the current consumption rate, and the maximum consumption rate is the maximum value in the historical record.

[0078] The drone communicates with other drones and charging base stations through a self-organizing network. The communication network allows real-time information sharing between drones, ensuring that charging requests can be quickly transmitted to drones with higher power in the drone group or the nearest charging base station.

[0079] In step S3, the intelligent scheduling algorithm is dynamically adjusted through the following parameters:

[0080] C1. Real-time power level of the drone;

[0081] C2. The mission urgency of the drone;

[0082] C3. The relative position of the drone to the charging base station or other drones;

[0083] C4. The overall power distribution of the drone swarm;

[0084] Intelligent scheduling algorithm formula: Scheduling weight = power level × task urgency / distance.

[0085] The charging base station can wirelessly charge at least two drones at the same time, and dynamically adjust the charging power of each wireless charging transmitting module according to the power level and position status of the drones;

[0086] Power calculation formula: P_module = P_total / N × power adjustment factor;

[0087] Among them, P_module is the charging power of a single transmitting module, P_total is the total transmitting power of the charging base station, N is the number of drones charged at the same time, and the power adjustment factor is the adjustment factor set according to the drone power demand and charging efficiency.

[0088] Example 1

[0089] Assume that there is a drone group performing a long-term monitoring mission. The mission area is large, and the flight time and charging requirements of the drones are uneven. In order to ensure that the drone group has sufficient power, the wireless charging method of the present invention is used as follows:

[0090] Each drone is integrated with a wireless charging receiving module, a communication module and a power monitoring module. The wireless charging receiving module uses electromagnetic induction technology, and the charging distance is set to 1 meter to ensure that wireless power can be effectively received during flight.

[0091] The power monitoring module of drone A detects that its power is lower than the set threshold (e.g. 20%), and sends a charging request to nearby drones and charging base stations through the communication module;

[0092] The charging scheduling system obtains the current location of drone A (e.g., GPS coordinates are (12.34, 56.78)), power status, and flight mission. Through the intelligent scheduling algorithm, it selects drone B, which is closest to drone A and has sufficient power, as the charging source, or selects mobile charging base station C for charging;

[0093] Drone A receives power from drone B or charging base station C through wireless power transmission technology (such as electromagnetic induction) to complete charging. If mobile charging base station C is selected, the base station monitors the environment in real time through laser radar, ultrasonic sensor and GPS positioning module to determine the best charging position, which is calculated by the formula;

[0094] If the distance between drone A and drone B is far, and drone B has sufficient power, while drone A has low power, drone B will start the charging relay function. The charging relay control module confirms that drone B is suitable for providing power, and transmits power to drone A through the wireless charging transmitter module. The power regulation module adjusts the output power to ensure transmission efficiency and safety;

[0095] The drone’s intelligent power management system will dynamically adjust the charging strategy based on battery health status, mission priority, etc.

[0096] All drones share real-time information through a self-organizing network. The real-time information sharing mechanism of the communication network ensures that charging requests can be quickly transmitted to drones with higher power or the nearest charging base station.

[0097] The charging base station can charge at least two drones at the same time, and the power of each wireless charging transmitter module is dynamically adjusted according to the power and location status of the drone.

[0098] Example 2

[0099] Assuming that in a city monitoring mission, a drone group needs to cover a large area and needs to be charged frequently to maintain continuous flight. To improve charging efficiency, the wireless charging method of the present invention is applied to this scenario as follows:

[0100] Each drone is equipped with a wireless charging receiving module and a power monitoring module. The charging base station is a mobile charging device with automatic navigation capabilities. The wireless charging module uses magnetic resonance technology, and the charging distance is set to 2 meters.

[0101] When the battery level of some drones in the drone swarm is lower than the preset threshold (e.g. 30%), these drones will send charging requests through the communication module. The charging scheduling system will schedule the drones according to their battery level, location and mission status. The intelligent scheduling algorithm will select the nearest charging base station and calculate the best charging path.

[0102] The charging base station moves dynamically within the mission area through the autonomous navigation module. The charging base station detects the environment through the lidar and GPS positioning modules to reduce the risk of collision and find the best charging location;

[0103] If drone B has sufficient power, it can charge drone A which has insufficient power. The relay charging function is enabled by the charging relay control module. The power is transmitted through the wireless charging transmitter module, and the power regulation module ensures the charging power adaptation.

[0104] The drone swarm’s power management system dynamically adjusts the charging strategy based on battery health status, flight mission urgency, and power consumption;

[0105] All drones share real-time information through a self-organizing network to ensure that charging requests can be quickly transmitted to drones with higher power or the nearest charging base station;

[0106] Charging power adjustment: The charging base station can charge multiple drones at the same time and dynamically adjust the charging power of each wireless charging transmitter module according to actual needs.

[0107] Through the above two embodiments, the wireless charging method of the present invention supports efficient charging of drone swarms. The optimal charging position is intelligently selected through the automatically moving charging base station. The wireless charging module adapts to different flight postures. The dynamic navigation function reduces the risk of collision and ensures stable operation in various environments.

[0108] Although the embodiments of the present invention have been shown and described, it is understandable to those skilled in the art that, unless otherwise defined, the technical terms or scientific terms used in the present invention shall have the usual meanings understood by persons with ordinary skills in the field to which the present invention belongs. The words "including" or "comprising" and the like used in the present invention mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. The words "connect" or "connected" and the like are not limited to physical or mechanical connections, but may also include electrical connections, whether direct or indirect. "Up", "down", "left", "right", etc. are only used to indicate relative position relationships. When the absolute position of the described object changes, the relative position relationship may also change accordingly.

[0109] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A wireless charging method for a drone swarm, characterized in that: The following steps are involved: S1. Integrate a wireless charging receiving module, a communication module and a power monitoring module on each drone; S2. When the power of a drone is lower than a preset threshold, the drone sends a charging request to nearby drones and / or charging base stations through the communication module; S3. The charging scheduling system selects the optimal charging source (drone or charging base station) and charging path through an intelligent scheduling algorithm based on the real-time location, power level, and flight mission status of the drone group; S4. The drone receives electrical energy from the selected charging source through wireless power transmission technology to complete charging.

2. A wireless charging method for a drone swarm according to claim 1, characterized in that: In step S1, the wireless charging receiving module uses electromagnetic induction or magnetic resonance to perform short-distance wireless energy transmission between drones or between a drone and a charging base station, and the charging distance is 0.5 meters to 5 meters.

3. A wireless charging method for a drone swarm according to claim 1, characterized in that: In step S2, the charging base station is a mobile charging device with automatic movement capability, and the base station includes: A1. The charging base station control unit is used to receive the battery status and location information of the drone group in real time, and determine the moving path of the charging base station through the path planning algorithm; A2. Wireless charging transmitter module, used for wireless energy transmission to drone swarms; A3. Autonomous navigation module, used to control the dynamic movement of the charging base station within the UAV flight area.

4. A wireless charging method for a drone swarm according to claim 3, characterized in that: The mobile charging device achieves high-precision perception of the environment through lidar, ultrasonic sensors and GPS positioning modules, and determines the optimal charging position through a control algorithm based on these data to reduce the risk of collision with drones and maximize charging efficiency.

5. The wireless charging method for a drone swarm according to claim 1, characterized in that: The drones in the drone group have a charging relay function, wherein the drones with sufficient power can provide power to the drones with low power through wireless power transmission technology, and the charging relay system includes: B1. Charging relay control module, used to determine whether the drone is suitable for providing power; B2. Wireless charging transmitter module, used to wirelessly transmit the power in the drone battery to other drones; B3. Power regulation module, used to regulate the power output to ensure transmission efficiency and safety.

6. A wireless charging method for a drone swarm according to claim 1, characterized in that: The drones are equipped with an intelligent power management system that dynamically adjusts the charging strategy based on the health status of the drone batteries, mission priority, flight path and power consumption rate to ensure balanced power distribution in the drone swarm.

7. A wireless charging method for a drone swarm according to claim 1, characterized in that: The drone communicates with other drones and charging base stations through a self-organizing network. The communication network allows real-time information sharing between drones, ensuring that charging requests can be quickly transmitted to drones with higher power in the drone group or the nearest charging base station.

8. The wireless charging method for a drone swarm according to claim 1, characterized in that: In step S3, the intelligent scheduling algorithm is dynamically adjusted through the following parameters: C1. The real-time power level of the drone; C2. The mission urgency of the drone; C3. The relative position of the drone to the charging base station or other drones; C4. The overall power distribution of the drone swarm.

9. A wireless charging method for a drone swarm according to claim 1, characterized in that: The charging base station can wirelessly charge at least two drones at the same time, and dynamically adjust the charging power of each wireless charging transmitting module according to the power level and position status of the drones.

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