Unmanned aerial vehicle cluster formation method

Abstract

The invention relates to the technical field of unmanned aerial vehicle clusters, in particular to an unmanned aerial vehicle cluster formation method. Comprising the following steps: system initialization and configuration, cluster communication and data exchange, collaborative algorithm calculation and control input, positioning and navigation assistance, global and dynamic task planning setting, energy management and optimization strategy implementation, and biological enlightenment formation control application. Compared with a traditional formation operation method, the unmanned aerial vehicle cluster formation method is higher in autonomy, and the unmanned aerial vehicle cluster formation is more accurate through an advanced autonomous flight control system and a sensor technology. Information sharing and cooperative actions among the unmanned aerial vehicles in the cluster formation are realized through an efficient communication technology, and even if part of the unmanned aerial vehicles fail, other unmanned aerial vehicles can still continue to execute tasks.

Description

Technical Field

[0001] The present invention relates to the technical field of drone clusters, and in particular to a drone cluster formation method. Background Art

[0002] A drone is an unmanned aircraft that uses radio remote control equipment and self-contained program control devices, while a drone swarm formation refers to multiple drones that are interconnected through network technology to form a centralized and unified whole, thereby collaboratively completing specific tasks. This formation system has the advantages of strong collaborative performance, flexible adaptability, and high task execution capability. Drone swarm formation technology involves multiple aspects such as communication, path planning, collision avoidance, distributed and centralized control, and is a complex and challenging field.

[0003] However, in general, in traditional formation operations, UAV formations often rely on ground control stations or pilot UAVs for command and control. Although this method can achieve formation flight to a certain extent, it has the disadvantage of strong dependence. The stability and effect of formation flight are highly dependent on the performance and stability of the ground control station or pilot UAV. Once the control station or pilot UAV fails, the entire formation may fall into chaos. Traditional formation operation methods are not flexible enough when dealing with emergencies and complex environments, and it is difficult to quickly adjust the formation structure and flight strategy. The communication distance and reliability between UAVs in the formation are limited, which may affect the overall effect and safety of the formation flight.

[0004] In summary, it is necessary to propose a drone swarm formation method to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a drone swarm formation method to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for forming a swarm of unmanned aerial vehicles comprises the following steps:

[0008] S1. System initialization and configuration;

[0009] S2. Cluster communication and data exchange;

[0010] S3. Collaborative algorithm calculation and control input;

[0011] S4. Perform positioning and navigation assistance;

[0012] S5. Set global and dynamic task planning;

[0013] S6. Implement energy management and optimization strategies;

[0014] S7. Application of biologically inspired formation control;

[0015] S8. Heterogeneous formation coordination and dynamic reconstruction;

[0016] S9. Environmental adaptive formation adjustment and optimization.

[0017] Preferably, the implementation process of step S1 is as follows:

[0018] S1.1. UAV status initialization: set the initial position, speed, acceleration and attitude of each UAV, and ensure that the communication system, sensor and navigation system of all UAVs work normally;

[0019] S1.2. Formation parameter setting: determine the target formation of the formation, including straight line, V-shape, circle, and set the basic parameters of speed, altitude and heading of the formation flight;

[0020] S1.3. Communication network configuration: Configure the communication network between drones, including selecting the communication protocol, setting the communication frequency and power, and testing the stability and reliability of the communication link.

[0021] Preferably, the implementation process of step S2 is as follows:

[0022] S2.1. Establish communication connection: Each drone starts the communication module, establishes connection with other drones in the cluster, checks the status of the communication connection, and ensures that all drones can communicate with each other;

[0023] S2.2. Data transmission and reception: Each drone regularly sends its own status information, including position, speed, acceleration and attitude, receives status information of other drones, and performs data analysis and processing;

[0024] S2.3. Data encryption and decryption: Encrypt the sent data to ensure the security of data transmission, and decrypt the received data to restore the original data.

[0025] Preferably, the implementation process of step S3 is as follows:

[0026] S3.1. Select the coordination algorithm: According to the formation needs and task requirements, select the Leader-Follower algorithm or the virtual structure coordination algorithm;

[0027] S3.2. Calculate control input: Based on the received state information, use the collaborative algorithm to calculate the control input that each UAV should take, and the control input includes speed adjustment, acceleration adjustment and attitude adjustment;

[0028] S3.3. Execute control instructions: Convert the calculated control input into specific control instructions, including motor speed and servo angle, execute control instructions, and adjust the motion state of the drone.

[0029] Preferably, the implementation process of step S4 is as follows:

[0030] S4.1. Multi-mode positioning: Use GPS, UWB, Mocap and other positioning technologies to obtain accurate location information of the drone, fuse multiple positioning data, and improve positioning accuracy and robustness;

[0031] S4.2. Environmental perception: Use sensors to perceive the surrounding environment, including obstacles and weather conditions, process and analyze the perceived data, and provide decision-making basis for obstacle avoidance and navigation;

[0032] S4.3. Path planning and obstacle avoidance: Plan the flight path of the drone according to mission requirements and environmental conditions, sense obstacles in real time during flight, and perform obstacle avoidance operations.

[0033] Preferably, the implementation process of step S5 is as follows:

[0034] S5.1. Global mission planning: Develop a global flight path and mission allocation plan based on mission requirements and the capabilities of the drone swarm to ensure that the drone swarm can complete the mission efficiently and orderly;

[0035] S5.2. Dynamic mission adjustment: During mission execution, dynamically adjust mission allocation and flight paths according to real-time conditions to respond to emergencies or mission changes, ensuring that the drone cluster can respond flexibly.

[0036] Preferably, the implementation process of step S6 is as follows:

[0037] S6.1. Energy monitoring: Real-time monitoring of the energy status of the drone, including battery power and flight time, processing and analyzing energy data, and predicting the remaining flight time of the drone;

[0038] S6.2. Energy-saving flight strategy: Develop energy-saving flight strategies based on mission requirements and energy status, dynamically adjust flight speed and altitude, optimize energy use, and extend flight time.

[0039] Preferably, the implementation process of step S7 is as follows:

[0040] S7.1. Imitate the bird flock mechanism: Introduce the cooperation mechanism in bird flock flight, including the exclusion area, direction area and attraction area, to achieve the adaptive formation control of UAVs and improve the stability and flexibility of the formation;

[0041] S7.2. Introduction of learning behavior: Through learning behavior between UAVs, the adaptability and flexibility of formation control are improved, and the reinforcement learning algorithm is used to optimize the formation strategy and improve the formation performance.

[0042] Preferably, the implementation process of step S8 is as follows:

[0043] S8.1. Support mixed aircraft types: realize heterogeneous formation flight combining fixed-wing and rotary-wing aircraft, and optimize formation control and task allocation according to the characteristics of different types of UAVs;

[0044] S8.2. Dynamic formation reconstruction: During the mission execution, the formation structure is dynamically adjusted according to the UAV status and mission requirements to ensure the flexibility and adaptability of the UAV formation in complex environments.

[0045] Preferably, the implementation process of step S9 is as follows:

[0046] S9.1. Environmental perception and assessment: Use sensors to perceive the surrounding environment, assess the impact of the environment on the UAV formation, and judge the environmental complexity and risk level in real time to provide a basis for formation adjustments;

[0047] S9.2. Dynamic adjustment of formation strategy: Based on the results of environmental assessment, dynamically adjust the formation strategy and flight parameters to ensure the safe flight and efficient operation of the UAV formation in different environments, conduct real-time evaluation and optimization of the formation adjustment effect, and improve the formation performance.

[0048] Compared with the prior art, the beneficial effects of the present invention are: the drone cluster formation method of the present invention is more autonomous than the traditional formation operation method, and the drone cluster formation can achieve autonomous formation flight to a certain extent through advanced autonomous flight control systems and sensor technology, reduce dependence on ground control stations, and can quickly adapt to complex and changeable environments and mission requirements, flexibly adjust formation structures and flight strategies, and achieve information sharing and coordinated actions between drones in the cluster formation through efficient communication technology, thereby improving the stability and effectiveness of the overall formation flight. Even if some drones fail, other drones can still continue to perform tasks, ensuring the continuity and stability of the overall cluster capability. Therefore, the drone cluster formation method proposed in the present invention can perform tasks by forming a formation of drones with relatively low costs on a large scale. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 The present invention is a flow chart of a UAV swarm formation method. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] Example

[0052] See also Figure 1 The present invention proposes a drone cluster formation method, comprising the following steps:

[0053] S1. System initialization and configuration:

[0054] S1.1. UAV status initialization: set the initial position, speed, acceleration and attitude of each UAV, and ensure that the communication system, sensor and navigation system of all UAVs work normally;

[0055] S1.2. Formation parameter setting: determine the target formation of the formation, including straight line, V-shape, circle, and set the basic parameters of speed, altitude and heading of the formation flight;

[0056] S1.3. Communication network configuration: Configure the communication network between drones, including selecting the communication protocol, setting the communication frequency and power, and testing the stability and reliability of the communication link;

[0057] Through this step, the unified initialization and parameter configuration of the UAV cluster status are achieved, ensuring the good status of each UAV before formation flight, laying the foundation for subsequent collaborative control;

[0058] S2. Cluster communication and data exchange:

[0059] S2.1. Establish communication connection: Each drone starts the communication module, establishes connection with other drones in the cluster, checks the status of the communication connection, and ensures that all drones can communicate with each other;

[0060] S2.2. Data transmission and reception: Each drone regularly sends its own status information, including position, speed, acceleration and attitude, receives status information of other drones, and performs data analysis and processing;

[0061] S2.3. Data encryption and decryption: Encrypt the sent data to ensure the security of data transmission, and decrypt the received data to restore the original data;

[0062] Through this step, real-time communication and data exchange between drones are realized, so that each drone can obtain the status information of other drones in the cluster, providing necessary data support for the calculation of collaborative algorithms;

[0063] S3. Collaborative algorithm calculation and control input:

[0064] S3.1. Select the coordination algorithm: According to the formation needs and task requirements, select the Leader-Follower algorithm or the virtual structure coordination algorithm;

[0065] S3.2. Calculate control input: Based on the received state information, use the collaborative algorithm to calculate the control input that each UAV should take. The control input includes speed adjustment, acceleration adjustment and attitude adjustment, as shown in formula (1):

[0066] u i (t) = f(x i (t),x j (t),...,x n (t),d ij ,...,d in ,θ ij ,...,θ in ) (1);

[0067] In the formula, u i (t) represents the control input of the i-th UAV at time t, x i (t) represents its state (such as position, speed, etc.), d ij represents the distance between the i-th UAV and the j-th UAV, θ ij represents the relative angle between them, and f is a function determined according to the collaborative algorithm;

[0068] S3.3. Execute control instructions: Convert the calculated control input into specific control instructions, including motor speed and servo angle, execute the control instructions, and adjust the motion state of the drone, as shown in formula (2):

[0069]

[0070] In the formula, represents the expected distance between the i-th UAV and the j-th UAV, ∈ is a small tolerance used to represent the allowable deviation range of the formation;

[0071] Through this step, the collaborative algorithm and the received status information are used to calculate the control input that each UAV should take, and the execution of control instructions and the maintenance of the formation are realized, which improves the stability and accuracy of formation flight;

[0072] S4. Perform positioning and navigation assistance:

[0073] S4.1. Multi-mode positioning: Use GPS, UWB, Mocap and other positioning technologies to obtain accurate location information of the drone, fuse multiple positioning data, and improve positioning accuracy and robustness;

[0074] S4.2. Environmental perception: Use sensors to perceive the surrounding environment, including obstacles and weather conditions, process and analyze the perceived data, and provide decision-making basis for obstacle avoidance and navigation;

[0075] S4.3. Path planning and obstacle avoidance: Plan the flight path of the drone according to mission requirements and environmental conditions, sense obstacles in real time during flight, and perform obstacle avoidance operations;

[0076] Through this step, a variety of positioning technologies and environmental perception information are used to assist the UAV in precise positioning and navigation, avoiding deviation from the predetermined trajectory and further improving the accuracy of formation flight;

[0077] S5. Set up global and dynamic task planning:

[0078] S5.1. Global mission planning: Develop a global flight path and mission allocation plan based on mission requirements and the capabilities of the drone swarm to ensure that the drone swarm can complete the mission efficiently and orderly;

[0079] S5.2. Dynamic task adjustment: During the task execution process, dynamically adjust the task allocation and flight path according to the real-time situation to deal with emergencies or task changes, ensuring that the drone cluster can respond flexibly;

[0080] Through this step, the planning of the global flight path and task allocation of the drone cluster is realized, as well as dynamic adjustment based on real-time conditions, ensuring that the drone cluster can complete the task efficiently and orderly;

[0081] S6. Implement energy management and optimization strategies:

[0082] S6.1. Energy monitoring: Real-time monitoring of the energy status of the drone, including battery power and flight time, processing and analyzing energy data, and predicting the remaining flight time of the drone;

[0083] S6.2. Energy-saving flight strategy: Formulate energy-saving flight strategies based on mission requirements and energy status, dynamically adjust flight speed and altitude, optimize energy use, and extend flight time;

[0084] Through this step, the real-time monitoring and management of the energy status of the UAVs, as well as the formulation and implementation of energy-saving flight strategies, are achieved, which prolongs the flight time of the UAVs and improves the durability of formation flight;

[0085] S7. Bio-inspired Formation Control Applications:

[0086] S7.1. Imitating the bird flock mechanism: Introducing the cooperation mechanism in bird flock flight, including the exclusion area, direction area and attraction area, to achieve the adaptive formation control of UAVs and improve the stability and flexibility of the formation, see formula (3):

[0087] R i (t) = g(x i (t),x j (t),...,x n (t),v i (t),v j (t),...,v n (t)) (3);

[0088] In the formula, R i (t) represents the repulsion area (or attraction area, etc.) of the i-th UAV at time t, g is a function determined according to the biological inspiration mechanism, and v i (t) represents the speed of the i-th UAV;

[0089] S7.2. Introduction of learning behavior: Through the learning behavior between UAVs, the adaptability and flexibility of formation control are improved, and the reinforcement learning algorithm is used to optimize the formation strategy and improve the formation performance;

[0090] Through this step, bio-inspired mechanisms and learning behaviors are introduced to achieve adaptive control and dynamic adjustment of the UAV formation, improve the flexibility and adaptability of formation flight, and enable UAVs to better cope with changes in complex environments;

[0091] S8. Heterogeneous team coordination and dynamic reconstruction:

[0092] S8.1. Support mixed aircraft types: realize heterogeneous formation flight combining fixed-wing and rotary-wing aircraft, and optimize formation control and task allocation according to the characteristics of different types of UAVs;

[0093] S8.2. Dynamic formation reconstruction: During the mission execution, the formation structure is dynamically adjusted according to the UAV status and mission requirements to ensure the flexibility and adaptability of the UAV formation in complex environments;

[0094] This step enables heterogeneous formation flight combining fixed-wing and rotary-wing aircraft, as well as dynamic reconstruction based on the status of drones and mission requirements, which improves the diversity and adaptability of formation flight and enables drone swarms to complete more types of tasks.

[0095] S9. Environmental Adaptive Formation Adjustment and Optimization:

[0096] S9.1. Environmental perception and assessment: Use sensors to perceive the surrounding environment, assess the impact of the environment on the UAV formation, and judge the environmental complexity and risk level in real time to provide a basis for formation adjustments;

[0097] S9.2. Dynamic adjustment of formation strategy: Dynamically adjust formation strategy and flight parameters according to environmental assessment results to ensure safe flight and efficient operation of UAV formations in different environments, conduct real-time evaluation and optimization of formation adjustment effects, and improve formation performance;

[0098] Through this step, real-time perception and assessment of environmental complexity and risk level are achieved, as well as dynamic adjustment and optimization of formation strategies based on the assessment results, which improves the safety and robustness of formation flight and enables the drone cluster to maintain a stable flight state in various environments.

[0099] 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 method for forming a swarm of drones, characterized in that: The following steps are involved: S1. System initialization and configuration; S2. Cluster communication and data exchange; S3. Collaborative algorithm calculation and control input; S4. Perform positioning and navigation assistance; S5. Set global and dynamic task planning; S6. Implement energy management and optimization strategies; S7. Application of biologically inspired formation control; S8. Heterogeneous formation coordination and dynamic reconstruction; S9. Environmental adaptive formation adjustment and optimization.

2. A drone swarm formation method according to claim 1, characterized in that: The implementation process of step S1 is as follows: S1.

1. UAV status initialization: set the initial position, speed, acceleration and attitude of each UAV, and ensure that the communication system, sensor and navigation system of all UAVs work normally; S1.

2. Formation parameter setting: determine the target formation of the formation, including straight line, V-shape, circle, and set the basic parameters of speed, altitude and heading of the formation flight; S1.

3. Communication network configuration: Configure the communication network between drones, including selecting the communication protocol, setting the communication frequency and power, and testing the stability and reliability of the communication link.

3. A method for forming a swarm of unmanned aerial vehicles according to claim 2, characterized in that: The implementation process of step S2 is as follows: S2.

1. Establish communication connection: Each drone starts the communication module, establishes connection with other drones in the cluster, checks the status of the communication connection, and ensures that all drones can communicate with each other; S2.

2. Data transmission and reception: Each drone regularly sends its own status information, including position, speed, acceleration and attitude, receives status information from other drones, and performs data analysis and processing; S2.

3. Data encryption and decryption: Encrypt the sent data to ensure the security of data transmission, and decrypt the received data to restore the original data.

4. A method for forming a swarm of unmanned aerial vehicles according to claim 3, characterized in that: The implementation process of step S3 is as follows: S3.

1. Select the coordination algorithm: According to the formation needs and task requirements, select the Leader-Follower algorithm or the virtual structure coordination algorithm; S3.

2. Calculate control input: Based on the received state information, use the collaborative algorithm to calculate the control input that each UAV should take, and the control input includes speed adjustment, acceleration adjustment and attitude adjustment; S3.

3. Execute control instructions: Convert the calculated control input into specific control instructions, including motor speed and servo angle, execute control instructions, and adjust the motion state of the drone.

5. A method for forming a swarm of unmanned aerial vehicles according to claim 4, characterized in that: The implementation process of step S4 is as follows: S4.

1. Multi-mode positioning: Use GPS, UWB, Mocap and other positioning technologies to obtain accurate location information of the drone, fuse multiple positioning data, and improve positioning accuracy and robustness; S4.

2. Environmental perception: Use sensors to perceive the surrounding environment, including obstacles and weather conditions, process and analyze the perceived data, and provide decision-making basis for obstacle avoidance and navigation; S4.

3. Path planning and obstacle avoidance: Plan the flight path of the drone according to mission requirements and environmental conditions, sense obstacles in real time during flight, and perform obstacle avoidance operations.

6. A method for forming a swarm of unmanned aerial vehicles according to claim 5, characterized in that: The implementation process of step S5 is as follows: S5.

1. Global mission planning: Develop a global flight path and mission allocation plan based on mission requirements and the capabilities of the drone swarm to ensure that the drone swarm can complete the mission efficiently and orderly; S5.

2. Dynamic mission adjustment: During mission execution, dynamically adjust mission allocation and flight paths according to real-time conditions to respond to emergencies or mission changes, ensuring that the drone cluster can respond flexibly.

7. A method for forming a swarm of drones according to claim 6, characterized in that: The implementation process of step S6 is as follows: S6.

1. Energy monitoring: Real-time monitoring of the energy status of the drone, including battery power and flight time, processing and analyzing energy data, and predicting the remaining flight time of the drone; S6.

2. Energy-saving flight strategy: Develop energy-saving flight strategies based on mission requirements and energy status, dynamically adjust flight speed and altitude, optimize energy use, and extend flight time.

8. A method for forming a swarm of unmanned aerial vehicles according to claim 7, characterized in that: The implementation process of step S7 is as follows: S7.

1. Imitate the bird flock mechanism: Introduce the cooperation mechanism in bird flock flight, including the exclusion area, direction area and attraction area, to achieve the adaptive formation control of UAVs and improve the stability and flexibility of the formation; S7.

2. Introduction of learning behavior: Through learning behavior between UAVs, the adaptability and flexibility of formation control are improved, and the reinforcement learning algorithm is used to optimize the formation strategy and improve the formation performance.

9. A method for forming a swarm of unmanned aerial vehicles according to claim 8, characterized in that: The implementation process of step S8 is as follows: S8.

1. Support mixed aircraft types: realize heterogeneous formation flight combining fixed-wing and rotary-wing aircraft, and optimize formation control and task allocation according to the characteristics of different types of UAVs; S8.

2. Dynamic formation reconstruction: During the mission execution, the formation structure is dynamically adjusted according to the UAV status and mission requirements to ensure the flexibility and adaptability of the UAV formation in complex environments.

10. A method for forming a swarm of unmanned aerial vehicles according to claim 9, characterized in that: The implementation process of step S9 is as follows: S9.

1. Environmental perception and assessment: Use sensors to perceive the surrounding environment, assess the impact of the environment on the UAV formation, and judge the environmental complexity and risk level in real time to provide a basis for formation adjustments; S9.

2. Dynamic adjustment of formation strategy: Based on the results of environmental assessment, dynamically adjust the formation strategy and flight parameters to ensure the safe flight and efficient operation of the UAV formation in different environments, conduct real-time evaluation and optimization of the formation adjustment effect, and improve the formation performance.

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