CN117572891A - Unmanned aerial vehicle cluster system and working method thereof - Google Patents
Unmanned aerial vehicle cluster system and working method thereof Download PDFInfo
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Abstract
The invention discloses an unmanned aerial vehicle cluster system and a working method thereof, wherein the unmanned aerial vehicle cluster system comprises a communication module, a collaboration module, a collision avoidance module, a navigation module, a positioning module, an environment adaptability module, a security module, a data processing module and a real-time decision module, wherein the communication module, the collaboration module, the collision avoidance module, the navigation module, the positioning module, the environment adaptability module, the security module, the data processing module and the real-time decision module are in communication connection, the positioning accuracy is improved through an environment sensing unit, GPS positioning is corrected by using an external sensor or map data, the navigation and positioning accuracy is improved, sensor data can be received and processed, a real-time data analysis algorithm is operated, useful information is extracted, history data is learned by using a machine learning algorithm, a flight strategy is optimized, and real-time decisions and instructions are made through the real-time decision module so as to adapt to environment changes and optimize system performance.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle clusters, in particular to an unmanned aerial vehicle cluster system and a working method thereof.
Background
Unmanned aerial vehicle cluster refers to the cooperation of a plurality of unmanned aerial vehicles to complete complex tasks or cover a large area, and the technology brings about innovation in various fields, and the unmanned aerial vehicle cluster is various in application, including but not limited to military application, emergency rescue and intelligent agriculture.
Generally, the traditional unmanned aerial vehicle cluster system scheme is poor in performance on large-scale data processing or complex computing tasks, needs more hardware, manpower and time resources to coordinate at any time, is relatively high in cost, has the problem of expandability when handling large-scale problems, cannot cope with increased workload, and is poor in performance when dealing with fast-changing demands or environments, and the traditional unmanned aerial vehicle cluster system is low in performance, function, interactivity and acceptance due to the fact that the traditional unmanned aerial vehicle cluster system is based on the technology and the standard.
In summary, an unmanned aerial vehicle cluster system and a working method thereof are required to be provided to solve the problems.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle cluster system and a working method thereof, which are used for solving the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
an unmanned aerial vehicle cluster system comprises a communication module, a coordination module, a collision avoidance module, a navigation module, a positioning module, an environment adaptability module, a security module, a data processing module and a real-time decision module,
the system comprises a communication module, a coordination module, a collision avoidance module, a navigation module, a positioning module, an environment adaptability module, a safety module, a data processing module and a real-time decision module, wherein the communication module, the coordination module, the collision avoidance module, the navigation module, the positioning module, the environment adaptability module, the safety module, the data processing module and the real-time decision module are in communication connection;
the communication module is in bidirectional connection with the collaboration module, the communication module is in bidirectional connection with the collision avoidance module, the communication module is in bidirectional connection with the navigation module, the communication module is in bidirectional connection with the positioning module, the communication module is in bidirectional connection with the environment adaptation module, the communication module is in bidirectional connection with the security module, the communication module is in bidirectional connection with the data processing module, and the communication module is in bidirectional connection with the real-time decision module for supporting the issuing of instructions and the uploading of data;
the cooperative module is in bidirectional connection with the communication module, the cooperative module is in bidirectional connection with the collision avoidance module, the cooperative module is in bidirectional connection with the navigation module, and the cooperative module is in bidirectional connection with the real-time decision module and is used for receiving task instructions and sharing cooperative information;
the collision avoidance module is in bidirectional connection with the communication module, the collision avoidance module is in bidirectional connection with the cooperative module, the collision avoidance module is in bidirectional connection with the navigation module, the collision avoidance module is in bidirectional connection with the positioning module, and the collision avoidance module is in bidirectional connection with the real-time decision module and is used for receiving flight information and providing collision avoidance suggestions;
the navigation module is in bidirectional connection with the communication module, the navigation module is in bidirectional connection with the cooperative module, the navigation module is in bidirectional connection with the collision avoidance module, the navigation module is in bidirectional connection with the positioning module, and the navigation module is in bidirectional connection with the real-time decision module and is used for receiving task path information and providing current position and navigation adjustment;
the positioning module is in bidirectional connection with the communication module, the positioning module is in bidirectional connection with the cooperative module, the positioning module is in bidirectional connection with the navigation module, the positioning module is in bidirectional connection with the environment adaptability module, and the positioning module is in bidirectional connection with the real-time decision module and is used for providing current position information and receiving environment information which possibly influences positioning;
the environment adaptation module is in bidirectional connection with the communication module, the environment adaptation module is in bidirectional connection with the cooperative module, the environment adaptation module is in bidirectional connection with the collision avoidance module, and the environment adaptation module is in bidirectional connection with the real-time decision module and is used for receiving safety instructions and providing system states and safety suggestions;
the data processing module is in bidirectional connection with the communication module, the data processing module is in bidirectional connection with the navigation module, the data processing module is in bidirectional connection with the environment adaptability module, and the data processing module is in bidirectional connection with the real-time decision module and is used for receiving and processing sensor data and providing processed information;
the real-time decision module is in bidirectional connection with the communication module, the real-time decision module is in bidirectional connection with the cooperative module, the real-time decision module is in bidirectional connection with the collision avoidance module, the real-time decision module is in bidirectional connection with the navigation module, the real-time decision module is in bidirectional connection with the environment adaptation module, the real-time decision module is in bidirectional connection with the security module, and the real-time decision module is in bidirectional connection with the data processing module and is used for receiving information from other modules and providing real-time decisions and instructions.
Preferably, the communication module includes a communication protocol processing unit and a network management unit; the communication protocol processing unit is used for processing a communication protocol and ensuring effective communication with other unmanned aerial vehicles; the network management unit is used for managing the internal and external communication networks of the cluster, and processing communication route and bandwidth allocation.
Preferably, the collaboration module comprises a collaboration control unit and a task allocation and planning unit; the cooperative control unit is used for ensuring cooperative work among unmanned aerial vehicles, making a flight plan and avoiding collision; the task allocation and planning unit is used for allocating tasks to different unmanned aerial vehicles in the cluster and planning an execution path.
Preferably, the collision avoidance module includes a sensor fusion unit and a collision prediction and avoidance unit; the sensor fusion unit is used for fusing data from the sensor, so that the accuracy of collision detection is improved, and the sensor data comprise a camera, a radar and a laser radar; the collision prediction and avoidance unit is used for analyzing the data, predicting collision risk and taking avoidance measures.
Preferably, the navigation module comprises an inertial navigation unit and a visual navigation unit; the inertial navigation unit is used for providing real-time monitoring and estimation of the unmanned aerial vehicle motion; the visual navigation unit utilizes a visual sensor to perform landmark recognition and navigation; the positioning module comprises a GPS unit and an environment sensing unit; the GPS unit is used for providing global positioning information; the environment sensing unit improves positioning accuracy through external sensors or map data.
Preferably, the environment adaptation module comprises a weather sensor and an environment map unit; the weather sensor is used for detecting weather conditions and adjusting a flight plan; the environment map unit is used for constructing and updating a flight environment map and supporting flight decisions.
Preferably, the safety module comprises a flight control unit and a protection unit; the flight control unit is used for monitoring and controlling the flight, and comprises emergency response to abnormal conditions; the protection unit is used for preventing potential physical attacks or interference.
Preferably, the data processing module includes a data processing unit and a machine learning unit; the data processing unit is used for processing the sensor data and carrying out real-time data analysis; the machine learning unit is used for real-time decision making, learning and optimizing the flight strategy.
Preferably, the real-time decision module comprises a decision unit and a path planning unit; the decision unit makes real-time decisions based on the sensor data and task requirements; and the path planning unit dynamically adjusts the path of the unmanned aerial vehicle according to the task and the environment.
A working method of an unmanned aerial vehicle cluster system,
s1, starting communication, ensuring that unmanned aerial vehicles use compatible communication protocols, processing handshake and authentication processes of the communication protocols, ensuring safety of communication in a cluster, monitoring communication quality in real time, adjusting communication frequency and protocols according to requirements, managing network topology in the cluster, ensuring smooth communication among all unmanned aerial vehicles, processing external communication, dynamically adjusting communication routes, adapting to changes of the network topology, and carrying out unmanned aerial vehicle cluster flight tasks;
s2, periodically updating cluster states including positions, speeds and task completion conditions in the flight process of the unmanned aerial vehicle, making a strategy for maintaining cooperative flight according to the cluster states, avoiding collision and optimizing path planning, and supporting the treatment of abnormal conditions, such as replacement of the unmanned aerial vehicle with a failure or a connection failure;
s3, receiving task requirements from a task control center in the flight process of the unmanned aerial vehicle, dynamically distributing tasks according to the task types, priorities and the current state of the unmanned aerial vehicle, planning task execution paths, and considering factors such as obstacle avoidance, energy efficiency and the like;
s4, collecting data from various sensors in the flight process of the unmanned aerial vehicle, wherein the data are provided by a camera, a radar and a laser radar, carrying out space-time fusion on the data, establishing overall perception on the surrounding environment of the unmanned aerial vehicle, carrying out collision prediction by using the fused sensor data, identifying potential collision risks, formulating an avoidance strategy, and updating the avoidance strategy in real time to adapt to environmental changes, wherein the avoidance strategy comprises adjustment of flight height, speed or path;
s5, providing real-time unmanned aerial vehicle motion state based on an inertial sensor in the unmanned aerial vehicle flight process, fusing other positioning data, improving navigation accuracy, utilizing a camera and an image processing algorithm to perform landmark recognition and relative positioning, providing global positioning information under the condition of no GPS signal, providing high-precision positioning information as a reference of a navigation system when an open area is opened, correcting GPS positioning by using an external sensor or map data, improving positioning accuracy, updating a flight environment map, and supporting path planning and navigation;
s6, detecting wind speed, temperature and humidity meteorological conditions in the flight process of the unmanned aerial vehicle, adjusting a flight plan according to meteorological data, ensuring safety and smooth completion of tasks, constructing an environment map, identifying terrain features, obstacles and task targets, and updating the map in real time according to environmental changes so as to support path planning and decision making;
s7, monitoring the state of the unmanned aerial vehicle in real time, including the battery power, the temperature and the sensor state, taking emergency measures such as returning or safe landing for abnormal conditions, and taking corresponding measures to protect a communication and navigation system and ensure the stability and reliability of the system when detecting potential physical attacks or interferences such as signal interference or electromagnetic attack;
s8, receiving, storing and processing sensor data in the flight process of the unmanned aerial vehicle, running a real-time data analysis algorithm, extracting useful information, learning historical data by using a machine learning algorithm, optimizing a flight strategy, and making a real-time decision to adapt to environmental changes;
s9, making real-time decisions according to unmanned aerial vehicle flight task requirements, environmental changes and sensor data, and adjusting flight plans, path plans and task execution strategies to optimize system performance;
s10, planning a path of the unmanned aerial vehicle according to the flight task requirements, the environment map and the flight state of the unmanned aerial vehicle, adopting a dynamic planning or model-based method, considering obstacle avoidance, optimizing the flight time and minimizing the energy consumption factors, and ensuring the safety and the high efficiency of the path.
Compared with the prior art, the invention has the beneficial effects that: according to the method, cooperative work between unmanned aerial vehicle clusters is achieved through bidirectional connection between the communication module and the cooperative module, task instructions and cooperative information are shared, the unmanned aerial vehicle clusters can execute tasks efficiently, collision avoidance suggestions are provided, data are analyzed through the sensor fusion unit and the collision prediction avoidance unit, collision risk is predicted, a collision avoidance strategy is formulated, the unmanned aerial vehicle clusters are ensured to avoid collision in flight, real-time unmanned aerial vehicle motion state and positioning information are provided through the inertial navigation and visual navigation unit, positioning accuracy is improved through the environment sensing unit, GPS positioning is corrected through the external sensor or map data, navigation and positioning accuracy is improved, meanwhile, weather conditions are detected through the weather sensor and the environment map unit, a flight plan is adjusted, a flight environment map is constructed and updated to provide environment adaptability and flight safety, sensor data can be received and processed, a real-time data analysis algorithm is operated, useful information is extracted, history data are learned through the machine learning algorithm, the flight strategy is optimized, real-time decisions and instructions are made through the real-time module, and the environment change and the system performance is optimized, and the experience and acceptance are higher.
Drawings
Fig. 1 is a diagram of a cluster system of unmanned aerial vehicles according to the present invention;
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention provides a technical solution:
an unmanned aerial vehicle cluster system comprises a communication module, a coordination module, a collision avoidance module, a navigation module, a positioning module, an environment adaptability module, a security module, a data processing module and a real-time decision module,
the system comprises a communication module, a coordination module, a collision avoidance module, a navigation module, a positioning module, an environment adaptability module, a safety module, a data processing module and a real-time decision module, wherein the communication module, the coordination module, the collision avoidance module, the navigation module, the positioning module, the environment adaptability module, the safety module and the data processing module are in communication connection;
the communication module is in bidirectional connection with the cooperative module, the communication module is in bidirectional connection with the collision avoidance module, the communication module is in bidirectional connection with the navigation module, the communication module is in bidirectional connection with the positioning module, the communication module is in bidirectional connection with the environment adaptation module, the communication module is in bidirectional connection with the safety module, the communication module is in bidirectional connection with the data processing module, and the communication module is in bidirectional connection with the real-time decision module for supporting the issuing of instructions and the uploading of data;
the cooperative module is connected with the communication module in a bidirectional way, and the cooperative module is connected with the collision avoidance module in a bidirectional way,
The collaborative module is in bidirectional connection with the navigation module, and is in bidirectional connection with the real-time decision module, and is used for receiving task instructions and sharing collaborative information;
the collision avoidance module is in bidirectional connection with the communication module, the collision avoidance module is in bidirectional connection with the cooperative module, the collision avoidance module is in bidirectional connection with the navigation module, the collision avoidance module is in bidirectional connection with the positioning module, and the collision avoidance module is in bidirectional connection with the real-time decision module and is used for receiving flight information and providing collision avoidance suggestions;
the navigation module is in bidirectional connection with the communication module, the navigation module is in bidirectional connection with the cooperative module, the navigation module is in bidirectional connection with the collision avoidance module, the navigation module is in bidirectional connection with the positioning module, and the navigation module is in bidirectional connection with the real-time decision module and is used for receiving task path information and providing current position and navigation adjustment;
the positioning module is in bidirectional connection with the communication module, the positioning module is in bidirectional connection with the coordination module, the positioning module is in bidirectional connection with the navigation module, the positioning module is in bidirectional connection with the environment adaptability module, and the positioning module is in bidirectional connection with the real-time decision module and is used for providing current position information and receiving environment information which possibly influences positioning;
the environment adaptation module is in bidirectional connection with the communication module, the environment adaptation module is in bidirectional connection with the cooperative module, the environment adaptation module is in bidirectional connection with the collision avoidance module, and the environment adaptation module is in bidirectional connection with the real-time decision module and is used for receiving safety instructions and providing system states and safety suggestions;
the data processing module is in bidirectional connection with the communication module, the data processing module is in bidirectional connection with the navigation module, the data processing module is in bidirectional connection with the environment adaptation module, and the data processing module is in bidirectional connection with the real-time decision module and is used for receiving and processing sensor data and providing processed information;
the real-time decision module is in bidirectional connection with the communication module, the real-time decision module is in bidirectional connection with the cooperative module, the real-time decision module is in bidirectional connection with the collision avoidance module, the real-time decision module is in bidirectional connection with the navigation module, the real-time decision module is in bidirectional connection with the environment adaptation module, the real-time decision module is in bidirectional connection with the safety module, and the real-time decision module is in bidirectional connection with the data processing module and is used for receiving information from other modules and providing real-time decisions and instructions.
Further, the communication module comprises a communication protocol processing unit and a network management unit; the communication protocol processing unit is used for processing a communication protocol and ensuring effective communication with other unmanned aerial vehicles; the network management unit is used for managing the internal and external communication networks of the cluster, and processing communication route and bandwidth allocation.
Further, the collaboration module comprises a collaboration control unit and a task allocation and planning unit; the cooperative control unit is used for ensuring cooperative work among unmanned aerial vehicles, making a flight plan and avoiding collision; the task allocation and planning unit is used for allocating tasks to different unmanned aerial vehicles in the cluster and planning an execution path.
Further, the collision avoidance module includes a sensor fusion unit and a collision prediction and avoidance unit; the sensor fusion unit is used for fusing data from the sensor, so that the accuracy of collision detection is improved, and the sensor data comprise a camera, a radar and a laser radar; the collision prediction and avoidance unit is used for analyzing the data, predicting collision risk and taking avoidance measures.
Further, the navigation module comprises an inertial navigation unit and a visual navigation unit; the inertial navigation unit is used for providing real-time monitoring and estimation of the unmanned aerial vehicle motion; the visual navigation unit utilizes a visual sensor to perform landmark recognition and navigation; the positioning module comprises a GPS unit and an environment sensing unit; the GPS unit is used for providing global positioning information; the environment sensing unit improves positioning accuracy through external sensors or map data.
Further, the environment adaptation module comprises a meteorological sensor and an environment map unit; the weather sensor is used for detecting weather conditions and adjusting a flight plan; the environment map unit is used for constructing and updating a flight environment map and supporting flight decisions.
Further, the safety module comprises a flight control unit and a protection unit; the flight control unit is used for monitoring and controlling the flight, and comprises emergency response to abnormal conditions; the protection unit is used for preventing potential physical attacks or interference.
Further, the data processing module comprises a data processing unit and a machine learning unit; the data processing unit is used for processing the sensor data and carrying out real-time data analysis; the machine learning unit is used for real-time decision making, learning and optimizing the flight strategy.
Further, the real-time decision module comprises a decision unit and a path planning unit; the decision unit makes real-time decisions based on the sensor data and task requirements; and the path planning unit dynamically adjusts the path of the unmanned aerial vehicle according to the task and the environment.
A working method of an unmanned aerial vehicle cluster system,
s1, starting communication, ensuring that unmanned aerial vehicles use compatible communication protocols, processing handshake and authentication processes of the communication protocols, ensuring safety of communication in a cluster, monitoring communication quality in real time, adjusting communication frequency and protocols according to requirements, managing network topology in the cluster, ensuring smooth communication among all unmanned aerial vehicles, processing external communication, dynamically adjusting communication routes, adapting to changes of the network topology, and carrying out unmanned aerial vehicle cluster flight tasks;
s2, periodically updating cluster states including positions, speeds and task completion conditions in the flight process of the unmanned aerial vehicle, making a strategy for maintaining cooperative flight according to the cluster states, avoiding collision and optimizing path planning, and supporting the treatment of abnormal conditions, such as replacement of the unmanned aerial vehicle with a failure or a connection failure;
s3, receiving task requirements from a task control center in the flight process of the unmanned aerial vehicle, dynamically distributing tasks according to the task types, priorities and the current state of the unmanned aerial vehicle, planning task execution paths, and considering factors such as obstacle avoidance, energy efficiency and the like;
s4, collecting data from various sensors in the flight process of the unmanned aerial vehicle, wherein the data are provided by a camera, a radar and a laser radar, carrying out space-time fusion on the data, establishing overall perception on the surrounding environment of the unmanned aerial vehicle, carrying out collision prediction by using the fused sensor data, identifying potential collision risks, formulating an avoidance strategy, and updating the avoidance strategy in real time to adapt to environmental changes, wherein the avoidance strategy comprises adjustment of flight height, speed or path;
s5, providing real-time unmanned aerial vehicle motion state based on an inertial sensor in the unmanned aerial vehicle flight process, fusing other positioning data, improving navigation accuracy, utilizing a camera and an image processing algorithm to perform landmark recognition and relative positioning, providing global positioning information under the condition of no GPS signal, providing high-precision positioning information as a reference of a navigation system when an open area is opened, correcting GPS positioning by using an external sensor or map data, improving positioning accuracy, updating a flight environment map, and supporting path planning and navigation;
s6, detecting wind speed, temperature and humidity meteorological conditions in the flight process of the unmanned aerial vehicle, adjusting a flight plan according to meteorological data, ensuring safety and smooth completion of tasks, constructing an environment map, identifying terrain features, obstacles and task targets, and updating the map in real time according to environmental changes so as to support path planning and decision making;
s7, monitoring the state of the unmanned aerial vehicle in real time, including the battery power, the temperature and the sensor state, taking emergency measures such as returning or safe landing for abnormal conditions, and taking corresponding measures to protect a communication and navigation system and ensure the stability and reliability of the system when detecting potential physical attacks or interferences such as signal interference or electromagnetic attack;
s8, receiving, storing and processing sensor data in the flight process of the unmanned aerial vehicle, running a real-time data analysis algorithm, extracting useful information, learning historical data by using a machine learning algorithm, optimizing a flight strategy, and making a real-time decision to adapt to environmental changes;
s9, making real-time decisions according to unmanned aerial vehicle flight task requirements, environmental changes and sensor data, and adjusting flight plans, path plans and task execution strategies to optimize system performance;
s10, planning a path of the unmanned aerial vehicle according to the flight task requirements, the environment map and the flight state of the unmanned aerial vehicle, adopting a dynamic planning or model-based method, considering obstacle avoidance, optimizing the flight time and minimizing the energy consumption factors, and ensuring the safety and the high efficiency of the path.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. Unmanned aerial vehicle cluster system, including communication module, cooperation module, collision avoidance module, navigation module, positioning module, environment adaptability module, security module, data processing module and real-time decision-making module, its characterized in that:
the system comprises a communication module, a coordination module, a collision avoidance module, a navigation module, a positioning module, an environment adaptability module, a safety module, a data processing module and a real-time decision module, wherein the communication module, the coordination module, the collision avoidance module, the navigation module, the positioning module, the environment adaptability module, the safety module, the data processing module and the real-time decision module are in communication connection;
the communication module is in bidirectional connection with the collaboration module, the communication module is in bidirectional connection with the collision avoidance module, the communication module is in bidirectional connection with the navigation module, the communication module is in bidirectional connection with the positioning module, the communication module is in bidirectional connection with the environment adaptation module, the communication module is in bidirectional connection with the security module, the communication module is in bidirectional connection with the data processing module, and the communication module is in bidirectional connection with the real-time decision module for supporting the issuing of instructions and the uploading of data;
the cooperative module is in bidirectional connection with the communication module, the cooperative module is in bidirectional connection with the collision avoidance module, the cooperative module is in bidirectional connection with the navigation module, and the cooperative module is in bidirectional connection with the real-time decision module and is used for receiving task instructions and sharing cooperative information;
the collision avoidance module is in bidirectional connection with the communication module, the collision avoidance module is in bidirectional connection with the cooperative module, the collision avoidance module is in bidirectional connection with the navigation module, the collision avoidance module is in bidirectional connection with the positioning module, and the collision avoidance module is in bidirectional connection with the real-time decision module and is used for receiving flight information and providing collision avoidance suggestions;
the navigation module is in bidirectional connection with the communication module, the navigation module is in bidirectional connection with the cooperative module, the navigation module is in bidirectional connection with the collision avoidance module, the navigation module is in bidirectional connection with the positioning module, and the navigation module is in bidirectional connection with the real-time decision module and is used for receiving task path information and providing current position and navigation adjustment;
the positioning module is in bidirectional connection with the communication module, the positioning module is in bidirectional connection with the cooperative module, the positioning module is in bidirectional connection with the navigation module, the positioning module is in bidirectional connection with the environment adaptability module, and the positioning module is in bidirectional connection with the real-time decision module and is used for providing current position information and receiving environment information which possibly influences positioning;
the environment adaptation module is in bidirectional connection with the communication module, the environment adaptation module is in bidirectional connection with the cooperative module, the environment adaptation module is in bidirectional connection with the collision avoidance module, and the environment adaptation module is in bidirectional connection with the real-time decision module and is used for receiving safety instructions and providing system states and safety suggestions;
the data processing module is in bidirectional connection with the communication module, the data processing module is in bidirectional connection with the navigation module, the data processing module is in bidirectional connection with the environment adaptability module, and the data processing module is in bidirectional connection with the real-time decision module and is used for receiving and processing sensor data and providing processed information;
the real-time decision module is in bidirectional connection with the communication module, the real-time decision module is in bidirectional connection with the cooperative module, the real-time decision module is in bidirectional connection with the collision avoidance module, the real-time decision module is in bidirectional connection with the navigation module, the real-time decision module is in bidirectional connection with the environment adaptation module, the real-time decision module is in bidirectional connection with the security module, and the real-time decision module is in bidirectional connection with the data processing module and is used for receiving information from other modules and providing real-time decisions and instructions.
2. The system according to claim 1, wherein:
the communication module comprises a communication protocol processing unit and a network management unit;
the communication protocol processing unit is used for processing a communication protocol and ensuring effective communication with other unmanned aerial vehicles;
the network management unit is used for managing the internal and external communication networks of the cluster, and processing communication route and bandwidth allocation.
3. The system according to claim 2, wherein:
the collaboration module comprises a collaboration control unit and a task allocation and planning unit;
the cooperative control unit is used for ensuring cooperative work among unmanned aerial vehicles, making a flight plan and avoiding collision;
the task allocation and planning unit is used for allocating tasks to different unmanned aerial vehicles in the cluster and planning an execution path.
4. A system according to claim 3, characterized in that:
the collision avoidance module comprises a sensor fusion unit and a collision prediction and avoidance unit;
the sensor fusion unit is used for fusing data from the sensor, so that the accuracy of collision detection is improved, and the sensor data comprise a camera, a radar and a laser radar;
the collision prediction and avoidance unit is used for analyzing the data, predicting collision risk and taking avoidance measures.
5. The system according to claim 4, wherein:
the navigation module comprises an inertial navigation unit and a visual navigation unit;
the inertial navigation unit is used for providing real-time monitoring and estimation of the unmanned aerial vehicle motion;
the visual navigation unit utilizes a visual sensor to perform landmark recognition and navigation;
the positioning module comprises a GPS unit and an environment sensing unit;
the GPS unit is used for providing global positioning information;
the environment sensing unit improves positioning accuracy through external sensors or map data.
6. The system according to claim 5, wherein:
the environment adaptation module comprises a meteorological sensor and an environment map unit;
the weather sensor is used for detecting weather conditions and adjusting a flight plan;
the environment map unit is used for constructing and updating a flight environment map and supporting flight decisions.
7. The system according to claim 6, wherein:
the safety module comprises a flight control unit and a protection unit;
the flight control unit is used for monitoring and controlling the flight, and comprises emergency response to abnormal conditions;
the protection unit is used for preventing potential physical attacks or interference.
8. The system according to claim 7, wherein:
the data processing module comprises a data processing unit and a machine learning unit;
the data processing unit is used for processing the sensor data and carrying out real-time data analysis;
the machine learning unit is used for real-time decision making, learning and optimizing the flight strategy.
9. The system according to claim 8, wherein:
the real-time decision module comprises a decision unit and a path planning unit;
the decision unit makes real-time decisions based on the sensor data and task requirements;
and the path planning unit dynamically adjusts the path of the unmanned aerial vehicle according to the task and the environment.
10. The method of any one of claims 1-9, wherein:
s1, starting communication, ensuring that unmanned aerial vehicles use compatible communication protocols, processing handshake and authentication processes of the communication protocols, ensuring safety of communication in a cluster, monitoring communication quality in real time, adjusting communication frequency and protocols according to requirements, managing network topology in the cluster, ensuring smooth communication among all unmanned aerial vehicles, processing external communication, dynamically adjusting communication routes, adapting to changes of the network topology, and carrying out unmanned aerial vehicle cluster flight tasks;
s2, periodically updating cluster states including positions, speeds and task completion conditions in the flight process of the unmanned aerial vehicle, making a strategy for maintaining cooperative flight according to the cluster states, avoiding collision and optimizing path planning, and supporting the treatment of abnormal conditions, such as replacement of the unmanned aerial vehicle with a failure or a connection failure;
s3, receiving task requirements from a task control center in the flight process of the unmanned aerial vehicle, dynamically distributing tasks according to the task types, priorities and the current state of the unmanned aerial vehicle, planning task execution paths, and considering factors such as obstacle avoidance, energy efficiency and the like;
s4, collecting data from various sensors in the flight process of the unmanned aerial vehicle, wherein the data are provided by a camera, a radar and a laser radar, carrying out space-time fusion on the data, establishing overall perception on the surrounding environment of the unmanned aerial vehicle, carrying out collision prediction by using the fused sensor data, identifying potential collision risks, formulating an avoidance strategy, and updating the avoidance strategy in real time to adapt to environmental changes, wherein the avoidance strategy comprises adjustment of flight height, speed or path;
s5, providing real-time unmanned aerial vehicle motion state based on an inertial sensor in the unmanned aerial vehicle flight process, fusing other positioning data, improving navigation accuracy, utilizing a camera and an image processing algorithm to perform landmark recognition and relative positioning, providing global positioning information under the condition of no GPS signal, providing high-precision positioning information as a reference of a navigation system when an open area is opened, correcting GPS positioning by using an external sensor or map data, improving positioning accuracy, updating a flight environment map, and supporting path planning and navigation;
s6, detecting wind speed, temperature and humidity meteorological conditions in the flight process of the unmanned aerial vehicle, adjusting a flight plan according to meteorological data, ensuring safety and smooth completion of tasks, constructing an environment map, identifying terrain features, obstacles and task targets, and updating the map in real time according to environmental changes so as to support path planning and decision making;
s7, monitoring the state of the unmanned aerial vehicle in real time, including the battery power, the temperature and the sensor state, taking emergency measures such as returning or safe landing for abnormal conditions, and taking corresponding measures to protect a communication and navigation system and ensure the stability and reliability of the system when detecting potential physical attacks or interferences such as signal interference or electromagnetic attack;
s8, receiving, storing and processing sensor data in the flight process of the unmanned aerial vehicle, running a real-time data analysis algorithm, extracting useful information, learning historical data by using a machine learning algorithm, optimizing a flight strategy, and making a real-time decision to adapt to environmental changes;
s9, making real-time decisions according to unmanned aerial vehicle flight task requirements, environmental changes and sensor data, and adjusting flight plans, path plans and task execution strategies to optimize system performance;
s10, planning a path of the unmanned aerial vehicle according to the flight task requirements, the environment map and the flight state of the unmanned aerial vehicle, adopting a dynamic planning or model-based method, considering obstacle avoidance, optimizing the flight time and minimizing the energy consumption factors, and ensuring the safety and the high efficiency of the path.
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