An unmanned aerial vehicle flight control system

Abstract

The application discloses an unmanned aerial vehicle flight control system, and relates to the technical field of unmanned aerial vehicles.The application transfers the decision and calculation of the aerial vehicle from complete dependence on the ground control station to the aerial vehicle itself, sets an edge computing unit on the aerial vehicle, the aerial vehicle can perceive the environment in real time, plan a path and make a decision, and no longer has excessive dependence on the real-time performance and stability of ground communication, and can continue autonomous flight and control in the case of communication interruption or delay, meanwhile, a 5G+ communication module is adopted, network slicing and optimization technology are adopted, the communication connection of the aerial vehicle is ensured to be always stable, and the aerial vehicle can adapt to changes in the communication environment, so that communication delay and fluctuation are reduced, and the reliability and real-time performance of communication are improved.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, specifically to a flight control system for an UAV. Background Technology

[0002] Flight control systems are generally used to monitor and manipulate aircraft and spacecraft. Their main objective is to ensure the safety and stability of the aircraft, enabling it to be controlled and navigated under various flight phases and conditions.

[0003] Generally, a flight control system includes a flight management system (FMS), which provides accurate flight data and navigation information through the Global Positioning System (GPS) and the Inertial Navigation System (INS). Pilots can input flight plans, including information such as routes, altitudes, and speeds, through the FMS, which automatically controls the aircraft to follow these plans. The flight control system consists of various control surfaces and actuators used to control the aircraft's attitude and motion. The environmental perception system uses sensors to monitor the surrounding environment, helping pilots make more informed decisions to ensure safe flight.

[0004] However, for unmanned aerial vehicles (UAVs), traditional flight control systems rely on ground control stations to transmit commands and receive aircraft status information in real time. This dependence means that the aircraft cannot continue autonomous flight in the event of communication interruption or delay, causing flight mission interruption and flight safety risks. At the same time, the mobile communication technologies used in traditional flight control systems have limitations in data transmission due to variable time delays, bandwidth, and unpredictable connectivity. For example, GPRS has a data transmission delay of 200ms at a typical bandwidth of 40kb / s, which means that the aircraft has already flown 5m-6m by the time the data arrives. Such delays during takeoff or landing can lead to serious flight accidents. Furthermore, if the aircraft is traveling at a speed of 100km / h, it is practically impossible to achieve beyond visual line of sight flight without feedback information. Therefore, there is an urgent need for a flight control system for UAVs that can perceive the environment in real time and continue autonomous flight in the event of communication interruption or delay. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides a flight control system for unmanned aerial vehicles, which solves the problem that existing technologies rely on ground control stations to transmit commands and receive aircraft status information in real time, while variable time delays, bandwidth, and unpredictable connectivity characteristics limit data transmission.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides a flight control system for an unmanned aerial vehicle, characterized in that it comprises:

[0009] The sensor module, including barometric pressure sensors, wind speed sensors, lidar sensors, and vision sensors, is used to acquire information about the aircraft's attitude, position, speed, and surrounding environment, providing real-time perception data.

[0010] Edge computing units perform real-time perception, decision-making, and planning to enable autonomous control of the aircraft;

[0011] The communication module communicates with the ground control station, receives mission instructions, and provides feedback on flight status.

[0012] The flight control module implements the aircraft's motion control based on the decision results of the edge computing unit and sensor data;

[0013] The distributed control module distributes the aircraft's control tasks across multiple nodes for redundant control and collective decision-making.

[0014] The present invention is further configured such that the sensor module includes:

[0015] An inertial measurement unit (IMU) is used to measure the acceleration and angular velocity of an aircraft for attitude estimation and dynamic control.

[0016] The Global Positioning System (GPS) provides aircraft with position and speed information for positioning and navigation.

[0017] The visual sensor, which uses a lidar sensor, perceives the surrounding environment in real time for obstacle avoidance and target recognition;

[0018] The sensor module acquires the following environmental information:

[0019] The aircraft's attitude information includes its pitch, roll, and yaw angles;

[0020] The aircraft's position and speed information are obtained through the Global Positioning System (GPS), which provides the aircraft's current latitude and longitude coordinates, altitude information, and its speed in the horizontal and vertical directions.

[0021] The three-dimensional structural information of the environment is obtained through lidar sensors, including ground elevation and the location and height of obstacles.

[0022] Obstacle detection information is obtained by using lidar to detect obstacles in the surrounding environment, including buildings, trees, and other aircraft.

[0023] Atmospheric and wind speed information is obtained through barometric pressure sensors and wind speed sensors.

[0024] The present invention is further configured such that the edge computing unit includes:

[0025] The flight state estimator estimates the aircraft's attitude, position, and velocity based on IMU and GPS data;

[0026] An environmental sensor uses a visual sensor to perceive the surrounding environment in real time and detect obstacles and targets.

[0027] The path planner plans the optimal flight path for the aircraft based on the target mission and environmental information.

[0028] The decision-maker makes real-time decisions based on flight status and path planning results;

[0029] The present invention is further configured such that the path planning and decision-making steps in the edge computing unit specifically include:

[0030] The attitude information of the aircraft, including pitch angle, is obtained through sensor data. The roll angle θ, yaw angle ψ, position, and velocity information are used to estimate the state, which is represented by the state vector S.

[0031] Based on the aircraft's current state estimate S, environmental perception data, and target task, the MPC algorithm is used for path planning. The MPC optimization objective is expressed as:

[0032]

[0033] S k+1 =f(S) k U k k = 0, 1, ..., N-1;

[0034] U k ∈μ, k=0,1...,N-1,

[0035] Among them, S k U represents the state of the spacecraft at time k. k L(S) represents the control input at time k, N represents the prediction time domain, and L(S) represents the control input at time k. k U k M(S) is the cost function for the state and control inputs. n ) is the cost function of the terminal state, f(S) k U k ) is the dynamic model of the aircraft, and μ is the set of constraints for the control input;

[0036] Based on the optimization results of the MPC algorithm, the next action of the aircraft is determined, and the decision result is represented as a decision vector D, where Δθ and Δψ represent the changes in the aircraft's attitude at the three angles, respectively, and Δv x Δv y Δv z These represent the changes in the three velocity components of the aircraft, respectively.

[0037] The present invention is further configured such that the communication module includes:

[0038] The 5G+ communication module establishes a communication connection with the ground control station;

[0039] The data encryption and security module ensures the security of communication data and prevents data from being illegally obtained or tampered with.

[0040] The communication module determines the aircraft's communication requirements, including bandwidth, latency, and reliability;

[0041] Based on communication requirements, the communication network is divided into different virtual slices;

[0042] Real-time monitoring of resource utilization and communication demand in the communication network, and dynamic adjustment of slice resources and priorities based on actual conditions;

[0043] Based on the aircraft's communication needs and network resource status, allocate appropriate bandwidth and priority to each slice;

[0044] Develop communication strategies for each slice, including bandwidth allocation, priority setting, and transmission queue scheduling;

[0045] The slice allocation formula in the communication strategy is as follows:

[0046] Let the communication requirements of the aircraft be D. req The available bandwidth in the communication network is B. avail The priority of slice i is P i Then the bandwidth allocated to slice i is:

[0047]

[0048] Among them B i Bandwidth is allocated to i, and N is the total number of slices;

[0049] Depending on changes in the communication environment, the transmission rate R trans Based on signal strength S signal and packet loss rate P loss Perform adaptive adjustments:

[0050] R trans =f(S) signalP loss );

[0051] Among them, the function f(·) is the algorithm of the adaptive transmission mechanism, and the adaptive scheduling algorithm and the adaptive routing algorithm are selected.

[0052] The present invention is further configured such that the flight control module includes:

[0053] The attitude controller controls the attitude stability of the aircraft based on the attitude information provided by the flight state estimator.

[0054] The position controller controls the aircraft's position and speed based on GPS data collected by the Global Positioning System;

[0055] The thruster controller controls the electric thrusters of the aircraft, providing thrust control.

[0056] The flight control module flies according to the preset flight path and mission. The specific flight route is planned as follows:

[0057] Flight missions are pre-defined based on the application scenarios and mission requirements of the aircraft.

[0058] Using a path planning algorithm, a suitable flight path is pre-planned based on the aircraft's current position and target mission, and the pre-planned flight path is divided into multiple sub-tasks;

[0059] The present invention is further configured such that the autonomous flight control in the flight control module specifically includes:

[0060] By employing PID control and path tracking control algorithms, the aircraft can autonomously fly according to a preset flight path and sub-tasks under the control of the edge computing unit.

[0061] Real-time monitoring of the aircraft's status information, including position, speed, and attitude;

[0062] Based on the preset flight path and sub-tasks, determine whether the aircraft has completed the current sub-task, and then decide whether to move to the next sub-task;

[0063] The autonomous flight control strategy is as follows: when communication is interrupted or delayed, the aircraft continues to execute the current sub-task based on the current status information and flight path planning.

[0064] Autonomous flight control also includes:

[0065] The aircraft is equipped with an edge computing unit, enabling it to perceive the environment in real time and execute autonomous flight algorithms;

[0066] To develop autonomous flight control algorithms and strategies for aircraft;

[0067] In autonomous flight control, the control command is determined based on the aircraft's state information (State) and the preset flight path (Path):

[0068] Control = h(State, Path);

[0069] Here, the function h(·) is the selected autonomous fracturing algorithm.

[0070] (III) Beneficial Effects

[0071] This invention provides a flight control system for an unmanned aerial vehicle. It has the following beneficial effects:

[0072] By shifting the aircraft's decision-making and computation from complete reliance on ground control stations to the aircraft itself, and by setting up edge computing units on the aircraft, the aircraft can perceive the environment in real time, perform path planning and decision-making, and no longer rely too heavily on the real-time performance and stability of ground communication. It can continue to fly and control autonomously even if communication is interrupted or delayed. At the same time, by adopting 5G+ communication modules and using network slicing and optimization technologies, the aircraft's communication connection is ensured to remain stable at all times and can adapt to changes in the communication environment, thereby reducing communication latency and fluctuations and improving the reliability and real-time performance of communication.

[0073] In terms of flight mode, the flight control module enables the aircraft to continue autonomous flight even if communication is interrupted or delayed. Under the control of the edge computing unit, the aircraft flies according to the preset flight path and mission, no longer relying on real-time commands from the ground control station, thereby maintaining stable flight and avoiding loss of control or accidents caused by communication problems.

[0074] The control system adopts distributed control, distributing the control tasks of the aircraft to multiple nodes. Each node is equipped with an edge computing unit and a communication module, enabling it to independently perform flight control and data interaction. Even if communication problems occur between some nodes, other nodes can still continue to control the aircraft, ensuring stable flight and mission completion.

[0075] In summary, the unmanned aerial vehicle flight control system provided by this invention can better cope with communication delays and unpredictable connectivity issues. Edge computing enables the aircraft to have stronger autonomous decision-making and control capabilities. Optimized wireless communication network provides more stable and efficient communication services. Autonomous flight mode ensures that the aircraft can continue to fly when communication is interrupted. The distributed control system enhances the robustness and reliability of the system.

[0076] This solves the problem that existing technology centers rely on ground control stations to transmit commands and receive aircraft status information in real time, while variable time delays, bandwidth, and unpredictable connectivity characteristics limit data transmission. Attached Figure Description

[0077] Figure 1 This is a framework diagram of the flight control system for the unmanned aerial vehicle of the present invention. Detailed Implementation

[0078] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0079] Example

[0080] Please see Figure 1 This invention provides a flight control system for an unmanned aerial vehicle, comprising:

[0081] The sensor module, including barometric pressure sensors, wind speed sensors, lidar sensors, and vision sensors, is used to acquire information about the aircraft's attitude, position, speed, and surrounding environment, providing real-time perception data.

[0082] An inertial measurement unit (IMU) is used to measure the acceleration and angular velocity of an aircraft for attitude estimation and dynamic control.

[0083] The Global Positioning System (GPS) provides aircraft with position and speed information for positioning and navigation.

[0084] The visual sensor, which uses a lidar sensor, perceives the surrounding environment in real time for obstacle avoidance and target recognition;

[0085] The sensor module acquires the following environmental information:

[0086] The aircraft's attitude information includes its pitch, roll, and yaw angles;

[0087] The aircraft's position and speed information are obtained through the Global Positioning System (GPS), which provides the aircraft's current latitude and longitude coordinates, altitude information, and its speed in the horizontal and vertical directions.

[0088] The three-dimensional structural information of the environment is obtained through lidar sensors, including ground elevation and the location and height of obstacles.

[0089] Obstacle detection information is obtained by using lidar to detect obstacles in the surrounding environment, including buildings, trees, and other aircraft.

[0090] Atmospheric and wind speed information is obtained through barometric pressure sensors and wind speed sensors.

[0091] Edge computing units perform real-time perception, decision-making, and planning to enable autonomous control of the aircraft;

[0092] The flight state estimator estimates the aircraft's attitude, position, and velocity based on IMU and GPS data;

[0093] An environmental sensor uses a visual sensor to perceive the surrounding environment in real time and detect obstacles and targets.

[0094] The path planner plans the optimal flight path for the aircraft based on the target mission and environmental information.

[0095] The decision-maker makes real-time decisions based on flight status and path planning results;

[0096] The specific steps involved in path planning and decision-making within an edge computing unit include:

[0097] The attitude information of the aircraft, including pitch angle, is obtained through sensor data. The roll angle θ, yaw angle ψ, position, and velocity information are used to estimate the state, which is represented by a state vector S.

[0098] Based on the aircraft's current state estimate S, environmental perception data, and target mission, the MPC algorithm (MPC is an optimization control algorithm that solves for the optimal path by optimizing the aircraft's trajectory within a finite time; the MPC algorithm predicts the state at several future time points at each moment and generates the optimal control strategy by optimizing the objective and constraints) is used for path planning. The MPC optimization objective is expressed as:

[0099]

[0100] S k+1 =f(S) k U k k = 0, 1, ..., N-1;

[0101] U k ∈μ, k=0,1...,N-1,

[0102] Among them, S k U represents the state of the spacecraft at time k. k L(S) represents the control input at time k, N represents the prediction time domain, and L(S) represents the control input at time k.k U k M(S) is the cost function for the state and control inputs. n ) is the cost function of the terminal state, f(S) k U k ) is the dynamic model of the aircraft, and μ is the set of constraints for the control input;

[0103] Based on the optimization results of the MPC algorithm, the next action of the aircraft is determined, and the decision result is represented as a decision vector D, where Δθ and Δψ represent the changes in the aircraft's attitude at the three angles, respectively, and Δv x Δv y Δv z These represent the changes in the three velocity components of the aircraft. Combined with the environmental perception information from the flight state estimator, path planning and decision-making are performed in real time in the edge computing unit, enabling the aircraft to make optimal decisions in a constantly changing environment and fly along the optimal path. The path planning and decision-making based on the MPC algorithm provides more advanced and intelligent flight control capabilities for unmanned aerial vehicles.

[0104] The communication module communicates with the ground control station, receives mission instructions, and provides feedback on flight status.

[0105] The 5G+ communication module provides high-bandwidth and low-latency communication services to establish communication connections with ground control stations.

[0106] Data transmission protocols ensure accurate data transmission and stable communication.

[0107] The data encryption and security module ensures the security of communication data and prevents data from being illegally obtained or tampered with.

[0108] The communication module determines the aircraft's communication requirements, including bandwidth, latency, and reliability;

[0109] Based on communication requirements, the communication network is divided into different virtual slices;

[0110] Real-time monitoring of resource utilization and communication demand in the communication network, and dynamic adjustment of slice resources and priorities based on actual conditions;

[0111] Based on the aircraft's communication needs and network resource status, allocate appropriate bandwidth and priority to each slice;

[0112] Develop communication strategies for each slice, including bandwidth allocation, priority setting, and transmission queue scheduling;

[0113] The slice allocation formula in the communication strategy is as follows:

[0114] Let the communication requirements of the aircraft be D. req The available bandwidth in the communication network is B. avail The priority of slice i is P i Then the bandwidth allocated to slice i is:

[0115]

[0116] Among them B i Bandwidth is allocated to i, and N is the total number of slices;

[0117] Depending on changes in the communication environment, the transmission rate R trans Based on signal strength S signal and packet loss rate P loss Perform adaptive adjustments:

[0118] R trans =f(S) signal P loss ),

[0119] Among them, the function f(·) is the algorithm of the adaptive transmission mechanism, and the adaptive scheduling algorithm and the adaptive routing algorithm are selected.

[0120] By developing communication strategies for each slice and providing customized services for the aircraft's communication needs, we ensure the stability and reliability of communication connections. We also dynamically adjust communication resources and strategies based on the aircraft's actual communication needs and environmental changes, providing efficient and stable communication support for the aircraft.

[0121] The specific steps for implementing the data transmission protocol include:

[0122] Data packetization involves dividing the data to be transmitted into smaller data packets.

[0123] After receiving the data packet, the receiver sends an acknowledgment signal to the sender. If the sender does not receive the acknowledgment signal, it will retransmit the data packet.

[0124] Add error detection and correction codes to detect and correct errors during data transmission; improve the reliability of data transmission.

[0125] The specific steps for implementing the data encryption and security module include:

[0126] The transmitted data is encrypted using the AES advanced encryption standard.

[0127] Use digital signatures to ensure data integrity and source authentication;

[0128] The Diffie-Hellman key exchange protocol is used to ensure that the keys are not eavesdropped on or tampered with during communication between the two parties.

[0129] The flight control module implements the aircraft's motion control based on the decision results of the edge computing unit and sensor data;

[0130] The attitude controller controls the attitude stability of the aircraft based on the attitude information provided by the flight state estimator.

[0131] The position controller controls the aircraft's position and speed based on GPS data collected by the Global Positioning System;

[0132] The thruster controller controls the electric thrusters of the aircraft, providing thrust control.

[0133] The flight control module flies according to the preset flight path and mission. The specific flight route is planned as follows:

[0134] Flight missions are pre-defined based on the application scenarios and mission requirements of the aircraft.

[0135] Using path planning algorithms, a suitable flight path is pre-planned based on the aircraft's current position and target mission;

[0136] The preset flight path is divided into multiple sub-tasks;

[0137] Autonomous flight control specifically includes:

[0138] By employing PID control and path tracking control algorithms, the aircraft can autonomously fly according to a preset flight path and sub-tasks under the control of the edge computing unit.

[0139] Real-time monitoring of the aircraft's status information, including position, speed, and attitude;

[0140] Based on the preset flight path and sub-tasks, determine whether the aircraft has completed the current sub-task, and then decide whether to move to the next sub-task;

[0141] The autonomous flight control strategy is as follows: when communication is interrupted or delayed, the aircraft continues to execute the current sub-task based on the current status information and flight path planning.

[0142] Autonomous flight control includes:

[0143] The aircraft is equipped with an edge computing unit, enabling it to perceive the environment in real time and execute autonomous flight algorithms;

[0144] To develop autonomous flight control algorithms and strategies for aircraft;

[0145] In autonomous flight control, the control command is determined based on the aircraft's state information (State) and the preset flight path (Path):

[0146] Control = h(State, Path),

[0147] Wherein, the function h(·) is the selected autonomous fracturing algorithm;

[0148] Autonomous flight mode enables aircraft to continue flying autonomously even when communication is interrupted or delayed. It flies according to a preset flight path and mission, no longer relying on real-time commands from the ground control station. Through the control of the edge computing unit, the aircraft can achieve autonomous flight and mission execution, enhancing its autonomy and robustness in complex environments.

[0149] The distributed control module distributes the aircraft's control tasks across multiple nodes for redundant control and collective decision-making.

[0150] Multiple control nodes distribute the aircraft's control tasks to multiple nodes, ensuring system redundancy.

[0151] A collective decision-maker enables multiple nodes to make decisions collectively, ensuring system consistency and stability.

[0152] The distributed control module includes a distributed control system that distributes the aircraft's control tasks across multiple nodes, each node being equipped with an edge computing unit and a communication module;

[0153] Specific steps for constructing a distributed control system:

[0154] The number and location of nodes in the distributed control system are determined based on the aircraft's control mission and communication range.

[0155] The control task of the aircraft is divided into multiple subtasks, and these subtasks are assigned to different nodes;

[0156] Design the communication topology between nodes.

[0157] In summary, in this application:

[0158] The unmanned aerial vehicle flight control system provided by this invention avoids latency and unpredictable connectivity problems in the system, specifically:

[0159] By shifting the aircraft's decision-making and computation from complete reliance on ground control stations to the aircraft itself, and by setting up edge computing units on the aircraft, the aircraft can perceive the environment in real time, perform path planning and decision-making, and no longer rely too heavily on the real-time performance and stability of ground communication. It can continue to fly and control autonomously even if communication is interrupted or delayed. At the same time, by adopting 5G+ communication modules and using network slicing and optimization technologies, the aircraft's communication connection is ensured to remain stable at all times and can adapt to changes in the communication environment, thereby reducing communication latency and fluctuations and improving the reliability and real-time performance of communication.

[0160] In terms of flight mode, the flight control module enables the aircraft to continue autonomous flight even if communication is interrupted or delayed. Under the control of the edge computing unit, the aircraft flies according to the preset flight path and mission, no longer relying on real-time commands from the ground control station, thereby maintaining stable flight and avoiding loss of control or accidents caused by communication problems.

[0161] The control system adopts distributed control, distributing the control tasks of the aircraft to multiple nodes. Each node is equipped with an edge computing unit and a communication module, enabling it to independently perform flight control and data interaction. Even if communication problems occur between some nodes, other nodes can still continue to control the aircraft, ensuring stable flight and mission completion.

[0162] In summary, the unmanned aerial vehicle flight control system provided by this invention can better cope with communication delays and unpredictable connectivity issues. Edge computing enables the aircraft to have stronger autonomous decision-making and control capabilities. Optimized wireless communication network provides more stable and efficient communication services. Autonomous flight mode ensures that the aircraft can continue to fly when communication is interrupted. The distributed control system enhances the robustness and reliability of the system.

[0163] In the description of the embodiments of the present invention, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A flight control system for an unmanned aerial vehicle, characterized in that, include: The sensor module, including barometric pressure sensors, wind speed sensors, lidar sensors, and vision sensors, is used to acquire information about the aircraft's attitude, position, speed, and surrounding environment, providing real-time perception data. Edge computing units perform real-time perception, decision-making, and planning to enable autonomous control of the aircraft; The communication module communicates with the ground control station, receives mission instructions, and provides feedback on flight status. The flight control module implements the aircraft's motion control based on the decision results of the edge computing unit and sensor data; The distributed control module distributes the aircraft's control tasks across multiple nodes for redundant control and collective decision-making. The specific steps for path planning and decision-making in the edge computing unit include: The attitude information of the aircraft, including pitch angle, is obtained through sensor data. Roll angle Yaw angle Position and velocity information, the state estimate is represented as a state vector S, where Based on the aircraft's current state estimate S, environmental perception data, and target mission, the MPC algorithm is used for path planning. The MPC optimization objective is expressed as: in, Representing the aircraft The state at any given moment, Represents the moment The control input, Represents the prediction time domain. It is the cost function of the state and control inputs. It is the cost function of the terminal state. It is a dynamic model of an aircraft. It is the set of constraints that control the input; Based on the optimization results of the MPC algorithm, the next action of the aircraft is determined, and the decision result is represented as a decision vector. ,in , These represent the changes in the aircraft's attitude angles. These represent the changes in the three velocity components of the aircraft.

2. The flight control system for an unmanned aerial vehicle according to claim 1, characterized in that, The sensor module includes: An inertial measurement unit (IMU) is used to measure the acceleration and angular velocity of an aircraft for attitude estimation and dynamic control. The Global Positioning System (GPS) provides aircraft with position and speed information for positioning and navigation. The visual sensor, which uses a lidar sensor, perceives the surrounding environment in real time for obstacle avoidance and target recognition; The sensor module acquires the following environmental information: The aircraft's attitude information includes its pitch, roll, and yaw angles; The aircraft's position and speed information are obtained through the Global Positioning System (GPS), which provides the aircraft's current latitude and longitude coordinates, altitude information, and its speed in the horizontal and vertical directions. The three-dimensional structural information of the environment is obtained through lidar sensors, including ground elevation and the location and height of obstacles. Obstacle detection information is obtained by using lidar to detect obstacles in the surrounding environment, including buildings, trees, and other aircraft. Atmospheric and wind speed information is obtained through barometric pressure and wind speed sensors.

3. The flight control system for an unmanned aerial vehicle according to claim 1, characterized in that, The edge computing unit includes: The flight state estimator estimates the aircraft's attitude, position, and velocity based on IMU and GPS data; An environmental sensor uses a visual sensor to perceive the surrounding environment in real time and detect obstacles and targets. The path planner plans the optimal flight path for the aircraft based on the target mission and environmental information. The decision-maker makes real-time decisions based on flight status and path planning results.

4. The flight control system for an unmanned aerial vehicle according to claim 1, characterized in that, The communication module includes: The 5G+ communication module establishes a communication connection with the ground control station; The data encryption and security module ensures the security of communication data and prevents data from being illegally obtained or tampered with. The communication module determines the aircraft's communication requirements, including bandwidth, latency, and reliability; Based on communication requirements, the communication network is divided into different virtual slices; Real-time monitoring of resource utilization and communication demand in the communication network, and dynamic adjustment of slice resources and priorities based on actual conditions; Based on the aircraft's communication needs and network resource status, allocate appropriate bandwidth and priority to each slice; Develop communication strategies for each slice, including bandwidth allocation, priority setting, and transmission queue scheduling; The slice allocation formula in the communication strategy is as follows: Assume the communication requirements of the aircraft are The available bandwidth in the communication network is ,slice The priority is Then slice Allocated bandwidth: ; in for allocated bandwidth This represents the total number of slices. Depending on changes in the communication environment, the transmission rate Based on signal strength and packet loss rate Perform adaptive adjustments: , Among them, the function For the adaptive transmission mechanism, adaptive scheduling algorithm and adaptive routing algorithm are selected.

5. The flight control system for an unmanned aerial vehicle according to claim 1, characterized in that, The flight control module includes: The attitude controller controls the attitude stability of the aircraft based on the attitude information provided by the flight state estimator. The position controller controls the aircraft's position and speed based on GPS data collected by the Global Positioning System; The thruster controller controls the electric thrusters of the aircraft, providing thrust control. The flight control module flies according to the preset flight path and mission. The specific flight route is planned as follows: Flight missions are pre-defined based on the application scenarios and mission requirements of the aircraft. Using a path planning algorithm, a suitable flight path is pre-planned based on the aircraft's current position and target mission, and the pre-planned flight path is divided into multiple sub-tasks.

6. The flight control system for an unmanned aerial vehicle according to claim 5, characterized in that, The autonomous flight control in the flight control module specifically includes: By employing PID control and path tracking control algorithms, the aircraft can autonomously fly according to a preset flight path and sub-tasks under the control of the edge computing unit. Real-time monitoring of the aircraft's status information, including position, speed, and attitude; Based on the preset flight path and sub-tasks, determine whether the aircraft has completed the current sub-task, and then decide whether to move to the next sub-task; The autonomous flight control strategy is as follows: when communication is interrupted or delayed, the aircraft continues to execute the current sub-task based on the current status information and flight path planning. Autonomous flight control also includes: The aircraft is equipped with an edge computing unit, enabling it to perceive the environment in real time and execute autonomous flight algorithms; To develop autonomous flight control algorithms and strategies for aircraft; In autonomous flight control, control commands Based on the aircraft's status information and the preset flight path Sure: , where the function The selected autonomous fractal algorithm.

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