An AI-assisted integrated control system for UAV mission execution

Abstract

The present invention relates to the field of AI control technology. Specifically, it relates to an integrated control system for drone mission execution based on AI assistance. It includes a flight control unit responsible for attitude control, speed control, and position control of the drone, a perception and obstacle avoidance unit for adjusting the flight position according to the type of obstacles and performing real-time obstacle avoidance during flight, a trajectory planning unit for calculating the optimal flight trajectory based on the mission requirements and environmental information transmitted by the instruction transceiver unit, and dynamically adjusting the trajectory according to the updated flight position; an instruction transceiver unit for receiving flight instructions from the ground control station and executing corresponding flight tasks. This system can more accurately simulate the obstacle avoidance process. Especially when the obstacle is large, the urgency of avoiding collision and the required obstacle avoidance force will be stronger; at the same time, it enables the drone to more precisely avoid obstacles in a complex environment, improving the reliability and flexibility of obstacle avoidance.

Description

Technical Field

[0001] The present invention relates to the field of AI control technology, and more specifically, to a comprehensive control system for unmanned aerial vehicle (UAV) mission execution assisted by AI. Background Art

[0002] With the rapid development of UAV technology, its application scenarios have become increasingly widespread, and UAVs can be seen in many fields such as civilian logistics distribution, agricultural monitoring, and environmental detection. However, with the expansion of the application scope, the flight environment faced by UAVs has become more complex and changeable, which poses higher requirements for the safety and reliability of UAVs. Especially when performing diverse tasks in low-altitude integrated airspace, UAVs not only need to deal with fixed obstacles such as static buildings and trees, but also face challenges brought by other dynamic aircraft. Traditional obstacle avoidance algorithms usually assume that all obstacles have similar importance and ignore the differences in collision risks brought by obstacles of different sizes. When encountering larger obstacles, traditional obstacle avoidance strategies may not provide sufficient safety margins, leading to potential collision hazards. Moreover, the flight environment in the real world is often dynamically changing, especially the changes in wind speed and direction can have a significant impact on the flight trajectory of UAVs. Some existing obstacle avoidance systems may not fully consider the effects of these external factors, so that under strong wind conditions, UAVs may deviate from the predetermined flight path or even lose control. Therefore, it is necessary to design a comprehensive control system for UAV mission execution assisted by AI. Summary of the Invention

[0003] The purpose of the present invention is to provide a comprehensive control system for UAV mission execution assisted by AI to solve the problems in the above background art that traditional obstacle avoidance algorithms usually assume that all obstacles have similar importance, ignore the differences in collision risks brought by obstacles of different sizes, and do not fully consider the effects of external factors such as wind speed and direction, so that under strong wind conditions, UAVs may deviate from the predetermined flight path or even lose control.

[0004] To achieve the above object, the present invention aims to provide a comprehensive control system for UAV mission execution assisted by AI, including:

[0005] A flight control unit, which is responsible for the attitude control, speed control, and position control of the UAV, and adjusts flight parameters in real time to cope with different flight states and environmental conditions;

[0006] Among them, the flight control unit includes an attitude control module, a speed control module, and a position control module;

[0007] The perception and obstacle avoidance unit is used to perceive the surrounding environment in real time, analyze the environmental data for obstacle detection and classification, adjust the flight position according to the type of obstacle, and perform real-time obstacle avoidance during flight. Among them, the types of obstacles include static obstacles and dynamic obstacles. If it is a static obstacle, a static obstacle avoidance decision is triggered; if it is a dynamic obstacle, a dynamic obstacle avoidance decision is triggered. During the static obstacle avoidance decision-making process, the influencing factor of the size of the static obstacle is introduced for optimization, and during the dynamic obstacle avoidance decision-making process, the influencing factor of wind speed is introduced for optimization;

[0008] Among them, the perception and obstacle avoidance unit includes a detection and classification module, an obstacle avoidance module, and an updated data module;

[0009] The trajectory planning unit is used to calculate the optimal flight trajectory according to the task requirements and environmental information transmitted by the instruction transceiver unit, and dynamically adjust the trajectory according to the updated flight position;

[0010] The instruction transceiver unit is used to receive flight instructions from the ground control station, execute corresponding flight tasks, and feedback the real-time flight status and task path progress of the UAV to the ground control station.

[0011] As a further improvement of this technical solution, in the flight control unit:

[0012] The attitude control module is used to monitor and adjust the pitch angle, roll angle, and yaw angle of the UAV, and correct the attitude deviation caused by external disturbances in real time; the speed control module is used to control the speed of the UAV and adjust the speed according to the urgency of the task requirements; the position control module is used to calculate and adjust the current position of the UAV, dynamically adjust the flight path and target position based on the flight plan or real-time task requirements, and adjust the path according to environmental changes, so as to perform the tracking of the target position.

[0013] As a further improvement of this technical solution, in the perception and obstacle avoidance unit:

[0014] The detection and classification module is used to obtain the information of the surrounding environment of the UAV in real time through sensors, detect obstacles and obtain the position and size of the obstacles. If the position of the obstacle exceeds the safety threshold, it is judged whether the obstacle is a static obstacle or a dynamic obstacle through the motion trajectory;

[0015] The obstacle avoidance module is used to judge whether to execute the static obstacle avoidance strategy or the dynamic obstacle avoidance strategy according to the type of obstacle and the position and size of the obstacle obtained by the detection and classification module, so as to adjust the attitude, speed and position of the UAV to avoid obstacles;

[0016] The updated data module is used to calculate the flight parameters of the UAV after obstacle avoidance and feedback the flight parameter results to the flight control unit, so as to update the flight parameters of the UAV in real time.

[0017] As a further improvement of this technical solution, in the detection and classification module, obstacles are detected and the positions and sizes of the obstacles are obtained as follows:

[0018] Obstacle position detection:

[0019] ;

[0020] Among them, is the distance from the current position of the UAV to the obstacle; is the three-dimensional coordinates of the obstacle; is the current position of the UAV;

[0021] If , the distance between the UAV and the obstacle is less than the safe distance, and the obstacle avoidance module 22 activates the obstacle avoidance signal;

[0022] The size of the obstacle is detected by combining lidar, ultrasonic sensors, and cameras to obtain the size of the obstacle.

[0023] As a further improvement of this technical solution, in the detection and classification module, the obstacle is judged as a static obstacle or a dynamic obstacle by the movement trajectory. Specifically: if both the speed and acceleration are zero, it is judged as a static obstacle; if the speed is not zero and changes, and at the same time the acceleration is not zero and there is a change rule, it is judged as a dynamic obstacle.

[0024] As a further improvement of this technical solution, the static obstacle avoidance strategy in the obstacle avoidance module is based on the static total force as follows:

[0025] Target attraction:

[0026] ;

[0027] Among them, is the target attraction; is the attraction coefficient; is the position of the target static obstacle; is the current position of the UAV;

[0028] The obstacle avoidance force of a single static obstacle:

[0029] ;

[0030] Among them, is the obstacle avoidance force of a single static obstacle; is the repulsive force coefficient; is the distance between the UAV and the static obstacle; is the obstacle avoidance direction;

[0031] ;

[0032] wherein, is the total force on a single static obstacle;

[0033] Obstacle avoidance force of multiple static obstacles:

[0034] ;

[0035] wherein, is the obstacle avoidance force of multiple static obstacles; is the distance between the UAV and the th static obstacle; is the th obstacle avoidance direction of the static obstacle; is the number of static obstacles; ;

[0036] ;

[0037] wherein, is the total force on multiple static obstacles.

[0038] As a further improvement of this technical solution, in the perception and obstacle avoidance unit, the influence factor of the size of the static obstacle is introduced in the process of static obstacle avoidance decision for optimization. After optimization, it is specifically:

[0039] Influence of the size of the obstacle on the attraction force:

[0040] ;

[0041] wherein, is the attraction force of the static obstacle;

[0042] Influence of the size of the obstacle on the obstacle avoidance force of a single static obstacle:

[0043] ;

[0044] wherein, is the optimized obstacle avoidance force of a single static obstacle; is the influence coefficient of the obstacle size on the obstacle avoidance force; is the volume of the obstacle;

[0045] ;

[0046] wherein, is the total force on the optimized single static obstacle;

[0047] Effect of the size of the obstacle on the obstacle avoidance force of multiple static obstacles:

[0048] ;

[0049] wherein, is the obstacle avoidance force of the optimized multiple static obstacles; is the th volume of the obstacle;

[0050] ;

[0051] wherein, is the total force on the optimized multiple static obstacles.

[0052] As a further improvement of the present technical solution, the dynamic obstacle avoidance strategy in the obstacle avoidance module is based on the dynamic total force, specifically as follows:

[0053] Predict the position of the dynamic obstacle:

[0054] ;

[0055] wherein, is the position of the dynamic obstacle at time ; is the position of the dynamic obstacle at time ; is the velocity of the dynamic obstacle at time ; is the time step; is the acceleration of the dynamic obstacle at time ;

[0056] Calculate the distance between the UAV and the future position of the dynamic obstacle:

[0057] ;

[0058] ;

[0059] wherein, is the distance from the current position of the UAV to the future position of the dynamic obstacle;

[0060] Based on the obstacle avoidance force of the dynamic obstacle:

[0061] ;

[0062] wherein, is the obstacle avoidance force of the dynamic obstacle; is the unit vector from the UAV to the future position of the dynamic obstacle; is the adjustment coefficient of the dynamic obstacle avoidance force; is the predicted speed of the dynamic obstacle; is the current speed of the UAV;

[0063] Based on the total force on the dynamic obstacle:

[0064] ;

[0065] where, is the total force on the dynamic obstacle.

[0066] As a further improvement of this technical solution, in the perception and obstacle avoidance unit, the influence factor of wind speed is introduced for optimization during the dynamic obstacle avoidance decision-making process. After optimization, it is specifically:

[0067] The influence of wind speed on the attraction force:

[0068] ;

[0069] ;

[0070] where, is the optimized attraction force of the dynamic obstacle; is the wind speed vector; is the optimized wind speed vector; is the adjustment coefficient of the wind speed influence factor;

[0071] The influence of wind speed on the obstacle avoidance force:

[0072] ;

[0073] where, is the optimized obstacle avoidance force of the dynamic obstacle;

[0074] Then, based on the total force on the dynamic obstacle:

[0075] ;

[0076] where, is the optimized total force on the dynamic obstacle.

[0077] As a further improvement of this technical solution, the updated data module is specifically as follows:

[0078] Attitude adjustment:

[0079] ;

[0080] where, is the total torque required for attitude adjustment; is the gain coefficient of the guiding force; is the total force, ; is the unit vector of the force direction; is the gain coefficient of the gyro feedback; is the current attitude angular rate;

[0081] Speed adjustment:

[0082] ;

[0083] wherein, is the adjusted flight speed of the UAV; is the current speed of the UAV; is the speed gain coefficient; is the target attraction force, ; is the obstacle avoidance force, ; is the environmental coefficient;

[0084] Path adjustment:

[0085] ;

[0086] wherein, is the adjusted flight position; is the current flight position; is the unit vector of the target direction.

[0087] Compared with the prior art, the beneficial effects of the present invention:

[0088] 1. In the comprehensive control system for UAV mission execution based on AI assistance, by introducing the influence of the obstacle volume and wind speed during the obstacle avoidance process, the obstacle avoidance process can be more accurately simulated. Especially when the obstacle is large, the urgency to avoid collision and the required obstacle avoidance force will be stronger; at the same time, it enables the UAV to more accurately avoid obstacles in a complex environment, especially when the wind speed and direction are uncertain, improving the reliability and flexibility of obstacle avoidance.

[0089] 2. In the comprehensive control system for UAV mission execution based on AI assistance, the obstacle avoidance module adopts the most appropriate obstacle avoidance strategy according to the specific type of the obstacle, and the data update module continuously collects the latest position information and feeds it back to other system components, enabling the entire flight control system to instantly respond to environmental changes and dynamically adjust the flight plan, thereby improving the task execution efficiency and success rate of the UAV. It not only enhances the UAV's ability to handle emergencies but also significantly reduces the collision risk, laying a solid foundation for realizing more intelligent and reliable unmanned flight. Description of the Drawings

[0090] Figure 1 is the overall flow chart of the present invention;

[0091] The meanings of each label in the figure are as follows:

[0092] 1. Flight control unit; 11. Attitude control module; 12. Speed control module; 13. Position control module; 2. Sensing and obstacle avoidance unit; 21. Detection and classification module; 22. Obstacle avoidance module; 23. Update data module; 3. Trajectory planning unit; 4. Instruction transceiver unit. Detailed implementation manners

[0093] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention. Embodiment

[0094] Please refer to Figure 1 as shown, a comprehensive control system for UAV mission execution based on AI assistance is provided, including: a flight control unit 1, a sensing and obstacle avoidance unit 2, a trajectory planning unit 3, and an instruction transceiver unit 4;

[0095] The flight control unit 1 will transmit the flight parameters of the aircraft to the obstacle avoidance module 22 and the trajectory planning unit 3; the obstacle avoidance module 22 performs obstacle avoidance according to the flight data and transmits the data after obstacle avoidance completion to the update data module 23, and the update data module 23 will transmit the updated flight parameters in real time back to the flight control unit 1;

[0096] When a new destination is received from the ground station, the instruction transceiver unit 4 will transmit the relevant information from the starting point to the end point to the trajectory planning unit 3. After completing the path planning, the trajectory planning unit 3 will hand over the detailed flight plan (the specific position, speed, and attitude requirements that should be reached at each moment) to the flight control unit 1 for execution. During the actual flight process, due to factors such as wind speed changes and GPS signal fluctuations, deviations may occur. At this time, the trajectory planning unit 3 needs to continuously receive the updated flight position information and timely correct the original plan to ensure the smooth completion of the overall task; at the same time, the instruction transceiver unit 4 will continuously receive the flight status of the flight control unit 1. The specific steps are as follows:

[0097] The flight control unit 1 is responsible for the attitude control, speed control, and position control of the UAV, and adjusts the flight parameters (such as thrust, control input, etc.) in real time to cope with different flight states and environmental conditions;

[0098] Among them, the flight control unit 1 includes an attitude control module 11, a speed control module 12, and a position control module 13;

[0099] In the flight control unit 1:

[0100] The attitude control module 11 is used to monitor and adjust the pitch angle, roll angle, and yaw angle of the UAV, and to correct in real time the attitude deviation caused by external disturbances (such as wind speed, air flow, etc.), so as to maintain stable flight;

[0101] The speed control module 12 is used to control the speed of the UAV, including the forward speed, lateral speed, and vertical speed, and to adjust the speed according to the urgency of the mission requirements to ensure the efficiency and safety of flight; when adjusting the flight speed, the dynamic characteristics of the aircraft, environmental conditions (such as wind speed), and mission requirements are considered;

[0102] The position control module 13 is used to calculate and adjust the current position of the UAV to ensure that it accurately reaches the target position, dynamically adjusts the flight path and target position based on the flight plan or real-time mission requirements, and adjusts the path according to environmental changes (such as obstacles, wind speed, etc.), so as to perform target position tracking;

[0103] The perception and obstacle avoidance unit 2 is used to perceive the surrounding environment (obstacles, dynamic objects, terrain, etc.) in real time, and at the same time analyze the environmental data for obstacle detection and classification, and adjust the flight position according to the type of obstacle to perform real-time obstacle avoidance during flight. Among them, the types of obstacles include static obstacles and dynamic obstacles. If it is a static obstacle, a static obstacle avoidance decision is triggered; if it is a dynamic obstacle, a dynamic obstacle avoidance decision is triggered. The influence factor of the size of the static obstacle is introduced for optimization during the static obstacle avoidance decision-making process, and the influence factor of the wind speed is introduced for optimization during the dynamic obstacle avoidance decision-making process;

[0104] Among them, the perception and obstacle avoidance unit 2 includes a detection and classification module 21, an obstacle avoidance module 22, and an updated data module 23;

[0105] The detection and classification module 21 is used to obtain the information of the surrounding environment of the UAV in real time through sensors (such as lidar, ultrasonic, camera, etc.), detect obstacles and obtain the position and size of the obstacles. If the position of the obstacle exceeds the safety threshold, the obstacle is judged as a static obstacle or a dynamic obstacle through the motion trajectory;

[0106] In the detection and classification module 21, the obstacles are detected and the position and size of the obstacles are obtained as follows:

[0107] Obstacle position detection:

[0108] ;

[0109] Wherein, is the distance from the current position of the UAV to the obstacle; is the three-dimensional coordinates of the obstacle; is the current position of the UAV;

[0110] If , the distance between the UAV and the obstacle is less than the safe distance, and the obstacle avoidance module 22 activates the obstacle avoidance signal;

[0111] The size of the obstacle is detected by combining lidar, ultrasonic sensors, and cameras to obtain the size of the obstacle.

[0112] For long-distance and complex environments, lidar is used. Especially in outdoor complex environments, it can provide high-precision obstacle detection. For close-range obstacle detection, if the UAV mainly performs close-range obstacle avoidance, especially when flying at low altitude or in a small area, ultrasonic sensors are an ideal choice. For indoor flight or low-cost applications, cameras and computer vision can be used to obtain the size and position of the obstacle through image processing technology, which is especially suitable for indoor flight or cost-sensitive applications.

[0113] In the detection and classification module 21, the obstacle is judged as a static obstacle or a dynamic obstacle through the motion trajectory. Specifically: if both the speed and acceleration are zero, it is judged as a static obstacle; if the speed is not zero and changes occur, and at the same time the acceleration is not zero and there is a change rule, it is judged as a dynamic obstacle;

[0114] The obstacle avoidance module 22 is used to judge whether to execute the static obstacle avoidance strategy or the dynamic obstacle avoidance strategy according to the obstacle type, position, and size obtained by the detection and classification module 21, so as to adjust the attitude, speed, and position of the UAV to avoid the obstacle;

[0115] The static obstacle avoidance strategy in the obstacle avoidance module 22 is based on the static total force, specifically as follows:

[0116] Target attraction:

[0117] ;

[0118] Wherein, is the target attraction; is the attraction coefficient, which determines the intensity of the flight target; is the position of the target static obstacle; is the current position of the UAV;

[0119] Obstacle avoidance force for a single static obstacle:

[0120] ;

[0121] Among them, is the obstacle avoidance force for a single static obstacle; is the repulsive force coefficient, which controls the intensity of the obstacle avoidance reaction; is the distance between the UAV and the static obstacle; is the obstacle avoidance direction, representing the unit vector pointing from the UAV to the obstacle;

[0122] ;

[0123] Among them, is the total force on a single static obstacle;

[0124] Obstacle avoidance force for multiple static obstacles:

[0125] ;

[0126] Among them, is the obstacle avoidance force for multiple static obstacles; is the distance between the UAV and the th static obstacle; is the th obstacle avoidance direction of the static obstacle; is the number of static obstacles; ;

[0127] ;

[0128] Among them, is the total force on multiple static obstacles;

[0129] This mechanism ensures that even in the presence of multiple static obstacles, the UAV can reach the destination along the optimal path. At the same time, for the obstacle avoidance forces generated by single or multiple static obstacles, they follow the inverse square law, that is, as the distance increases, the obstacle avoidance force gradually decreases, which helps to avoid unnecessary large turns or decelerations and improves the flight efficiency;

[0130] According to the obstacle type, position and size information provided by the detection and classification module 21, intelligently select static or dynamic obstacle avoidance strategies, and accordingly adjust the attitude, speed and position of the UAV to ensure safe flight. Especially for static obstacles, a method based on the potential field theory is used to calculate the obstacle avoidance force. This method is not only simple and intuitive, but also can effectively simulate the physical laws of nature, making the behavior of the UAV more in line with human intuition, thus enhancing the reliability and safety of the system.

[0131] In the perception and obstacle avoidance unit 2, the influence factor of the size of the static obstacle is introduced and optimized during the static obstacle avoidance decision-making process. After optimization, it is specifically:

[0132] The influence of the size of the obstacle on the attraction force:

[0133] ;

[0134] Among them,

[0135] is the attraction force of the static obstacle; this attraction force is not affected by the size of the obstacle;

[0136] The influence of the size of the obstacle on the obstacle avoidance force of a single static obstacle:

[0137] ;

[0138] Among them, is the optimized obstacle avoidance force of a single static obstacle; is the influence coefficient of the obstacle size on the obstacle avoidance force; is the volume of the obstacle; the larger the volume, the stronger the obstacle avoidance force, even if the distance is far;

[0139] ;

[0140] Among them, is the total force on the optimized single static obstacle;

[0141] The influence of the size of the obstacle on the obstacle avoidance force of multiple static obstacles:

[0142] ;

[0143] Among them, is the optimized obstacle avoidance force of multiple static obstacles; is the th volume of the obstacle;

[0144] ;

[0145] Among them, is the total force on the optimized multiple static obstacles;

[0146] Existing technologies usually ignore the size of the obstacle and mainly focus on the distance and direction. In practical applications, even if a large obstacle is at a far distance, a stronger obstacle avoidance reaction may be required. By introducing the combination of the obstacle volume (or area) and the distance, the obstacle avoidance process can be more accurately simulated. Especially when the obstacle is large, the urgency of avoiding collision and the required obstacle avoidance force will be stronger;

[0147] By adjusting the coefficient of the size factor , it is possible to flexibly control the impact of obstacle size on the obstacle avoidance strategy. For different types of tasks (such as urban flight, forest flight, etc.), this coefficient can be adjusted according to environmental requirements, thus providing a more adaptable obstacle avoidance solution.

[0148] The dynamic obstacle avoidance strategy in the obstacle avoidance module 22 is based on the dynamic total force, as follows:

[0149] Predict the position of the dynamic obstacle:

[0150] ;

[0151] Among them, is the position of the dynamic obstacle at time ; is the position of the dynamic obstacle at time ; is the velocity of the dynamic obstacle at time ; is the time step; is the acceleration of the dynamic obstacle at time ; This predictive ability enables the UAV to plan an obstacle avoidance path in advance in a dynamic environment, avoiding the collision risk caused by suddenly emerging obstacles.

[0152] Calculate the distance between the UAV and the future position of the dynamic obstacle:

[0153] ;

[0154] ;

[0155] Among them, is the distance from the current position of the UAV to the future position of the dynamic obstacle; It helps to determine the most suitable obstacle avoidance solution, ensuring that the UAV can avoid obstacles while maintaining an efficient mission execution path. In addition, considering the motion characteristics in three-dimensional space, this method can adapt to changes at different altitude levels, enhancing the flexibility and adaptability of the system.

[0156] Obstacle avoidance force based on the dynamic obstacle:

[0157] ;

[0158] Among them, is the obstacle avoidance force of the dynamic obstacle; is the unit vector from the UAV to the future position of the dynamic obstacle, representing the obstacle avoidance direction; is the adjustment coefficient of the dynamic obstacle avoidance force, determining the contribution of the speed difference to the obstacle avoidance force; is the predicted speed of the dynamic obstacle; is the current speed of the drone; not only considering the distance factor, but also adding a speed difference term, enabling the drone to make faster and more accurate responses according to the relative speed between itself and the obstacle, thus improving the obstacle avoidance efficiency and safety.

[0159] Based on the total force on the dynamic obstacle:

[0160] ;

[0161] Among them, is the total force on the dynamic obstacle; integrating the obstacle avoidance logic into the overall path planning process enables the drone to maintain stability and continuity in a complex and changing environment, and can quickly make appropriate adjustments even in the face of unforeseen situations.

[0162] In the perception and obstacle avoidance unit 2, the influence factor of wind speed is introduced for optimization during the dynamic obstacle avoidance decision-making process. After optimization, it is specifically:

[0163] The influence of wind speed on the attraction force:

[0164] ;

[0165] ;

[0166] Among them, is the attraction force of the optimized dynamic obstacle; is the wind speed vector; is the optimized wind speed vector, considering the long-term influence of wind speed changes on the flight trajectory; is the adjustment coefficient of the wind speed influence factor, which can be adjusted according to environmental conditions; the optimized dynamic obstacle avoidance strategy will enable the drone to more sensitively adjust the obstacle avoidance strategy in an environment with large wind speed changes, thus reducing the risk of collision with obstacles.

[0167] The influence of wind speed on the obstacle avoidance force:

[0168] ;

[0169] Among them, is the obstacle avoidance force of the optimized dynamic obstacle;

[0170] is then based on the total force on the dynamic obstacle:

[0171] ;

[0172] Among them, is the total force on the optimized dynamic obstacle;

[0173] By introducing the influence of wind speed on obstacle avoidance for dynamic obstacles, the optimized dynamic obstacle avoidance strategy enables the UAV to more precisely avoid obstacles in complex environments, especially when the wind speed is uncertain. The optimization of wind speed not only affects the relative speed between the UAV and the obstacle but also can adjust the magnitude and direction of the obstacle avoidance force, improving the reliability and flexibility of obstacle avoidance.

[0174] The updated data module 23 is used to calculate the flight parameters of the UAV after obstacle avoidance and feedback the flight parameter results to the flight control unit 1, thereby updating the flight parameters of the UAV in real time;

[0175] The updated data module 23 is as follows:

[0176] Attitude adjustment:

[0177] ;

[0178] Among them, is the total torque required for attitude adjustment (used to adjust the pitch, roll, and yaw angles of the UAV); is the gain coefficient of the guiding force; is the total force, the resultant force of the UAV with the target and the obstacle calculated; ; is the unit vector of the force direction (from the UAV to the target or the obstacle avoidance direction); is the gain coefficient of the gyro feedback; is the current attitude angular rate, used to prevent overshoot and improve system stability;

[0179] Speed adjustment:

[0180] ;

[0181] Among them, is the adjusted flight speed of the UAV; is the current speed of the UAV; is the speed gain coefficient, used to adjust the adjustment amplitude of the flight speed; is the target attraction force, ; is the obstacle avoidance force, ; is the environmental coefficient, considering the influence of environmental factors (such as wind speed, air flow, etc.), and dynamically adjusting the flight speed with the change of the environment;

[0182] Path adjustment:

[0183] ;

[0184] Among them, is the adjusted flight position; is the current flight position; is the unit vector of the target direction; Considering the effects of target attraction and obstacle avoidance force comprehensively, the drone can move forward towards the predetermined target while avoiding obstacles. In addition, due to the introduction of the time step , this algorithm supports continuous time series prediction, so that it can better simulate the motion process in the real world and provide a smoother and more reasonable flight trajectory for the drone.

[0185] The trajectory planning unit 3 is used to calculate the optimal flight trajectory according to the task requirements and environmental information transmitted by the instruction transceiver unit 4, and dynamically adjust the trajectory according to the updated flight position;

[0186] The trajectory planning unit 3 supports the dynamic adjustment function. When the drone receives an update of the new flight position, it can immediately re-evaluate the existing trajectory and make corresponding modifications according to the latest information. This ability enables the drone to perform well even in unknown or semi-structured environments, significantly enhancing its autonomy and intelligence level. For example, in the event of unexpected situations such as the sudden emergence of an air traffic control area or a temporarily established no-fly zone, the drone can quickly adjust its flight path to avoid violations while minimizing the impact on the original plan.

[0187] The instruction transceiver unit 4 is used to receive flight instructions from the ground control station, execute the corresponding flight tasks, and feedback the real-time flight status and task path progress of the drone to the ground control station; the instruction transceiver unit 4 plays the role of a bridge connecting the ground control station and the drone. On the one hand, it receives flight instructions from the ground control station and ensures that these instructions are accurately conveyed to the drone so that the latter can execute tasks according to the predetermined plan; on the other hand, the instruction transceiver unit also needs to feedback the real-time flight status and task path progress of the drone to the ground control station, enabling the operator to keep track of the task execution at any time and intervene or adjust if necessary.

[0188] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification are only the preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed.

Claims

1. An AI-assisted integrated control system for UAV mission execution, characterized in that: include: A flight control unit (1), the flight control unit (1) being responsible for attitude control, speed control and position control of the UAV, and adjusting flight parameters in real time to cope with different flight states and environmental conditions; The flight control unit (1) comprises an attitude control module (11), a speed control module (12) and a position control module (13); A sensing obstacle avoidance unit (2), the sensing obstacle avoidance unit (2) being used to sense the surrounding environment in real time, and simultaneously analyzing environmental data to detect and classify obstacles, and adjusting the flight position according to the type of obstacle so as to avoid obstacles in real time during flight, wherein the types of obstacles include static obstacles and dynamic obstacles, and if it is a static obstacle, a static obstacle avoidance decision is triggered; if it is a dynamic obstacle, a dynamic obstacle avoidance decision is triggered, and in the process of static obstacle avoidance decision, an influencing factor of the size of the static obstacle is introduced for optimization, and in the process of dynamic obstacle avoidance decision, an influencing factor of the wind speed is introduced for optimization; The perception and obstacle avoidance unit (2) includes a detection and classification module (21), an obstacle avoidance module (22) and a data update module (23); A trajectory planning unit (3), the trajectory planning unit (3) being used to calculate an optimal flight trajectory according to mission requirements and environmental information transmitted by the command transceiver unit (4), and dynamically adjust the trajectory according to an updated flight position; A command transceiver unit (4), the command transceiver unit (4) being used to receive flight instructions from a ground control station, execute corresponding flight tasks, and feed back the real-time flight status and task path progress of the UAV to the ground control station; In the obstacle avoidance sensing unit (2), the influencing factor of the size of the static obstacle is introduced in the static obstacle avoidance decision process for optimization. After optimization, the specific results are: The effect of obstacle size on attraction: ； in, is the attraction of static obstacles; The effect of obstacle size on the avoidance of a single static obstacle: ； in, is the optimized obstacle avoidance force of a single static obstacle; is the influence coefficient of obstacle size on obstacle avoidance force; is the volume of the obstacle; The distance from the current position of the drone to the obstacle; ； in, is the total force of a single static obstacle after optimization; The effect of obstacle size on the avoidance of multiple static obstacles: ； in, The obstacle avoidance for multiple static obstacles is optimized; For the The volume of the obstacle; ； in, is the total force of multiple static obstacles after optimization; In the obstacle avoidance sensing unit (2), the influencing factor of wind speed is introduced to optimize the dynamic obstacle avoidance decision process, and the optimization is as follows: The effect of wind speed on attraction: ; ； in, The attraction of the optimized dynamic obstacle; is the wind speed vector; is the optimized wind speed vector; is the adjustment coefficient of wind speed influence factor; The influence of wind speed on obstacle avoidance: ； in, The obstacle avoidance capability for optimized dynamic obstacles; The total force based on the dynamic obstacle is: ； in, is the total force on the optimized dynamic obstacle.

2. The AI-assisted UAV task execution integrated control system according to claim 1 is characterized in that: The flight control unit (1) comprises: The attitude control module (11) is used to monitor and adjust the pitch angle, roll angle and yaw angle of the UAV, and to correct attitude deviations caused by external disturbances in real time; the speed control module (12) is used to control the speed of the UAV, and to adjust the speed according to the emergency situation required by the mission; the position control module (13) is used to calculate and adjust the current position of the UAV, dynamically adjust the flight path and target position based on the flight plan or real-time mission requirements, and adjust the path according to environmental changes, so as to track the target position.

3. The AI-assisted UAV task execution integrated control system according to claim 2 is characterized in that: In the obstacle avoidance sensing unit (2): The detection and classification module (21) is used to obtain information about the environment around the drone in real time through sensors, detect obstacles and obtain the position and size of the obstacles. If the position of the obstacle exceeds a safety threshold, it is determined whether the obstacle is a static obstacle or a dynamic obstacle through the motion trajectory. The obstacle avoidance module (22) is used to determine whether to execute a static obstacle avoidance strategy or a dynamic obstacle avoidance strategy based on the obstacle type and the location and size of the obstacle obtained by the detection and classification module (21), thereby adjusting the attitude, speed and position of the drone to avoid the obstacle; The data update module (23) is used to calculate the flight parameters of the UAV after obstacle avoidance, and feed back the flight parameter results to the flight control unit (1), thereby updating the flight parameters of the UAV in real time.

4. The AI-assisted UAV task execution integrated control system according to claim 3 is characterized in that: In the detection and classification module (21), obstacles are detected and the position and size of the obstacles are obtained, as follows: Obstacle position detection: ； in, The distance from the current position of the drone to the obstacle; is the three-dimensional coordinate of the obstacle; is the current position of the drone; if , the distance between the UAV and the obstacle is less than the safe distance, and the obstacle avoidance module (22) activates the obstacle avoidance signal; The size of the obstacle is detected by combining lidar, ultrasonic sensors and cameras to obtain the size of the obstacle.

5. The AI-assisted UAV task execution integrated control system according to claim 4 is characterized in that: In the detection and classification module (21), whether the obstacle is a static obstacle or a dynamic obstacle is determined by the motion trajectory. Specifically, if the speed and acceleration are both zero, it is determined to be a static obstacle; if the speed is not equal to zero and changes, and the acceleration is not zero and has a changing pattern, it is determined to be a dynamic obstacle.

6. The AI-assisted UAV task execution integrated control system according to claim 5 is characterized in that: The static obstacle avoidance strategy in the obstacle avoidance module (22) is based on the static total force, which is as follows: Target attraction: ； in, For target attraction; is the attraction coefficient; is the target static obstacle position; is the current position of the drone; Obstacle avoidance for a single static obstacle: ； in, is the obstacle avoidance force of a single static obstacle; is the repulsion coefficient; is the distance between the UAV and the static obstacle; For obstacle avoidance direction; ； in, is the total force on a single static obstacle; Obstacle avoidance for multiple static obstacles: ； in, Obstacle avoidance for multiple static obstacles; For drones and The distance to a static obstacle; For the The obstacle avoidance direction of a static obstacle; is the number of static obstacles; ; ； in, is the total force of multiple static obstacles.

7. The AI-assisted UAV task execution integrated control system according to claim 6 is characterized in that: The dynamic obstacle avoidance strategy in the obstacle avoidance module (22) is based on the dynamic total force, which is as follows: Predict the position of dynamic obstacles: ； in, For dynamic obstacles at time location; For dynamic obstacles at time location; For dynamic obstacles at time speed; is the time step; For dynamic obstacles at time acceleration; Calculate the distance between the drone and the future position of the dynamic obstacle: ； ； in, is the distance from the current position of the drone to the future position of the dynamic obstacle; Obstacle avoidance based on dynamic obstacles: ； in, is the obstacle avoidance force of dynamic obstacles; is the unit vector from the UAV to the future position of the dynamic obstacle; is the adjustment coefficient of the dynamic obstacle avoidance force; is the predicted speed of the dynamic obstacle; is the current speed of the drone; Total force based on dynamic obstacles: ； in, is the total force on the dynamic obstacle.

8. The AI-assisted UAV mission execution integrated control system according to claim 7 is characterized in that: The update data module (23) is specifically as follows: Posture Adjustment: ； in, The total torque required for attitude adjustment; is the gain coefficient of the guiding force; is the total force, ; is the unit vector of the force direction; is the gain factor of gyro feedback; is the current attitude angular rate; Speed ​​adjustment: ； in, Adjusted flight speed for drones; is the current speed of the drone; is the speed gain coefficient; For target attraction, ; To avoid obstacles, ; is the environmental factor; Path Adjustment: ； in, is the adjusted flight position; is the current flight position; is the unit vector in the target direction.

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