Unmanned aerial vehicle dynamic obstacle avoidance method based on safe flight corridor

Abstract

The application discloses a kind of unmanned aerial vehicle dynamic obstacle avoidance methods based on safe flight corridor, comprising the following steps: constructing safe flight corridor model and potential field constraint model, and planning initial flight path for unmanned aerial vehicle;Control unmanned aerial vehicle along the initial flight path planned and real-time detection dynamic obstacle, if detecting obstacle enters within set distance threshold, then using the potential field constraint model calculates the desired acceleration of unmanned aerial vehicle and predicts the position of unmanned aerial vehicle and obstacle, then, verify whether the predicted position of unmanned aerial vehicle satisfies constraint condition, if not satisfied, corresponding increase corresponding potential field weight, until the predicted position of unmanned aerial vehicle satisfies the constraint condition;Whether unmanned aerial vehicle completes obstacle avoidance is monitored, if complete, control unmanned aerial vehicle returns to the initial flight path planned and continues to fly.The unmanned aerial vehicle dynamic obstacle avoidance method can effectively realize the real-time obstacle avoidance of sudden dynamic obstacle in safe flight corridor, ensure the flight safety of unmanned aerial vehicle.

Description

Technical Field

[0001] This invention relates to the field of UAV trajectory planning, and specifically proposes a dynamic obstacle avoidance method for UAVs based on a safe flight corridor. Background Technology

[0002] With the rapid development of drone technology, drones have been widely used in logistics, emergency rescue, environmental monitoring, and inspection. During actual flight, drones often need to autonomously complete path planning and obstacle avoidance tasks in complex environments, especially in urban areas and forests where obstacles are dense and dynamically changing. Safe and efficient obstacle avoidance has become one of the key technologies for autonomous drone flight. Existing drone obstacle avoidance methods mainly include static obstacle avoidance methods based on global path planning and dynamic obstacle avoidance methods based on local perception. Global path planning methods typically rely on prior environmental information and struggle to cope with sudden dynamic obstacles during flight; while local obstacle avoidance methods, although able to respond to environmental changes in real time, are prone to getting trapped in local optima in complex environments, even causing flight paths to deviate from safe zones, posing certain safety hazards. In recent years, safe flight corridors, as an effective spatial constraint, have been widely used in drone path planning and safe flight management. By pre-setting a spatial passage for the drone, safe flight corridors can effectively reduce the complexity of path planning and improve flight safety. However, in the existing technology, there is little research on efficient obstacle avoidance methods for sudden dynamic obstacles in safe flight corridors, and there is still a lack of an efficient solution that can combine the spatial constraints of safe corridors with dynamic obstacle avoidance methods.

[0003] Therefore, how to achieve efficient and real-time obstacle avoidance of sudden dynamic obstacles within a safe flight corridor, and ensure that drones always fly safely within a safe area, has become an urgent technical challenge to be solved. Summary of the Invention

[0004] In view of this, the present invention proposes a dynamic obstacle avoidance method for unmanned aerial vehicles based on a safe flight corridor, in order to solve the problems existing in the prior art.

[0005] The technical solution provided by this invention is: a dynamic obstacle avoidance method for unmanned aerial vehicles (UAVs) based on a safe flight corridor, comprising the following steps:

[0006] S1: Construct a safe flight corridor model and a potential field constraint model, and plan the initial flight path for the UAV. The potential field constraint model is as follows:

[0007] ;

[0008] In the formula, Represents the combined potential field. This represents the potential field constrained by the boundary of the safe flight corridor. Represents the repulsive potential field of a dynamic obstacle. This represents the attractive potential field at the target location. , , These represent the weights of the potential field constrained by the boundary of the safe flight corridor, the repulsive potential field of the dynamic obstacle, and the attractive potential field of the target position, respectively.

[0009] S2: Control the drone to fly along the planned initial flight path and detect dynamic obstacles in real time. If the distance between the drone and the dynamic obstacle is less than the threshold of the repulsive force of the dynamic obstacle, execute S3.

[0010] S3: Calculate the expected acceleration of the UAV using the potential field constraint model and predict the positions of the UAV and obstacles. Then, verify whether the predicted position of the UAV meets the boundary constraint conditions of the safe flight corridor and the safe distance constraint conditions with the obstacles. If not, increase the weight of the boundary constraint potential field of the safe flight corridor and / or the repulsive potential field of the dynamic obstacle until the predicted position of the UAV meets the constraint conditions and control the UAV to fly under the action of the comprehensive potential field.

[0011] S4: Monitor whether the drone has completed obstacle avoidance. If it has, control the drone to return to the planned initial flight path and continue flying.

[0012] Preferably, in S1,

[0013] ;

[0014] In the formula, This indicates the current location of the drone. To the center line of the corridor The shortest distance, The boundary potential coefficients are... The radius of the safe flight corridor;

[0015] ;

[0016] In the formula, Indicates the repulsive force coefficient of the obstacle. This indicates the threshold distance at which the repulsive force of a dynamic obstacle acts. Indicates drones and obstacles Dynamic distance;

[0017] ;

[0018] In the formula, For the attractive potential coefficient, and These indicate the locations of the drone and the target, respectively.

[0019] Further optimization, in S3, the desired acceleration of the drone. The calculation formula is as follows:

[0020] ;

[0021] in, .

[0022] Further optimization, in S3, the predicted position of the UAV is calculated using the following formula: In the formula, and These represent the predicted and current positions of the drone, respectively. Indicates the speed of the drone. This represents the expected acceleration of the drone;

[0023] obstacle The predicted location is calculated using the following formula: In the formula, and They represent obstacles. The predicted location and the current location, Indicates obstacles The speed.

[0024] Further optimization: In S3, if the predicted position of the UAV does not meet the boundary constraints of the safe flight corridor, the weight of the boundary constraint potential field of the safe flight corridor is increased; if the predicted position of the UAV does not meet the safe distance constraint from the obstacle, the weight of the repulsive potential field of the dynamic obstacle is increased; if the predicted position of the UAV does not meet both the boundary constraints of the safe flight corridor and the safe distance constraint from the obstacle, the weight of both the boundary constraint potential field of the safe flight corridor and the repulsive potential field of the dynamic obstacle is increased.

[0025] Further optimization is made if the predicted position of the UAV does not simultaneously meet the safety flight corridor boundary constraint and the safety distance constraint from the obstacle, the increase in weight of the dynamic obstacle repulsive potential field is greater than the increase in weight of the safety flight corridor boundary constraint potential field.

[0026] Further optimization, in S4, the method for controlling the initial flight path of the UAV's return planning is as follows:

[0027] Detect drones and any obstacles Do they all meet the requirements? ,in, For a safety margin, if satisfied, the potential field weights are readjusted and the UAV flies onto the initial flight path under the influence of the combined potential field after the weights are adjusted. The weight of the potential field attracting the target position in the combined potential field is calculated using the following formula:

[0028] ;

[0029] The weighting function of the potential field at the boundary of the safe flight corridor is calculated by the following formula:

[0030] ;

[0031] In the formula, = { Let i = 1, 2, ..., N, represent the distance from the drone to the nearest obstacle. , , , .

[0032] Further optimization involves setting the centerline of the safe flight corridor as the initial flight path in S4. The method for determining the return to the centerline of the safe flight corridor is as follows:

[0033] The system monitors in real time whether the drone's current position is close to the center line of the safe flight corridor and whether the drone's current speed direction is close to the desired speed direction. If so, the system determines that the drone has returned to the center line of the safe flight corridor.

[0034] The proposed UAV dynamic obstacle avoidance method based on a safe flight corridor can effectively achieve real-time obstacle avoidance of sudden dynamic obstacles within a safe flight corridor, ensuring the flight safety of the UAV. By combining the potential field method with the boundary constraints of the safe flight corridor, the problem of the UAV deviating from the safe area during obstacle avoidance is avoided. It solves the problems of local minima and insufficient spatial constraints in the traditional artificial potential field method. In addition, the parameters in this method are adaptively adjusted to adapt to complex and ever-changing environments, and it has strong robustness and practicality. Attached Figure Description

[0035] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments:

[0036] Figure 1 A flowchart of the UAV dynamic obstacle avoidance method based on a safe flight corridor provided by the present invention;

[0037] Figure 2 A schematic diagram illustrating the trajectory of a drone dynamically avoiding obstacles within a safe flight corridor;

[0038] Figure 3 This diagram illustrates the potential field at the boundary of the safety corridor, the gravitational potential field of the target, the repulsive potential field of dynamic obstacles, and the comprehensive potential field. Detailed Implementation

[0039] The present invention will be further explained below with reference to specific implementation schemes, but this explanation does not limit the scope of the invention.

[0040] like Figure 1 As shown, this invention provides a dynamic obstacle avoidance method for unmanned aerial vehicles (UAVs) based on a safe flight corridor, comprising the following steps:

[0041] S1: Construct a safe flight corridor model and a potential field constraint model, and plan the initial flight path for the UAV;

[0042] Among them, the radius of the safe flight corridor The design principles are as follows:

[0043] (1) ,in, This design principle, which takes the maximum diagonal length of the drone, ensures that the drone has sufficient maneuverability within the corridor.

[0044] (2) Corridor radius The recommended value range is 0.5 to 3 meters, and the specific value can be dynamically adjusted according to the task and environment.

[0045] Among them, the centerline of the safe flight corridor can be automatically generated by combining the environmental map with global path planning algorithms such as A* and RRT*, supporting various forms such as straight lines, curves, and segments. Spline interpolation is used for smooth fitting to obtain the parameterized expression C(s).

[0046] The potential field constraint model is as follows:

[0047] ;

[0048] In the formula, Represents the combined potential field. This represents the potential field constrained by the boundary of the safe flight corridor. Represents the repulsive potential field of a dynamic obstacle. This represents the attractive potential field at the target location. , , These represent the weights of the potential field constrained by the boundary of the safe flight corridor, the repulsive potential field of the dynamic obstacle, and the attractive potential field of the target position, respectively.

[0049] in, ;

[0050] In the formula, This indicates the current location of the drone. To the center line of the corridor The shortest distance, For the boundary potential field coefficients, the preferred values ​​are... The value range is 20~100. The larger the size, the stronger the regression force. The radius of the safe flight corridor, where the drone's current position is... It can be acquired in real time through multi-source fusion of GNSS, IMU, and visual odometry;

[0051] in, ;

[0052] In the formula, Represents the repulsive force coefficient of the obstacle, preferably. The value range is 5 to 30. The larger the size, the more sensitive the obstacle avoidance response. This represents the threshold distance at which the repulsive force from a dynamic obstacle acts, i.e., the minimum safe distance threshold between the drone and the obstacle, preferably... The value range is 1~3 m. >2 to 3 times the size of the largest drone Indicates drones and obstacles The dynamic distance is calculated using the following formula:

[0053]

[0054] In the formula, Indicates the drone at a certain time The position at that time Indicates obstacles The initial position, Indicates obstacles The speed, among which, obstacles The location can be detected by lidar and camera sensors installed on the drone, and the speed can be measured by millimeter-wave radar;

[0055] in, ;

[0056] In the formula, For the attractive potential coefficient, the preferred value is... The value range is 0.5 to 2. The larger the size, the stronger the attraction. and Indicate the positions of the drone and the target, respectively;

[0057] This step, by establishing clear safe flight boundaries and intelligent constraint mechanisms, can lay the foundation for the safe and efficient flight of UAVs in complex and dynamic environments, ensuring that UAVs always fly within protected airspace, while providing an efficient mission execution baseline trajectory.

[0058] S2: Control the drone to fly along the planned initial flight path and detect dynamic obstacles in real time. If the distance between the drone and the dynamic obstacle is less than the threshold distance of the repulsive force of the dynamic obstacle, the drone will be controlled. At that time, execute S3;

[0059] When the distance between the drone and the dynamic obstacle is less than the threshold of the repulsive force distance of the dynamic obstacle. When performing obstacle avoidance, it can improve obstacle avoidance efficiency and real-time performance;

[0060] S3: Calculate the desired acceleration of the UAV using the aforementioned potential field constraint model. The system predicts the positions of the drone and obstacles. Then, it verifies whether the predicted position of the drone satisfies the safe flight corridor boundary constraints and the safe distance constraints from obstacles. If not, it increases the weights of the safe flight corridor boundary constraint potential field and / or the dynamic obstacle repulsive potential field until the predicted position of the drone satisfies the constraints and the drone is controlled to fly under the influence of the combined potential field, thus achieving obstacle avoidance. The obstacle avoidance process is as follows: Figure 2 As shown;

[0061] Among them, the expected acceleration of drones The calculation formula is as follows:

[0062] ;

[0063] in, ;

[0064] like Figure 3 As shown, the potential field is low within the boundary of the safe flight corridor and high outside the boundary. Under the combined effect of the potential field, the UAV will be pushed away from the high potential energy region (obstacles, corridor boundary) and pulled towards the low potential energy region (target point). The gradient can be calculated using the finite difference method or the analytical method.

[0065] The predicted location of the drone can be calculated using the following formula: In the formula, and These represent the predicted and current positions of the drone, respectively. Indicates the speed of the drone. This represents the expected acceleration of the drone;

[0066] obstacle The predicted location can be calculated using the following formula: In the formula, and They represent obstacles. The predicted location and the current location, Indicates obstacles speed;

[0067] Where the predicted position of the UAV does not meet the boundary constraints of the safe flight corridor, i.e.: If R > 0, then increase the weight of the boundary constraint potential field of the large safe flight corridor (the increase is between 0.05 and 0.15) to bring it back into the safe flight corridor as soon as possible;

[0068] If the predicted position of the drone does not meet the safe distance constraint from the obstacle, that is: < This increases the weight of the repulsive potential field of the dynamic obstacle (by an increase of 0.1 to 0.3), causing it to move away from the obstacle.

[0069] If the predicted position of the UAV does not satisfy both of the above constraints, then the weights of the boundary constraint potential field of the safe flight corridor and the repulsive potential field of the dynamic obstacle are increased simultaneously. Preferably, the increase in the weight of the repulsive potential field of the dynamic obstacle is greater than the increase in the weight of the boundary constraint potential field of the safe flight corridor.

[0070] This step, through obstacle avoidance decision-making and adaptive weight adjustment, achieves a balance between safety and maneuverability, ensuring that the UAV can effectively avoid obstacles while remaining within a safe corridor during obstacle avoidance, thus establishing a reliable closed-loop control mechanism.

[0071] S4: Monitor whether the drone has completed obstacle avoidance. If it has, control the drone to return to the planned initial flight path and continue flying.

[0072] The method for controlling the initial flight path of the UAV's return planning is as follows:

[0073] Detect drones and any obstacles Do they all meet the requirements? ,in, For safety margin, a value of 0.5 meters was chosen, and the obstacle avoidance threshold was defined. The length of the transition interval between the target position and the fully safe state, if satisfied, indicates that the UAV has entered the safe zone. The potential field weights are then readjusted, and the UAV flies back to its initial flight path under the influence of the combined potential field. In the combined potential field, the weight of the potential field attracted by the target position is calculated by the following formula:

[0074] ;

[0075] The weighting function of the potential field at the boundary of the safe flight corridor is calculated by the following formula:

[0076] ;

[0077] In the formula, = { Let i = 1, 2, ..., N, represent the distance from the drone to the nearest obstacle. , , , The above values ​​were determined based on a large number of simulation experiments and flight test results. Among them, the setting of empirical parameter values ​​takes into account flight safety, trajectory smoothness and mission efficiency. While ensuring the safe obstacle avoidance of the UAV, it can achieve efficient path return and mission continuity, ensuring that the UAV can smoothly return to the predetermined route and continue to perform the original mission after completing obstacle avoidance.

[0078] Typically, the initial flight path is set to the centerline of the safe flight corridor;

[0079] The method for determining the centerline of the safe flight corridor is as follows:

[0080] As the drone gradually approaches the centerline of the safe flight corridor under the guidance of dynamically adjusted potential field weights, it monitors in real time the distance between its current position and the centerline, as well as whether its current velocity direction is close to the desired velocity direction. When the following conditions are met, the return to the centerline is considered complete, and the drone flies along the previously generated planned path:

[0081] ;

[0082] In the formula, The location threshold is preferably 0.3 meters. The velocity direction threshold is preferably set to 0.1. In the formula The point closest to the centerline of the safe flight corridor The tangent vector at that point, This represents the desired flight speed.

[0083] Specifically, by iteratively calculating and gradually approximating the optimal parameter s, a convergent solution is obtained, ensuring that the true closest point is found. The gradient descent method is used to solve the problem. :

[0084] ;

[0085] in, The learning rate is 0.1, and the iteration termination condition is... , When the iteration converges, the final... That is what we are looking for. .

[0086] Through simulation testing, the above methods were demonstrated that the drones could detect obstacles in real time and dynamically adjust their flight trajectories (with smooth flight trajectories) without deviating from the safe corridor, successfully avoiding obstacles and finally reaching the target point.

[0087] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0088] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0089] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0090] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0091] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0092] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A dynamic obstacle avoidance method for unmanned aerial vehicles (UAVs) based on a safe flight corridor, characterized in that, include: S1: Construct a safe flight corridor model and a potential field constraint model, and plan the initial flight path for the UAV. The potential field constraint model is as follows: ； In the formula, Represents the combined potential field. This represents the potential field constrained by the boundary of the safe flight corridor. Represents the repulsive potential field of a dynamic obstacle. This represents the attractive potential field at the target location. , , These represent the weights of the potential field constrained by the boundary of the safe flight corridor, the repulsive potential field of the dynamic obstacle, and the attractive potential field of the target position, respectively. ; In the formula, This indicates the current location of the drone. To the center line of the corridor The shortest distance, The boundary potential coefficients are... The radius of the safe flight corridor; S2: Control the drone to fly along the planned initial flight path and detect dynamic obstacles in real time. If the distance between the drone and the dynamic obstacle is less than the threshold of the repulsive force of the dynamic obstacle, execute S3. S3: Calculate the expected acceleration of the UAV using the potential field constraint model and predict the positions of the UAV and obstacles. Then, verify whether the predicted position of the UAV meets the boundary constraint conditions of the safe flight corridor and the safe distance constraint conditions with the obstacles. If not, increase the weight of the boundary constraint potential field of the safe flight corridor and / or the repulsive potential field of the dynamic obstacle until the predicted position of the UAV meets the constraint conditions and control the UAV to fly under the action of the comprehensive potential field. S4: Monitor whether the drone has completed obstacle avoidance. If it has, control the drone to return to the planned initial flight path and continue flying.

2. The UAV dynamic obstacle avoidance method based on a safe flight corridor according to claim 1, characterized in that, In S1, ； In the formula, Indicates the repulsive force coefficient of the obstacle. This indicates the threshold distance at which the repulsive force of a dynamic obstacle acts. Indicates drones and obstacles Dynamic distance; ； In the formula, For the attractive potential coefficient, and These indicate the locations of the drone and the target, respectively.

3. The UAV dynamic obstacle avoidance method based on a safe flight corridor according to claim 1, characterized in that, In S3, the expected acceleration of the drone The calculation formula is as follows: ； in, .

4. The UAV dynamic obstacle avoidance method based on a safe flight corridor according to claim 3, characterized in that, In S3, the predicted position of the UAV is calculated using the following formula: In the formula, and These represent the predicted and current positions of the drone, respectively. Indicates the speed of the drone. This represents the expected acceleration of the drone; obstacle The predicted location is calculated using the following formula: In the formula, and They represent obstacles. The predicted location and the current location, Indicates obstacles The speed.

5. The UAV dynamic obstacle avoidance method based on a safe flight corridor according to claim 3, characterized in that, In S3, if the predicted position of the UAV does not meet the boundary constraints of the safe flight corridor, the weight of the boundary constraint potential field of the safe flight corridor is increased; if the predicted position of the UAV does not meet the safe distance constraint from the obstacle, the weight of the dynamic obstacle repulsive potential field is increased; if the predicted position of the UAV does not meet both the boundary constraints of the safe flight corridor and the safe distance constraint from the obstacle, the weight of both the boundary constraint potential field of the safe flight corridor and the dynamic obstacle repulsive potential field is increased.

6. The UAV dynamic obstacle avoidance method based on a safe flight corridor according to claim 5, characterized in that, If the predicted position of the UAV does not simultaneously meet the safety flight corridor boundary constraints and the safe distance constraints from obstacles, the weight increase of the dynamic obstacle repulsive potential field is greater than the weight increase of the safety flight corridor boundary constraint potential field.

7. The UAV dynamic obstacle avoidance method based on a safe flight corridor according to claim 1, characterized in that, In S4, the method for controlling the initial flight path of the UAV's return planning is as follows: Detect drones and any obstacles Do they all meet the requirements? ,in, Indicates drones and obstacles Dynamic distance, , For a safety margin, if satisfied, the potential field weights are readjusted and the UAV flies onto the initial flight path under the influence of the combined potential field after the weights are adjusted. The weight of the potential field attracting the target position in the combined potential field is calculated using the following formula: ； The weighting function of the potential field at the boundary of the safe flight corridor is calculated by the following formula: ； In the formula, = { } represents the distance from the drone to the nearest obstacle. , , , .

8. The UAV dynamic obstacle avoidance method based on a safe flight corridor according to claim 7, characterized in that, In S4, the centerline of the safe flight corridor is set as the initial flight path. The method for determining the return to the centerline of the safe flight corridor is as follows: The system monitors in real time whether the drone's current position is close to the center line of the safe flight corridor and whether the drone's current speed direction is close to the desired speed direction. If so, the system determines that the drone has returned to the center line of the safe flight corridor.

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