A method and system for dynamically adjusting the trajectory of unmanned aerial vehicles (UAVs) in a safe zone

Abstract

This invention relates to the field of multi-UAV trajectory planning technology, and discloses a method and system for dynamically adjusting the safe zone for UAV trajectory planning. The method includes: calibrating key dynamic parameters of the UAV; acquiring the instantaneous velocity of the UAV in real time; calculating a dynamic safe radius matching the current velocity based on the key dynamic parameters and the instantaneous velocity; expanding obstacles based on the dynamic safe radius and constructing safety constraints based on the expansion result; solving and optimizing the trajectory under the safety constraints to obtain the UAV trajectory planning result. This method quantifies the UAV's inertia, system delay, and braking capability into a dynamic safe radius, and uses this as a hard constraint to generate the trajectory, thereby maximizing the UAV's operational performance while ensuring physical safety.

Description

Technical Field

[0001] This invention relates to the field of multi-UAV trajectory planning technology, and in particular to a UAV trajectory planning method and system for dynamically adjusting the safety zone. Background Technology

[0002] In recent years, drones have been increasingly widely used in logistics, agricultural and forestry protection, and disaster relief. Ensuring the safe and efficient collaborative operation of multiple drones in the same space, amidst increasingly complex mission requirements, has become a core technological challenge in this field. To improve drone operational efficiency, such as shortening delivery times or increasing search and rescue coverage in disaster areas, practical applications urgently demand high-speed flight capabilities and operational efficiency from drones. However, the physical inertia and system delays of drones are not negligible at high speeds, significantly increasing the difficulty of collision avoidance during high-speed flight.

[0003] Existing UAV trajectory planning methods generally employ a fixed safety zone model. This zone is typically determined based on the UAV's physical dimensions (i.e., geometric radius), and sometimes an additional fixed safety margin is added to handle various uncertainties. However, because this method ignores the UAV's real-time dynamic state, it leads to static limitations in setting the safety boundary. Furthermore, the large fixed margin set to ensure safe high-speed flight can make the UAV overly conservative at low speeds, significantly reducing mission flexibility; conversely, using only a small physical radius at high speeds increases the risk of collisions due to insufficient braking distance. Therefore, a new collision avoidance model is needed that can break free from the dependence on a fixed radius and intelligently link the safety margin to the UAV's real-time dynamic state, thereby maximizing the operational efficiency of multiple UAVs while ensuring physical safety. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for dynamically adjusting the safety zone in UAV trajectory planning. Existing technologies generally rely on a fixed safety radius for collision avoidance. This fixed radius cannot guarantee sufficient braking distance at high speeds, posing a physical collision hazard; while at low speeds, it is overly conservative, reducing the UAV's operational efficiency and flexibility when navigating narrow spaces. This invention aims to solve this problem by introducing an adaptive safety margin algorithm based on a physical dynamics model, enabling the UAV to maintain an optimal operating state that is both safe and efficient within the permissible flight speed range. This method quantifies the UAV's inertia, system delay, and braking capability into a dynamic safety radius, using this as a hard constraint to generate the trajectory, thereby maximizing the UAV's operational performance while ensuring physical safety.

[0005] On the one hand, a method for dynamically adjusting the trajectory planning of unmanned aerial vehicles (UAVs) within a safe area is provided, including:

[0006] Calibrate the key dynamic parameters of the UAV;

[0007] Real-time acquisition of the drone's instantaneous speed;

[0008] Based on the key dynamic parameters and the instantaneous velocity, calculate the dynamic safety radius that matches the current velocity;

[0009] The obstacle is expanded based on the dynamic safety radius, and safety constraints are constructed based on the expansion results.

[0010] The motion trajectory is solved and optimized under the aforementioned safety constraints to obtain the UAV trajectory planning result.

[0011] On the other hand, a drone trajectory planning system for dynamically adjusting a safe area is provided, including:

[0012] The calibration module is configured to calibrate the key dynamic parameters of the UAV.

[0013] The acquisition module is configured to acquire the instantaneous speed of the drone in real time.

[0014] The calculation module is configured to: calculate a dynamic safety radius that matches the current velocity based on the key dynamic parameters and the instantaneous velocity;

[0015] The processing module is configured to: expand the obstacle based on the dynamic safety radius, and construct safety constraints based on the expansion result;

[0016] The output module is configured to solve and optimize the motion trajectory under the security constraints to obtain the UAV trajectory planning result.

[0017] The above technical solution has the following advantages or beneficial effects:

[0018] This invention innovatively incorporates the real-time speed and braking physics model of a UAV into the definition of the safety radius, replacing the traditional fixed geometric radius with a dynamic safety radius. This design significantly improves the system's reliability and efficiency during operation. Based on the reliable safety guarantee of the physics model, the UAV can safely unleash its full performance in open areas, thereby shortening mission time and improving overall operational efficiency. At the same time, this invention avoids blindly being overly conservative. In low-speed or congested environments, the dynamic safety margin... It automatically shrinks to near zero, which allows drones to safely pass through narrow spaces that are incorrectly judged as impassable by fixed large margin methods. This significantly improves the system's flexibility and effectively reduces the risk of deadlock caused by excessive conservatism. Attached Figure Description

[0019] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0020] Figure 1 This is a schematic diagram of the overall process of the obstacle avoidance method for speed adaptation of multiple UAVs proposed in this invention;

[0021] Figure 2 A schematic diagram of the obstacle environment constructed for this invention;

[0022] Figure 3 The image shows the path planning results of multiple UAVs without the speed adaptive obstacle avoidance method provided by the present invention.

[0023] Figure 4 The diagram shows the path planning results of multiple UAVs using the speed-adaptive obstacle avoidance method provided by this invention. Detailed Implementation

[0024] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0025] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0026] All data acquisition in this embodiment is carried out in accordance with laws and regulations and with user consent, and the data is used legally.

[0027] Example 1

[0028] like Figure 1 As shown, a method for planning the trajectory of a drone that dynamically adjusts the safe area includes:

[0029] S101: Calibrate key dynamic parameters of the UAV;

[0030] S102: Real-time acquisition of the drone's instantaneous speed;

[0031] S103: Based on the key dynamic parameters and the instantaneous velocity, calculate the dynamic safety radius that matches the current velocity;

[0032] S104: Expand the obstacle based on the dynamic safety radius, and construct safety constraints based on the expansion result;

[0033] S105: Solve and optimize the motion trajectory under the stated safety constraints to obtain the UAV trajectory planning result.

[0034] Assume it exists One drone, each drone trajectory In a finite time domain Internal planning For the current planning cycle, For the planning time domain, the trajectory is represented as a piecewise polynomial, for example... part Bernstein polynomials of order:

[0035] ;

[0036] in, It is a drone In the The first segment of the trajectory One control point, These are Bernstein basis functions. The goal of the planner is to find a set of optimal control points. The core of this invention lies in achieving safe and efficient planning by dynamically adjusting the boundaries of safety constraints. This represents the normalized time parameter, and the specific calculation formula is as follows: , It is the first The drone in The starting time of the segment trajectory, It is the first The drone in The termination time of the segment trajectory, It represents the three-dimensional real number space.

[0037] Furthermore, S101: calibrating the key dynamic parameters of the UAV, wherein the key dynamic parameters include: the basic physical radius of the UAV, the maximum physical deceleration of the UAV, and the total reaction time of the UAV.

[0038] The basic physical radius of a drone The radius of the non-reducible geometrical envelope sphere of the drone is used in this experiment, which employs a micro-drone swarm experimental platform (Crazyswarm). The basic physical radius of the drone is... rice.

[0039] Maximum physical deceleration of drones This is the average deceleration that the drone can achieve at maximum braking power. In flight testing, this is measured by the maximum speed... Perform an emergency stop and measure the stopping distance. To identify them, their relationship can be approximated as:

[0040] ;

[0041] Total reaction time of drones It is a composite delay, encompassing the total time from state awareness, data transmission, planning calculation to actuator response. Its components are:

[0042] ;

[0043] in, Indicates perceptual delay, Indicates data transmission delay. Indicates the trajectory calculation delay. This indicates a delay in the actuator's response.

[0044] The total reaction time was calibrated experimentally. s.

[0045] Understandably, the first step is to identify, acquire, and store the three core physical parameters of the drone through system identification: the drone's basic physical radius. The maximum physical deceleration of the drone and the total system response time These parameters serve as the physical basis for subsequent dynamic calculations.

[0046] Furthermore, in step S102: the instantaneous speed of the drone is acquired in real time, and the instantaneous speed is collected by the drone's motion capture system.

[0047] Real-time acquisition of the drone's instantaneous speed in each online planning cycle Real-time execution. This is achieved by acquiring data from a state estimator such as a motion capture system for the drone. Current dynamic state :

[0048] ;

[0049] in, For location, For speed, This is the acceleration vector.

[0050] The key input is the current instantaneous velocity. That is, the magnitude of the velocity vector:

[0051] .

[0052] in, Indicates that drones are in The three-dimensional velocity vector at time t, This represents the discrete time point of the current online planning cycle, where For time series indexing, Indicates along The velocity vector of the axis, Indicates along The velocity vector of the axis, Indicates along The velocity vector of the axis.

[0053] It should be understood that within each real-time online planning cycle, the motion capture system acquires high-precision pose information of the UAV in three-dimensional space in real time and calculates its current instantaneous velocity. This speed value is the key input that triggers the dynamic adjustment of the safety radius.

[0054] Further, in S103: based on the key dynamic parameters and the instantaneous velocity, calculate the dynamic safety radius that matches the current velocity; wherein, the dynamic safety radius is the sum of the first parameter and the second parameter, the first parameter is the product of the adjustment parameter and the basic physical radius of the UAV, and the second parameter is the dynamic safety margin.

[0055] For example, a speed-adaptive dynamic safety radius is calculated in real time. :

[0056] ;

[0057] in, Indicates the instantaneous speed of the drone. It is an adjustable parameter, with a value range of [value range missing]. Here, we take 0.93. It is the basic physical radius of the drone. It is a dynamic safety margin.

[0058] Furthermore, the dynamic safety margin is the sum of the third parameter and the fourth parameter. The third parameter represents the reaction distance of the UAV flying at its current speed within the set reaction time range; the fourth parameter represents the physical braking distance required for the UAV to brake with its maximum braking capacity.

[0059] Dynamic safety margin The calculation formula is:

[0060] ;

[0061] Among them, the first item This means that within the set reaction time range, the drone is operating at its current speed. Flight reaction distance; second item It is a classic kinematic formula, representing the maximum braking capacity of a drone. The physical braking distance required for braking.

[0062] Therefore, through instantaneous velocity Based on the calibrated parameters, a dynamic safety radius that adapts to speed is calculated in real time. . The drone's current speed was precisely quantified. The reaction distance necessary for a safe stop. Therefore, the safety margin is no longer a blind, fixed value, but is closely coupled with the real-time dynamic state.

[0063] Further, S104: Increasing the obstacle based on the dynamic safety radius, and constructing safety constraints based on the increase result, includes:

[0064] The expansion process for static obstacles and the construction of constraints are performed, as well as the expansion process for dynamic obstacles and the construction of constraints.

[0065] The expansion processing of static obstacles and the construction of constraints refers to:

[0066] When the drone's flight speed exceeds the set flight threshold of 0.5... At that time, the virtual boundary of the static obstacle expands outward, and the virtual expansion increment is... The calculation formula is as follows:

[0067] ;

[0068] in, This indicates that when the drone's flight speed is At that time, the dynamic safety radius of the drone;

[0069] When the drone's flight speed is determined by speed Decelerate to speed At that time, the virtual boundary of the static obstacle shrinks inward, and the virtual shrinkage amount is... The calculation formula is as follows:

[0070] ;

[0071] in, This indicates that when the drone's flight speed is At that time, the dynamic safety radius of the drone;

[0072] This indicates that when the drone's flight speed is The dynamic safety radius of the drone.

[0073] Because the drone trajectory is represented as Using the Bernstein polynomial property, the trajectory curve of the UAV is completely contained within the convex hull formed by its control points. This allows for the extraction of the set of trajectory control points for the UAV within the current planning period. With each control point Centered on, with By expanding a sphere to a radius of [radius], a set of expanded control spheres is obtained. Indicates the first There are several control points. Take the convex hull of the union of all inflated control spheres, construct a convex polyhedron S, and generate static obstacle constraints. , This refers to static environmental obstacles.

[0074] For example, flexible expansion and safety constraint construction of obstacles are performed based on dynamic safety radii. In this invention, obstacles in the environment are no longer treated as fixed geometries, but rather as a set of geometric shapes that change with the real-time speed of the drone. A dynamically expanding body that is in flux.

[0075] For static environmental obstacles The constructed obstacle environment, such as Figure 2 As shown, the calculated dynamic safety radius is used. The obstacle is subjected to real-time "virtual expansion".

[0076] When the drone's flight speed exceeds the set flight threshold of 0.5... hour, As the size increases significantly, the virtual boundary of the static obstacle expands outward, causing the remaining passable free space to narrow, thus forcing the generation of convex polyhedral channels representing the free space. It is necessary to stay away from physical obstacles and leave a long physical distance to ensure the buffer zone required for braking;

[0077] Conversely, when the drone's flight speed decreases, As the obstacle shrinks, the virtual expansion boundary of the obstacle recedes, giving the drone more freedom of movement.

[0078] To ensure absolute safety, convex polyhedral channels Arbitrary boundary plane and static obstacles Euclidean distance between geometric entities It is forced to accommodate dynamic expansion, that is, to satisfy the inequality. .

[0079] Furthermore, the expansion processing of dynamic obstacles and the construction of constraints refer to:

[0080] Obtain the dynamic safety radius of the current drone and neighboring drones;

[0081] Calculate the normal vector of the separating hyperplane used to separate the two UAV spaces. :

[0082] ;

[0083] in, Indicates the current drone The center position vector, Indicates adjacent drones The center position vector;

[0084] In the direction of the normal vector, the dynamic safety margin is calculated based on the dynamic safety radii of the current UAV and its neighboring UAVs. :

[0085] ;

[0086] in, Indicates drone With speed The corresponding dynamic safety radius during flight. Indicates drone With speed The corresponding dynamic safety radius during flight.

[0087] Based on dynamic safety margin Construct dynamic obstacle constraints:

[0088] .

[0089] For example, for dynamic interactive obstacles, i.e., other cooperative drones This method utilizes a relative expansion model to construct a separating hyperplane linear constraint between multiple UAVs. This relative expansion model is a collision avoidance model based on the co-evolution of the dynamics of both UAVs, achieved by acquiring real-time UAV data. and drones Current speed , And calculate their corresponding dynamic safety radii respectively. and In this process, neighboring drones are no longer considered as fixed-size spheres, but rather both interacting drones are considered as dynamic spheres that expand or contract with their speed.

[0090] When two drones are in a high-speed flight state with a relative motion trend, the dynamic safety radius of both increases and the repulsion domain of each other expands rapidly, forcing the planner to start planning avoidance paths or performing deceleration operations at a greater physical distance.

[0091] When a mission requires the two to perform close-range collaborative interweaving or convergence in a dense space, the UAV uses the dynamic safety radius as a hard spatial constraint. The trajectory optimizer calculates the UAV's desired speed and actively decelerates to reduce its safety radius, thus allowing the separating hyperplane to still exist at extremely close physical distances.

[0092] To implement this dynamic safety in the optimization problem, we first calculate the normal vector of the separating hyperplane used to separate the two machine spaces. :

[0093] ;

[0094] Then, the dynamic safety margin that must be reserved in the direction of this normal vector is calculated. Dynamic safety margin Defined as the sum of the current dynamic expansion radii of the two drones, i.e.

[0095] .

[0096] The linear inequality constraints thus constructed It can adjust the tightness of the constraint in real time according to the speed of both parties, achieving an adaptive balance between safety and efficiency.

[0097] It should be understood that flexible expansion and safety constraint construction of obstacles are performed based on dynamic safety radii. Obstacles in the environment are no longer treated as fixed geometries, but rather a dynamic safety radius is employed. The obstacles are treated as a set of objects that change with the drone's real-time speed. A dynamically expanding body that is in flux.

[0098] Further, step S105: Solving and optimizing the motion trajectory under the safety constraints to obtain the UAV trajectory planning result includes:

[0099] Construct the objective function and constraints;

[0100] The constraints include: dynamic constraints, continuity constraints, static obstacle constraints, and dynamic obstacle constraints; the dynamic constraints are used to ensure that the generated velocity and acceleration are within the physically feasible range; the continuity constraints are used to ensure that the trajectory connections are smooth transitions; the static obstacle constraints are used to ensure that the UAV can avoid obstacles; and the dynamic obstacle constraints are used to ensure that there is a safe distance between the UAVs.

[0101] Under the constraints, the objective function is solved to obtain the planned UAV trajectory.

[0102] It should be understood that the safety constraints, which dynamically change with velocity, are input as hard constraints into the quadratic programming QP solver. Under these constraints, a smooth trajectory is generated that satisfies both the dynamic constraints and the physical braking safety constraints.

[0103] For example, the motion trajectory is solved and optimized under the constraints. This step integrates all constraints into a quadratic programming (QP) problem for solution.

[0104] The trajectory optimizer in each planning cycle Solve the following objective function:

[0105] ;

[0106] in, As an index of trajectory smoothness, For tracking indicators of target points.

[0107] Indicates the current planning target: drones The trajectory control point vector, The control point vector representing the target location.

[0108] The solution process is subject to a series of linear constraints:

[0109] First, there are dynamic constraints:

[0110] ;

[0111] in Represents the dynamic constraint matrix. Indicates the first Control points of the segment trajectory This represents the dynamic threshold vector, which contains the maximum speed of the drone. and maximum acceleration The boundary values ​​ensure that the generated velocity and acceleration are within physically feasible limits;

[0112] Secondly, there is the continuity constraint:

[0113] ;

[0114] in Represents a continuous mapping matrix. Indicates the first Control points of the segment trajectory It represents a continuous state vector, ensuring a smooth transition at trajectory connections;

[0115] Secondly, it is based on the dynamic radius. Static obstacle constraints:

[0116] ;

[0117] in The boundary depends on Ensure that the control point is located within the safe flight corridor; Indicates the first The drone in The first segment of the trajectory One control point;

[0118] Finally, dynamic obstacle constraints:

[0119] ;

[0120] in Indicates the use of drones for separation With drones The unit normal vector of the separating hyperplane in space. Indicates the first The drone in The first segment of the trajectory One control point, Indicates the adjacent first The drone in The first segment of the trajectory One control point, Indicates based on the instantaneous speed of the two machines and The calculated dynamic safety margin is used to ensure a safe distance between machines using a separation hyperplane.

[0121] By solving this QP problem, the optimizer outputs a set of optimal control points. This generates a motion trajectory that ensures both smoothness and dynamic feasibility while strictly satisfying the dynamic safety hard constraints based on the physical braking model.

[0122] Finally, to verify the effectiveness of the velocity adaptive planning method based on the physical dynamics model proposed in this invention, this embodiment... Figure 2 A simulation comparison experiment was conducted in the obstacle environment shown. The experimental results are as follows: Figure 3 and Figure 4 As shown. Among them, Figure 3 The path planning results without employing the dynamic safety radius method of this invention are shown. Figure 4 The path planning results using the dynamic safety radius method of this invention are shown. It can be seen that the algorithm proposed in this invention significantly improves the trajectory smoothness and traffic efficiency in complex environments while ensuring physical safety.

[0123] Example 2

[0124] This embodiment provides a drone trajectory planning system for dynamically adjusting a safe area, including:

[0125] The calibration module is configured to calibrate the key dynamic parameters of the UAV.

[0126] The acquisition module is configured to acquire the instantaneous speed of the drone in real time.

[0127] The calculation module is configured to: calculate a dynamic safety radius that matches the current velocity based on the key dynamic parameters and the instantaneous velocity;

[0128] The processing module is configured to: expand the obstacle based on the dynamic safety radius, and construct safety constraints based on the expansion result;

[0129] The output module is configured to solve and optimize the motion trajectory under the security constraints to obtain the UAV trajectory planning result.

[0130] It should be noted that the calibration module, acquisition module, calculation module, processing module, and output module mentioned above correspond to steps S101 to S105 in Embodiment 1. The examples and application scenarios implemented by these modules and their corresponding steps are the same, but they are not limited to the content disclosed in Embodiment 1. It should be noted that these modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0131] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for dynamically adjusting the trajectory planning of a UAV within a safe zone, characterized in that, include: Calibrate the key dynamic parameters of the UAV; Real-time acquisition of the drone's instantaneous speed; Based on the key dynamic parameters and the instantaneous velocity, calculate the dynamic safety radius that matches the current velocity; The obstacle is expanded based on the dynamic safety radius, and safety constraints are constructed based on the expansion results. The obstacle is expanded based on the dynamic safety radius, and safety constraints are constructed based on the expansion result, including: The expansion processing and constraint construction of static obstacles and dynamic obstacles are performed. The expansion of static obstacles and the construction of constraints refer to: When the drone's flight speed exceeds the set flight threshold of 0.5... At that time, the virtual boundary of the static obstacle expands outward, and the virtual expansion increment is... The calculation formula is as follows: ； in, This indicates that when the drone's flight speed is At that time, the dynamic safety radius of the drone; It is the basic physical radius of the drone; When the drone's flight speed is determined by speed Decelerate to speed At that time, the virtual boundary of the static obstacle shrinks inward, and the virtual shrinkage amount is... The calculation formula is as follows: ； in, This indicates that when the drone's flight speed is At that time, the dynamic safety radius of the drone; This indicates that when the drone's flight speed is At that time, the dynamic safety radius of the drone; Utilizing the convex hull property of Bernstein polynomial trajectories, the UAV's trajectory curve is completely contained within the convex hull formed by its control points; the set of trajectory control points of the UAV within the current planning period is extracted. With each control point Centered on, with By expanding a sphere to a radius of [radius], a set of expanded control spheres is obtained. Indicates the first Find control points; take the convex hull of the union of all inflated control spheres, construct a convex polyhedron S, and generate static obstacle constraints. , Indicates static environmental obstacles; The expansion of dynamic obstacles and the construction of constraints refer to: Obtain the dynamic safety radius of the current drone and neighboring drones; Calculate the normal vector of the separating hyperplane used to separate the two UAV spaces. : ； in, Indicates the current drone The center position vector, Indicates adjacent drones The center position vector; In the direction of the normal vector, the dynamic safety margin is calculated based on the dynamic safety radii of the current UAV and its neighboring UAVs. : ； in, Indicates drone With speed The corresponding dynamic safety radius during flight. Indicates drone With speed The corresponding dynamic safety radius during flight; Based on dynamic safety margin Construct dynamic obstacle constraints: ； The motion trajectory is solved and optimized under the aforementioned safety constraints to obtain the UAV trajectory planning result.

2. The method for dynamically adjusting the trajectory of a UAV in a safe area as described in claim 1, characterized in that, The key dynamic parameters of the UAV are calibrated, including: the UAV's basic physical radius, the UAV's maximum physical deceleration, and the UAV's total reaction time.

3. The method for dynamically adjusting the trajectory of a UAV in a safe area as described in claim 1, characterized in that, Real-time acquisition of the drone's instantaneous speed is performed during each online planning cycle. Real-time execution; acquiring drone data from state estimators such as motion capture systems. Current dynamic state : ； in, For location, For speed, It is the acceleration vector; The key input is the current instantaneous velocity. That is, the magnitude of the velocity vector: ； in, Indicates that drones are in The three-dimensional velocity vector at time t, This represents the discrete time point of the current online planning cycle, where For time series indexing, Indicates along The velocity vector of the axis, Indicates along The velocity vector of the axis, Indicates along The velocity vector of the axis.

4. The method for dynamically adjusting the trajectory of a UAV in a safe area as described in claim 1, characterized in that, Based on the key dynamic parameters and the instantaneous velocity, a dynamic safety radius matching the current velocity is calculated; wherein, the dynamic safety radius is the sum of a first parameter and a second parameter, the first parameter being the product of the adjustment parameter and the basic physical radius of the UAV, and the second parameter being the dynamic safety margin.

5. The method for dynamically adjusting the trajectory of a UAV in a safe area as described in claim 4, characterized in that, The dynamic safety margin is the sum of the third parameter and the fourth parameter. The third parameter represents the reaction distance of the UAV flying at its current speed within the set reaction time range. The fourth parameter represents the physical braking distance required for the UAV to brake with its maximum braking capacity.

6. The method for dynamically adjusting the trajectory of a UAV in a safe area as described in claim 1, characterized in that, Solving and optimizing the motion trajectory under the aforementioned safety constraints yields the UAV trajectory planning results, including: Construct the objective function and constraints; The constraints include: dynamic constraints, continuity constraints, static obstacle constraints, and dynamic obstacle constraints; the dynamic constraints are used to ensure that the generated velocity and acceleration are within the physically feasible range; the continuity constraints are used to ensure that the trajectory connections are smooth transitions; the static obstacle constraints are used to ensure that the UAV can avoid obstacles; and the dynamic obstacle constraints are used to ensure that there is a safe distance between the UAVs. Under the constraints, the objective function is solved to obtain the planned UAV trajectory.

7. A drone trajectory planning system for dynamically adjusting a safe area, performing the method described in any one of claims 1-6, characterized in that, include: The calibration module is configured to calibrate the key dynamic parameters of the UAV. The acquisition module is configured to acquire the instantaneous speed of the drone in real time. The calculation module is configured to: calculate a dynamic safety radius that matches the current velocity based on the key dynamic parameters and the instantaneous velocity; The processing module is configured to: expand the obstacle based on the dynamic safety radius, and construct safety constraints based on the expansion result; The output module is configured to solve and optimize the motion trajectory under the security constraints to obtain the UAV trajectory planning result.

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