Unmanned aerial vehicle traffic control method fused with dynamic obstacle avoidance

Abstract

The invention relates to an unmanned aerial vehicle traffic control method fused with dynamic obstacle avoidance, and relates to the field of unmanned aerial vehicle cooperative control. Static obstacle parameters are obtained, and an unmanned aerial vehicle control twin space is built in combination with the multiple dynamic potential energy field models; and when the multiple unmanned aerial vehicles are in flight operation, the unmanned aerial vehicles are utilized to control twin spaces, a deadlock detection mechanism and a deadlock solving mechanism are combined to execute dynamic regulation and control of an air route, and the positions of the unmanned aerial vehicles are iteratively updated until all the unmanned aerial vehicles reach respective target points. According to the invention, the problems of local minimum value, route conflict and deadlock of a multi-unmanned aerial vehicle system cannot be effectively solved by a traditional unmanned aerial vehicle control method can be solved; efficient sensing of the unmanned aerial vehicle can be realized through dynamic sensing range adjustment; the deadlock problem in flight can be solved through deadlock detection and a multi-dimensional solution mechanism; and the obstacle avoidance efficiency of the multiple unmanned aerial vehicles is optimized through priority dynamic allocation and a vertical separation strategy.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) coordinated control, and in particular to a UAV traffic control method integrating dynamic obstacle avoidance. Background Art

[0002] With the development of drone technology, multi-drone systems are increasingly used in military reconnaissance, disaster relief, logistics and distribution, etc. In the process of multi-drone collaborative operation, how to ensure that multiple drones can complete tasks safely and efficiently and avoid collisions with each other and obstacles in the environment is a technical problem that needs to be solved urgently.

[0003] At present, the commonly used UAV obstacle avoidance and path planning methods mainly include artificial potential field method, rapid expansion random tree method, A* algorithm, etc. Among them, the artificial potential field method is widely used in real-time path planning of UAVs due to its simplicity, intuitiveness, high computational efficiency, etc. However, the traditional artificial potential field method has some limitations when dealing with the problem of multi-UAV collaborative obstacle avoidance, which are mainly manifested in the following aspects: Fixed perception range: Traditional methods usually use a fixed perception range to detect obstacles and other drones. This method cannot be dynamically adjusted according to the movement state of the drone, resulting in insufficient response at high speeds and redundant calculations at low speeds.

[0004] Single obstacle avoidance strategy: Existing methods mostly avoid obstacles in the horizontal direction and lack the ability to use three-dimensional space for all-round obstacle avoidance, which limits the efficiency of obstacle avoidance.

[0005] Deadlock problem: In a multi-UAV system, when two or more UAVs are close to each other and their paths intersect, they are prone to fall into a deadlock state of mutual avoidance. Traditional methods lack an effective deadlock detection and resolution mechanism.

[0006] Insufficient coordination: Coordination between multiple drones usually relies on a simple repulsion model and lacks priority management and collaborative decision-making mechanisms, which reduces the overall efficiency of the system.

[0007] Therefore, there is an urgent need for a multi-UAV collaborative obstacle avoidance and deadlock solution that can solve the above problems, so as to improve the collaborative flight efficiency and safety of multi-UAV systems in complex environments. Summary of the invention

[0008] The present invention aims to solve the problems of local minimum, route conflict and deadlock of multi-UAV system that traditional UAV control methods cannot effectively solve, and provides a UAV traffic control method integrating dynamic obstacle avoidance to solve the problems.

[0009] The technical solution of the present invention to solve the above technical problems is as follows: The present invention provides a UAV traffic control method integrating dynamic obstacle avoidance, including: initializing the state parameters of multiple UAVs, and establishing a dynamic potential field centered on the UAVs to obtain multiple dynamic potential field models, where the dynamic potential field is used to form a dynamic sensing range; obtaining the static obstacle parameters of the target flight area, and building a UAV control twin space in combination with the multiple dynamic potential field models; when multiple UAVs are performing flight operations, using the UAV control twin space, combining a deadlock detection mechanism and a deadlock resolution mechanism, performing dynamic route regulation, and iteratively updating the positions of the UAVs until all UAVs reach their respective target points, where the UAV control twin space is used to analyze target attraction, obstacle repulsion, and mutual repulsion between UAVs.

[0010] Preferably, the UAV traffic control method integrating dynamic obstacle avoidance further includes: setting a basic sensing range and a speed factor, where the basic sensing range is the radius of the potential field in a stationary state, and the speed factor is the influence coefficient of speed on the radius of the potential field; obtaining the initial dynamic sensing range by multiplying the UAV speed by the speed factor and adding the basic sensing range; forming a dynamic potential field centered on the UAV by multiplying the actual environmental complexity by the initial dynamic sensing range, where the potential field intensity of the UAV is negatively correlated with the distance between the UAV and the obstacle or other UAVs.

[0011] Preferably, the UAV traffic control method integrating dynamic obstacle avoidance further includes: calculating the ratio of the actual environmental interference factor to the standard environmental interference factor, and calculating the environmental interference complexity by weighted calculation, where the environmental interference factor at least includes wind speed, temperature, and air pressure; calculating the ratio of the actual static obstacle parameter to the standard static obstacle parameter, and calculating the static obstacle complexity by weighted calculation, where the static obstacle parameter includes the position, size, and area density of the static obstacle; calculating the ratio of the actual dynamic obstacle parameter to the standard dynamic obstacle parameter, denoted as the dynamic obstacle complexity, where the dynamic obstacle parameter is the occurrence probability of the dynamic obstacle, obtained by statistically analyzing the occurrence proportion of the dynamic obstacle within a preset time range; weighted fusing the environmental interference complexity, static obstacle complexity, and dynamic obstacle complexity to obtain the environmental complexity.

[0012] Preferably, the UAV traffic control method integrating dynamic obstacle avoidance further includes: taking the static obstacle parameters of the target flight area as static constraints, taking non-overlapping flight paths as dynamic constraints, and taking the shortest total flight distance as the goal, planning flight paths according to the starting positions and target positions of each UAV in the multiple UAVs to generate multiple flight paths; building a UAV control twin space according to the multiple flight paths and the multiple dynamic potential field models.

[0013] Preferably, the UAV traffic control method integrating dynamic obstacle avoidance further includes: in the UAV control twin space, calculating the direction vector from the current position of the UAV to the target position, multiplying the direction vector by the gravitational coefficient to obtain the target gravity; calculating the relative distance from the UAV to the obstacle, if the relative distance is less than the dynamic perception range of the dynamic potential field, calculating the repulsive force of the obstacle, wherein the direction of the repulsive force is the direction away from the obstacle, and the magnitude of the repulsive force is negatively correlated with the relative distance; calculating the relative distance between UAVs, when the relative distance between UAVs is less than the safety distance, calculating the direction and magnitude of the mutual repulsive force between the two UAVs to obtain the mutual repulsive force between UAVs, wherein the direction of the mutual repulsive force is the unit vector connecting the two UAVs, and the magnitude of the mutual repulsive force is negatively correlated with the relative distance between UAVs.

[0014] Preferably, the UAV traffic control method integrating dynamic obstacle avoidance further includes: collecting the position information of the UAV in consecutive K frames within a preset time window to obtain a position coordinate sequence, where K is an integer greater than or equal to 3; analyzing and obtaining the actual path length and effective displacement distance of the UAV according to the position coordinate sequence, and setting the ratio of the effective displacement distance to the actual path length as the trajectory efficiency; setting a displacement threshold and a trajectory efficiency threshold, when the displacement of the UAV in consecutive K frames is less than the displacement threshold and the trajectory efficiency is lower than the trajectory efficiency threshold, it is determined that the UAV is in a potential deadlock state.

[0015] Preferably, the UAV traffic control method integrating dynamic obstacle avoidance further includes: when the relative distance between two UAVs is less than the safety distance and they are in a potential deadlock state, triggering a deadlock resolution mechanism, then evaluating the urgency of the two UAVs respectively, setting the one with a higher urgency score as the high priority, and setting the one with a lower urgency score as the low priority; reducing the mutual repulsive force weight of the high-priority UAV and increasing the mutual repulsive force weight of the low-priority UAV, and adding a vertical force component and a horizontal component perpendicular to the mutual repulsive force direction to the low-priority UAV to guide it to perform three-dimensional avoidance; setting the number of frames for deadlock resolution, if the duration exceeds the number of frames for deadlock resolution, cancel the priority setting and restore the normal obstacle avoidance mode of the UAV.

[0016] Preferably, the UAV traffic control method integrating dynamic obstacle avoidance further includes: obtaining the flight characteristic information and task type of two UAVs, where the flight characteristic information includes the remaining distance and remaining power; obtaining the task importance according to the task type; comprehensively evaluating the urgency of the two UAVs based on the task importance, remaining distance and remaining power, where the task importance is positively correlated with the urgency, and the remaining distance and remaining power are negatively correlated with the urgency.

[0017] Preferably, the UAV traffic control method integrating dynamic obstacle avoidance further includes: setting a deadlock warning mechanism, when the distance between two UAVs is less than the warning distance and the relative velocity directions point to each other, and the trajectory efficiency is relatively low, triggering the deadlock resolution mechanism in advance; predicting the potential collision time of the two UAVs, when the potential collision time is less than the set threshold, according to the priorities of the two UAVs, performing preventive avoidance in advance; wherein, the predicting the potential collision time of the two UAVs includes: performing flight path deviation analysis according to the flight control system accuracy error, the actual environmental interference factor, and the probability of the appearance of dynamic obstacles, to obtain the flight path deviation ratio; according to the flight path deviation ratio, expanding the current flight paths of the two UAVs to obtain two predicted path coverage ranges; using the UAV control twin space, performing collision simulation of the two UAVs according to the two predicted path coverage ranges, when the two predicted path coverage ranges are connected, recording the connection time point as the potential collision time.

[0018] Preferably, the UAV traffic control method integrating dynamic obstacle avoidance further includes: adding the target gravitational force, the obstacle repulsive force, and the mutual repulsive force between UAVs to obtain the flight resultant force; updating the speed and position of the UAV according to the current speed, the current position, and the resultant force of the UAV to obtain the new speed and the new position, where the new speed is the product of the inertia coefficient and the current speed plus the product of the difference between 1 and the inertia coefficient and the resultant force, and the new position is calculated according to the new speed and the current position; checking whether all UAVs have reached their respective target points, if so, ending the iteration, otherwise continuing to update the position until all UAVs reach their respective target points.

[0019] The beneficial effects of the present invention are: by establishing a dynamic potential energy field centered on the UAV, multiple dynamic potential energy field models are obtained, wherein the dynamic potential energy field is used to form a dynamic perception range; then obtaining the static obstacle parameters of the target flight area, and building a UAV control twin space in combination with the multiple dynamic potential energy field models; then during the multi-UAV flight operation, using the UAV control twin space, combining the deadlock detection mechanism and the deadlock resolution mechanism, performing dynamic route regulation, and iteratively updating the positions of the UAVs until all UAVs reach their respective target points, wherein the UAV control twin space is used to perform analysis of the target gravitational force, the obstacle repulsive force, and the mutual repulsive force between UAVs. That is to say, through the adjustment of the dynamic perception range, the UAV can efficiently perceive obstacles and other UAVs; through the intelligent deadlock detection and multi-dimensional resolution mechanism, the deadlock problem in multi-UAV flight is successfully solved; through the dynamic priority allocation and vertical separation strategy, the obstacle avoidance efficiency of multi-UAVs is optimized, thereby significantly improving the obstacle avoidance efficiency and safety of the multi-UAV system in a complex environment. Description of the Drawings

[0020] Figure 1Schematic flow chart of the UAV traffic control method integrating dynamic obstacle avoidance provided by the present invention; Figure 2 Schematic flow chart of establishing a dynamic potential field centered on a UAV in the UAV traffic control method integrating dynamic obstacle avoidance provided by the present invention. Detailed implementation manners

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part rather than all of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.

[0022] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined.

[0023] In the description of the present invention, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present invention is not necessarily construed as being more preferred or having more advantages than other embodiments. In order for any person skilled in the art to implement and use the present invention, the following description is given. In the following description, details are set forth for purposes of explanation. It should be understood that those skilled in the art can recognize that the present invention can be implemented without using these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope that conforms to the principles and features disclosed in the present invention.

[0024] Embodiment 1, as Figure 1 shown, the embodiment of the present invention provides a UAV traffic control method integrating dynamic obstacle avoidance, which specifically includes the following steps: S10: Initialize the state parameters of multiple UAVs, and establish a dynamic potential field centered on the UAVs to obtain multiple dynamic potential field models, where the dynamic potential field is used to form a dynamic perception range.

[0025] Further, as Figure 2 shown, step S10 of the present invention further includes: Set the basic perception range and speed factor, where the basic perception range is the radius of the potential energy field in the stationary state, and the speed factor is the influence coefficient of speed on the radius of the potential energy field; multiply the UAV speed by the speed factor and add the basic perception range to obtain the initial dynamic perception range.

[0026] Specifically, first, initialize the state parameters of multiple UAVs, that is, set the state parameters such as the initial position, speed, and mission objective for each UAV. Then, establish a dynamic potential energy field centered on the UAV, and the dynamic potential energy field is used to form a dynamic perception range to obtain multiple dynamic potential energy field models.

[0027] Among them, the method for establishing a dynamic potential energy field centered on the UAV is as follows. First, set the basic perception range and speed factor. The basic perception range is used to define the radius of the potential energy field of the UAV in the stationary state, and the speed factor represents the influence weight of the flight speed on the radius of the potential energy field. For example, set the basic perception range to 4.5 meters and the speed influence factor to 5. Then, multiply the UAV speed by the speed factor and add the basic perception range, and use the sum of the two as the initial dynamic perception range. That is to say, the UAV has a default perception range when it is stationary, called the basic perception range. When it starts to fly, this perception range is dynamically expanded according to its speed. The faster the speed, the larger the perception range. For example, assume the basic perception range is 4.5 meters, the speed influence factor is 5, and the flight speed is 10 meters per second. Then the initial dynamic perception range is 10 * 5 + 4.5, which is 54.5 meters.

[0028] Multiply the actual environmental complexity by the initial dynamic perception range to form a dynamic potential energy field centered on the UAV, where the potential energy field intensity of the UAV is negatively correlated with the distance between the UAV and obstacles or other UAVs.

[0029] Furthermore, the present invention further includes the following steps: Calculate the ratio of the actual environmental interference factor to the standard environmental interference factor, and weighted calculation to obtain the environmental interference complexity, where the environmental interference factor at least includes wind speed, temperature, and air pressure; calculate the ratio of the actual static obstacle parameter to the standard static obstacle parameter, and weighted calculation to obtain the static obstacle complexity, where the static obstacle parameter includes the position, size, and regional density of the static obstacle; calculate the ratio of the actual dynamic obstacle parameter to the standard dynamic obstacle parameter, denoted as the dynamic obstacle complexity, where the dynamic obstacle parameter is the occurrence probability of the dynamic obstacle, obtained by statistically counting the occurrence proportion of the dynamic obstacle within a preset time range; weighted fusion of the environmental interference complexity, static obstacle complexity, and dynamic obstacle complexity to obtain the environmental complexity.

[0030] Specifically, first, obtain environmental interference factors, which include but are not limited to wind speed, temperature, and air pressure. Here, mainly consider the impact of natural environmental factors on the flight of drones. Usually, the wind speed is the largest, followed by air pressure (affecting lift), and then temperature (affecting the battery / sensor); Next, obtain the standard environmental interference factors, where the standard environmental interference factors are the optimal flight environment parameters of the drone and can be set according to the actual scenario; Then, calculate the ratio of the actual environmental interference factors to the standard environmental interference factors, that is, analyze the deviation degree of the actual environmental interference factors from the standard environmental interference factors, and perform weighted calculation on the deviation degrees of multiple interference factors to obtain the environmental interference complexity, which reflects the impact degree of the current environment on the flight of the drone.

[0031] Next, obtain static obstacle parameters, including the position, size (average width, height, or volume of the obstacle), and area density (number of obstacles or proportion of occlusion area within a specified range, such as 0.1 obstacle per square meter on average) of the static obstacles. Static obstacles refer to those objects whose positions do not change, such as buildings, trees, utility poles, mountains, etc., which affect the flight path design and safety of the drone; Then calculate the ratio of the actual static obstacle parameters to the standard static obstacle parameters, and assign a weight to each parameter (for example, density importance 40%, size 30%, distribution distance 30%). Finally, perform weighted averaging to obtain the static obstacle complexity, which is used to evaluate the "danger level" or "complexity level" of the static obstacles to the flight.

[0032] Then, obtain dynamic obstacle parameters. The dynamic obstacle parameters are the occurrence probabilities of dynamic obstacles, which are obtained by statistically analyzing the occurrence ratios of dynamic obstacles within a preset time range (such as within the last week). For example, within a week, 20 statistics were made, and the number of bird appearances was 2 times, so the occurrence probability is 2 / 20 = 0.1. Among them, dynamic obstacles refer to those obstacles whose positions and states may change, such as birds; Next, calculate the ratio of the actual dynamic obstacle parameters to the standard dynamic obstacle parameters, which is set as the dynamic obstacle complexity. The greater the dynamic obstacle complexity, the more frequent and influential the dynamic obstacles (such as vehicles, pedestrians, flying birds, etc.) in the environment, and the higher the flight risk. The drone may require higher warning and more complex obstacle avoidance strategies. Since the impact degree of each complexity value on the total environmental complexity is different, a weight needs to be assigned to each complexity value. The setting of the weight is usually based on the actual situation and task requirements and can be determined through experience or simulation tests. The sum of the weights of the three complexity items should be 1; Finally, according to the weight configuration result, weighted fusion of the environmental interference complexity, static obstacle complexity, and dynamic obstacle complexity is performed to obtain the environmental complexity, which is used to help the drone system judge the flight risk.

[0033] Further, multiply the actual environmental complexity by the initial dynamic perception range, and use the product of the two as the dynamic potential field centered on the drone, and set the potential field strength of the drone. The potential field strength of the drone is negatively correlated with the distance between the drone and the obstacle or other drones. That is, the closer the obstacle or other drones are, the greater the strength of the potential field. This means that the potential field will be adjusted according to the distance from the obstacle or other drones. The closer the distance, the greater the attraction or repulsion force received. For example, if the distance is 2 units, the potential field strength may be 10; while when the distance is 10 units, the potential field strength may be only 2.

[0034] By combining the environmental complexity and the dynamic perception range, the perception ability of the drone can be dynamically adjusted; the larger the perception range, the more obstacles or other drones the drone can sense in advance, and the higher the flight flexibility and safety; and the negative correlation between the potential field strength and the distance ensures that during the flight of the drone, by increasing the sensitivity to nearby obstacles, the flight trajectory can be automatically adjusted to avoid collisions.

[0035] S20: Obtain the static obstacle parameters of the target flight area, and build a drone control twin space in combination with the multiple dynamic potential field models.

[0036] Further, step S20 of the present invention further includes: Taking the static obstacle parameters of the target flight area as static constraints, the non-overlapping flight path as dynamic constraints, and the shortest total flight distance as the goal, perform flight path planning according to the starting positions and target positions of each drone in the multi-drone, and generate multiple flight paths; build a drone control twin space according to the multiple flight paths and the multiple dynamic potential field models.

[0037] Specifically, taking the static obstacle parameters of the target flight area as static constraints, the static obstacle parameters refer to the fixed obstacles existing in the flight area, such as buildings, hills, trees, etc. The existence of these obstacles forms static constraints on the flight path, and the unmanned aerial vehicle (UAV) must avoid these obstacles to prevent collisions. Therefore, when planning the path, it is necessary to take into account the positions, sizes, and area densities of these obstacles to ensure that the flight path does not cross any static obstacles. Taking non-overlapping flight paths as dynamic constraints, that is, the path of each UAV must avoid crossing or overlapping with the paths of other UAVs to reduce mutual interference and collision risks. This constraint requires that the path planning algorithm not only ensures that each UAV can avoid static obstacles but also avoids conflicts between paths. Taking the shortest total flight distance as the goal, that is, the shortest flight distance is one of the goals of path planning. The UAV should try to choose the shortest flight path to reduce energy consumption, improve efficiency, and reduce flight time. On the premise of ensuring obstacle avoidance and non-overlapping paths, the flight distance of each UAV is minimized through an optimization algorithm. According to the starting positions and target positions of each UAV in the multi-UAV system, flight path planning can be carried out using algorithms such as the A* algorithm, Dijkstra algorithm, genetic algorithm, or particle swarm optimization algorithm, etc., to calculate the optimal path for each UAV from the starting position to the target position and avoid overlapping with the paths of other UAVs during the calculation process, generating multiple flight paths.

[0038] Then, based on the multiple flight paths and multiple dynamic potential field models, a UAV control twin space is constructed. The twin space is a virtual copy of the physical system or environment, used for real-time monitoring, simulation, and optimization. For the UAV system, the control twin space means mapping multiple UAVs, the flight area, and its environmental factors (such as obstacles, weather, the positions of other UAVs, etc.) into the virtual space, and using this virtual space to simulate and optimize tasks such as flight path planning, obstacle avoidance, and coordination; among them, the twin space needs to monitor in real time parameters such as the position, speed, and flight path of each UAV, and integrate them with the environmental parameters (such as wind speed, air pressure, temperature, etc.) in the flight area; map the static obstacles (buildings, terrain, etc.) and dynamic obstacles (such as other UAVs, flying objects, etc.) in the flight area into the twin space, and the states (such as position, speed, appearance probability, etc.) of the dynamic obstacles need to be updated continuously; environmental interference factors (such as wind speed, temperature, air pressure, etc.) will affect the flight stability and path planning of the UAV. Therefore, in the twin space, the changes in environmental parameters need to affect the potential field, flight speed, and path adjustment of the UAV.

[0039] By constructing a control twin space over the entire flight area, efficient cooperation and path optimization of multi-UAV flight missions can be achieved; the twin space provides a real-time simulation and feedback environment for each UAV, which can handle multiple factors such as static obstacles, dynamic constraints, and flight path optimization, and adjusts and optimizes the flight path in real time through a dynamic potential field model, which not only improves the safety of flight missions but also enhances flight efficiency.

[0040] S30: During multi-UAV flight operations, utilize the UAV control twin space, combine a deadlock detection mechanism and a deadlock resolution mechanism, perform dynamic route regulation, and iteratively update the UAV positions until all UAVs reach their respective target points, where the UAV control twin space is used for analyzing target attraction, obstacle repulsion, and mutual repulsion between UAVs.

[0041] Furthermore, step S30 of the present invention further includes: Within the UAV control twin space, calculate the direction vector from the current position of the UAV to the target position, multiply the direction vector by the attraction coefficient to obtain the target attraction; calculate the relative distance from the UAV to the obstacle, if the relative distance is less than the dynamic perception range of the dynamic potential field, calculate the obstacle repulsion, where the direction of the repulsion is the direction away from the obstacle, and the magnitude of the repulsion is negatively correlated with the relative distance; calculate the relative distance between UAVs, when the relative distance between UAVs is less than the safety distance, calculate the direction and magnitude of the mutual repulsion between the two UAVs to obtain the mutual repulsion between UAVs, where the direction of the mutual repulsion is the unit vector connecting the two UAVs, and the magnitude of the mutual repulsion is negatively correlated with the relative distance between UAVs.

[0042] Specifically, first, within the UAV control twin space, calculate the direction vector from the current position of the UAV to the target position. The direction vector indicates the direction from the current position to the target position, and the magnitude (i.e., its modulus) of this vector can represent the straight-line distance from the current position of the UAV to the target position; then, multiply the direction vector by the attraction coefficient, and take the product of the two as the target attraction, where the attraction coefficient is 0.02. The target attraction refers to the force by which the UAV is attracted to the target, and its magnitude is related to the distance between the current position of the UAV and the target position.

[0043] Next, calculate the relative distance from the UAV to the obstacle, which represents the straight-line distance between the current position of the UAV and the obstacle. Then, determine whether this distance is less than the dynamic sensing range. If it is less than this range, it indicates that the obstacle has an impact on the UAV. At this time, calculate the repulsive force of the obstacle. The repulsive force of the obstacle is a force negatively correlated with the relative distance between the UAV and the obstacle, that is, the smaller the distance, the greater the repulsive force, and the direction of the repulsive force is the direction away from the obstacle. For example, for a cylindrical obstacle, calculate the horizontal distance from the UAV to the center of the obstacle minus the radius of the obstacle to obtain the actual distance. When the distance is less than the sensing range, calculate the repulsive force, where the direction of the repulsive force is the direction away from the center of the obstacle, and the magnitude of the repulsive force is negatively correlated with the actual distance. For a rectangular obstacle, calculate the distance from the UAV to the nearest point of the obstacle. When the distance is less than the sensing range, calculate the repulsive force, where the direction of the repulsive force is the direction away from the nearest point, and the magnitude of the repulsive force is negatively correlated with the distance. When the height of the UAV is lower than the height of the obstacle and the horizontal distance is close to the edge of the obstacle, increase the vertical upward repulsive force component to guide the UAV to fly upward over the obstacle. By calculating the repulsive force of the obstacle in real time, the flight control system can dynamically adjust the flight trajectory of the UAV to ensure avoiding obstacles and achieving safe flight.

[0044] Then, calculate the relative distance between UAVs, which represents the straight-line distance between two UAVs; obtain the safety distance, which can be set according to the UAV type and flight scenario. For example, set the safety distance to 20 meters. When the relative distance between UAVs is less than the safety distance, that is, the minimum safety interval that must be maintained between UAVs. If the relative distance between two UAVs is less than this safety distance, it means that they may collide or fly too close. At this time, it is necessary to calculate the repulsive force between them to avoid collision. At this time, calculate the direction and magnitude of the repulsive force between the two UAVs to obtain the repulsive force between UAVs. Among them, the direction of the repulsive force is the unit vector connecting the two UAVs, and the magnitude of the repulsive force is negatively correlated with the relative distance between UAVs, that is, the direction of the repulsive force between UAVs is from one UAV to the other UAV, that is, the unit direction vector between the two UAVs; the magnitude of the repulsive force is usually negatively correlated with the relative distance between UAVs, that is, the smaller the distance, the greater the repulsive force. Through this mechanism, when the relative distance between two UAVs is too close, the repulsive force will prompt them to move away from each other, thus avoiding collision and ensuring flight safety. This mechanism is particularly important for multi-UAV cooperative operations and dynamic obstacle avoidance.

[0045] Furthermore, step S30 of the present invention further includes: Collect the position information of the drone at consecutive K frames within a preset time window to obtain a sequence of position coordinates, where K is an integer greater than or equal to 3; analyze the obtained sequence of position coordinates to obtain the actual path length and the effective displacement distance of the drone, and set the ratio of the effective displacement distance to the actual path length as the trajectory efficiency; set a displacement threshold and a trajectory efficiency threshold, and when the displacement of the drone at consecutive K frames is less than the displacement threshold and the trajectory efficiency is lower than the trajectory efficiency threshold, it is determined that the drone is in a potential deadlock state.

[0046] Specifically, collect the position information of the drone at consecutive K frames within a preset time window (such as within 10 seconds) to obtain a sequence of position coordinates, where K is an integer greater than or equal to 3; then, analyze the obtained sequence of position coordinates to obtain the actual path length and the effective displacement distance of the drone. The actual path length refers to the total path length that the drone passes from the starting position to the ending position, and the actual path length can be obtained by calculating the displacements between consecutive frames and summing them up; the effective displacement distance is the straight-line distance from the starting position to the final position of the drone. Further, set the ratio of the effective displacement distance to the actual path length as the trajectory efficiency, which reflects whether the drone effectively moves in the target direction. If the trajectory efficiency is low, it indicates that the drone may be hovering in place or deviating from the predetermined path. For example, if the trajectory efficiency is close to 1, it means that the drone is flying along the shortest path and has high efficiency; if the trajectory efficiency is much less than 1, it means that the flight path of the drone is deviated, or there may be a situation of staying at a certain position.

[0047] Set a displacement threshold and a trajectory efficiency threshold, where the displacement threshold and the trajectory efficiency threshold can be set according to the flight scenario and flight requirements. For example, set the displacement threshold to 5 meters and the trajectory efficiency threshold to 0.8. When the displacement of the drone at consecutive K frames is less than the displacement threshold and the trajectory efficiency is lower than the trajectory efficiency threshold, it is determined that the drone is in a potential deadlock state.

[0048] Further, step S30 of the present invention further includes: When the relative distance between two drones is less than the safety distance and they are in a potential deadlock state, trigger a deadlock resolution mechanism, then conduct an urgency evaluation on the two drones respectively, set the one with a larger urgency score as the high priority, and set the one with a smaller urgency score as the low priority.

[0049] Further, the present invention further includes the following steps: Obtain the flight characteristic information and task types of the two drones, where the flight characteristic information includes the remaining distance and the remaining battery power; match and obtain the task importance according to the task type; conduct an overall urgency evaluation on the two drones respectively based on the task importance, the remaining distance, and the remaining battery power, where the task importance is positively correlated with the urgency, and the remaining distance and the remaining battery power are negatively correlated with the urgency.

[0050] Specifically, when the relative distance between two UAVs is less than the safety distance and they are in a potential deadlock state, the deadlock resolution mechanism is triggered. Then, an urgency evaluation is performed on the two UAVs respectively. By comprehensively considering factors such as task importance, remaining flight distance, and remaining battery power, the urgency score of each UAV is obtained. The UAV with the higher urgency score is set as the high priority, and the UAV with the lower urgency score is set as the low priority. Through the urgency evaluation, the two UAVs can be prioritized according to factors such as the importance of the tasks, remaining battery power, and remaining distance of the UAVs, so as to reasonably allocate tasks and resources and avoid competition conflicts.

[0051] Among them, the method of urgency evaluation is as follows. First, obtain the flight characteristic information and task types of the two UAVs. Among them, the flight characteristic information includes the remaining distance and the remaining battery power. The remaining distance refers to the remaining flight distance for the UAV to reach the target position, and the remaining battery power refers to the remaining battery power of the UAV's current battery. Then, match and obtain the task importance according to the task type (such as emergency rescue, patrol, surveillance, etc.). Among them, each task will have a task importance score. Usually, emergency tasks (such as rescue) will have a higher task importance, while non-emergency tasks (such as patrol) will be lower. The task importance is positively correlated with the urgency, that is, the more urgent the task, the higher the urgency. Then, a comprehensive urgency evaluation is performed on the two UAVs respectively according to the task importance, remaining distance, and remaining battery power. Among them, the task importance is positively correlated with the urgency, and the remaining distance and remaining battery power are negatively correlated with the urgency, that is, the greater the task importance, the greater the urgency; the smaller the remaining distance and the smaller the remaining battery power, the greater the urgency. Through this comprehensive urgency evaluation, the system can reasonably allocate tasks to ensure that the high-priority UAV can complete emergency tasks first or adjust its flight path. This mechanism ensures that UAVs with tight resources or important tasks can obtain the right of way priority, improving the overall efficiency and resource utilization rate of the system.

[0052] Reduce the mutual exclusion force weight of the high-priority UAV, increase the mutual exclusion force weight of the low-priority UAV, and add a vertical force component and a horizontal component perpendicular to the mutual exclusion force direction to the low-priority UAV to guide it to perform three-dimensional avoidance; set the number of frames for deadlock resolution to continue. If the duration exceeds the number of frames for deadlock resolution to continue, cancel the priority setting and restore the normal obstacle avoidance mode of the UAV.

[0053] Specifically, then, in order to reduce interference to high-priority drones, the weight of their mutual repulsion force is reduced, which can prevent high-priority drones from being overly restricted during emergency tasks and ensure that they can quickly and accurately avoid obstacles and other drones. On the contrary, the weight of the mutual repulsion force of low-priority drones is increased, so that when low-priority drones encounter other drones, they can more actively avoid collisions, ensuring that high-priority drones have enough space to perform tasks. Further, for low-priority drones, when avoiding obstacles, not only the obstacle avoidance force in the horizontal plane is considered, but also the obstacle avoidance force in the vertical direction and the horizontal obstacle avoidance force component perpendicular to the direction of the mutual repulsion force can be introduced to guide them to perform three-dimensional avoidance. That is, when a drone faces an obstacle or other drones, in addition to the thrust in the horizontal plane, the avoidance in the vertical direction also needs to be considered. For example, if there is another drone approaching in the vertical direction, the low-priority drone can be guided to adjust its flight altitude up or down. This force component can help low-priority drones avoid direct collisions with other drones and more flexibly adjust their flight paths in a limited space. For example, if the direction of the mutual repulsion force between two drones points to the horizontal plane, then the avoidance force of the low-priority drone will not only be distributed in the horizontal direction but also consider the changes in the vertical direction, ensuring that the obstacle avoidance actions are more diverse.

[0054] Set the number of consecutive frames for deadlock resolution. Here, the number of consecutive frames for deadlock resolution is 10. That is, during the deadlock resolution process, in order to prevent the system from falling into a deadlock state, a number of consecutive frames can be set. That is, when a drone is in a potential deadlock state for a long time, the maximum number of consecutive frames for continuous resolution. If the deadlock resolution duration exceeds the set number of consecutive frames, the priority setting is cancelled, and the normal obstacle avoidance mode of the drone is restored. This can prevent the system from overly interfering with the trajectory of a certain drone, resulting in reduced efficiency. Whenever a drone is in a potential deadlock state, the system records the number of consecutive frames of this state. If it exceeds the maximum number of frames (such as 10 frames), the priority adjustment is automatically cancelled, and the normal obstacle avoidance mode is restored. The setting of the number of consecutive frames for deadlock resolution is to prevent the system from overly interfering with the trajectory of low-priority drones and ensure that the normal obstacle avoidance mode can be restored when the deadlock state lasts too long, improving the flexibility and efficiency of the system. Through these strategies, the safety and efficiency in multi-drone cooperative tasks can be effectively improved, conflicts between drones can be avoided, and the stable operation of the system can be ensured.

[0055] Furthermore, step S30 of the present invention further includes: Set up a deadlock warning mechanism. When the distance between two UAVs is less than the warning distance and the relative velocity directions point towards each other, and at the same time the trajectory efficiency is low, trigger the deadlock resolution mechanism in advance; predict the potential collision time of the two UAVs. When the potential collision time is less than the set threshold, according to the priorities of the two UAVs, carry out preventive avoidance in advance; among them, the prediction of the potential collision time of the two UAVs includes: analyzing the flight path deviation according to the flight control system accuracy error, actual environmental interference factor and the probability of the appearance of dynamic obstacles, and obtaining the flight path deviation ratio; according to the flight path deviation ratio, expand the current flight paths of the two UAVs to obtain two predicted path coverage ranges; use the UAV control twin space to perform collision simulation on the two UAVs according to the two predicted path coverage ranges. When the two predicted path coverage ranges meet, record the meeting time point as the potential collision time.

[0056] Specifically, set up a deadlock warning mechanism. The purpose of the deadlock warning mechanism is to trigger the deadlock resolution mechanism in advance when two UAVs are approaching and there is a potential deadlock risk, so as to avoid an unsolvable deadlock state. The deadlock state usually occurs when the distance between the two UAVs is very close and their relative velocity directions point towards each other, resulting in an inability to avoid normally. First, it is necessary to define a warning distance, that is, when the distance between the two UAVs is less than this warning distance, trigger the warning mechanism; then, calculate the relative velocity direction between the two UAVs. If the relative velocity points towards each other, it means that the two UAVs are moving towards each other and there is a collision risk. The trajectory efficiency is the ratio of the effective displacement of the UAV to the actual flight path length. When the trajectory efficiency is low, it means that the movement efficiency of the UAV is poor and it may enter a deadlock state. Therefore, if the trajectory efficiencies of the two UAVs are lower than the preset threshold, it can be considered that they may be at risk of a potential deadlock state. If the above conditions are met (that is, the distance between the two UAVs is less than the warning distance, the relative velocity directions point towards each other and the trajectory efficiency is low), then trigger the deadlock resolution mechanism in advance and start to avoid obstacles or adjust the flight path.

[0057] Next, predict the potential collision time of the two UAVs. The potential collision time prediction mechanism is used to evaluate in advance the time of collision between the two UAVs and take preventive avoidance measures before the collision occurs. This mechanism can dynamically adjust the flight strategies of the UAVs according to the motion states, flight paths and priorities of the two UAVs; in order to prevent collisions in advance, a collision time threshold can be set, such as 5 seconds. If the calculated potential collision time is less than 5 seconds, the system will consider the collision risk to be high and trigger the avoidance strategy; according to the task priorities of the two UAVs, determine the UAV that takes priority to avoid. If the priorities of the two UAVs are different, the UAV with the higher priority will perform the avoidance operation to ensure that it can complete more important tasks. On the contrary, the UAV with the lower priority will adaptively adjust its path to avoid conflicts with the UAV with the higher priority.

[0058] Among them, the method for predicting the potential collision time of two unmanned aerial vehicles (UAVs) is as follows. First, flight path deviation analysis is carried out according to the accuracy error of the flight control system, the actual environmental interference factor, and the probability of the appearance of dynamic obstacles. Among them, the flight control system will plan the flight path according to the sensor data. However, due to reasons such as sensor accuracy and system error, there will be a certain deviation between the actual flight path and the predetermined path, and these errors need to be estimated and compensated; external environmental factors such as wind speed, temperature change, and air pressure change will affect the flight trajectory of the UAV. Environmental factors such as wind speed will change the flight speed and direction, so the flight path needs to be adjusted according to the environmental interference factor; the appearance of dynamic obstacles (for example, birds, other flying objects, etc.) will also affect the flight path. Through historical data and sensor information, the probability of the appearance of dynamic obstacles is evaluated, and the deviation of the flight path is adjusted accordingly. Based on the above factors, the deviation ratio of the flight path is calculated. Assuming the deviation ratio is P, it represents the degree of deviation between the predetermined path and the actual path, which can be analyzed through historical data or predicted through a machine learning model.

[0059] Then, according to the flight path deviation ratio, the current flight paths of the two UAVs are extended. According to the extension of the flight path, the coverage ranges of the two predicted paths are obtained. The coverage range represents the spatial area that the two UAVs may occupy during flight. The purpose of path extension is to make the coverage range wider and be able to take into account the effects of path deviation, environmental interference, and dynamic obstacles. Then, the control twin space is used to simulate the flight paths of the UAVs, predict whether the two UAVs will collide, and calculate the potential collision time. By simulating the extended flight paths of the two UAVs and the coverage ranges of the predicted paths, it is calculated whether the two paths will intersect. If the coverage ranges of the two extended paths are adjacent, it means that the UAVs will collide at a certain time point; record the time point when the coverage ranges of the predicted paths are adjacent, and this time point is the potential collision time. If the time when the two paths are adjacent is less than the set threshold (for example, 5 seconds), it is considered that there is a collision risk between the two UAVs.

[0060] Furthermore, step S30 of the present invention further includes: Add the target gravitational force, the obstacle repulsive force, and the mutual repulsive force between the UAVs to obtain the flight resultant force; update the speed and position of the UAV according to the current speed, current position, and resultant force of the UAV to obtain the new speed and new position. Among them, the new speed is the product of the inertia coefficient and the current speed plus the product of the difference between 1 and the inertia coefficient and the resultant force, and the new position is calculated according to the new speed and the current position; check whether all UAVs have reached their respective target points. If so, end the iteration, otherwise continue to update the position until all UAVs reach their respective target points.

[0061] Specifically, first, the target gravity, obstacle repulsion, and mutual repulsion between drones are added to obtain the flight resultant force. The flight resultant force refers to the combined effect of various forces on the drone during flight, which determines the direction and speed of the drone. Then, the speed and position of the drone are updated according to the current speed, current position, and resultant force of the drone. The update speed of the drone is calculated based on the relationship between the inertia coefficient and the current speed and the flight resultant force. The inertia coefficient is usually used to control the response speed of the system, indicating the sensitivity of the system to the resultant force, where the inertia coefficient is 0.8; new speed = inertia coefficient × current speed + (1-inertia coefficient) × resultant force; then the position at the next moment is calculated according to the new speed and current position as the new position. After each iteration, it is necessary to check whether all drones have reached the target point. This is determined by comparing the distance between the current position of the drone and the target position. If the distance between the current position of the drone and the target point is less than a preset threshold (such as 1 meter), the drone is considered to have reached the target point. If all drones have reached their respective target points, the task is terminated and the iteration is stopped. Otherwise, the position update continues until all drones have completed the task. This method realizes collaborative obstacle avoidance and mission planning in a multi-drone system, ensuring that drones can effectively avoid obstacles and perform tasks according to predetermined goals.

[0062] The UAV traffic control method integrating dynamic obstacle avoidance provided by the embodiment of the present invention has at least the following technical effects: By establishing a dynamic potential field centered on a drone, multiple dynamic potential field models are obtained, wherein the dynamic potential field is used to form a dynamic perception range; then the static obstacle parameters of the target flight area are obtained, and the drone control twin space is built in combination with the multiple dynamic potential field models; then, when multiple drones are flying, the drone control twin space is used to perform dynamic route control and iteratively update the drone position in combination with the deadlock detection mechanism and the deadlock resolution mechanism until all drones reach their respective target points, wherein the drone control twin space is used to analyze target gravity, obstacle repulsion, and mutual repulsion between drones. In other words, by adjusting the dynamic perception range, the drone can achieve efficient perception of obstacles and other drones; through intelligent deadlock detection and multi-dimensional resolution mechanisms, the deadlock problem in multi-drone flight is successfully solved; through dynamic priority allocation and vertical separation strategies, the obstacle avoidance efficiency of multiple drones is optimized, thereby significantly improving the obstacle avoidance efficiency and safety of the multi-drone system in complex environments.

[0063] Although preferred embodiments of the present invention have been described, additional changes and modifications may occur to these embodiments once those skilled in the art understand the basic inventive concepts.

[0064] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A method for controlling unmanned aerial vehicle traffic integrating dynamic obstacle avoidance, characterized in that The method includes: Initializing the state parameters of multiple unmanned aerial vehicles (UAVs), and establishing a dynamic potential field centered on the UAVs to obtain multiple dynamic potential field models, where the dynamic potential field is used to form a dynamic perception range; Obtaining the static obstacle parameters of the target flight area, and building a UAV control twin space in combination with the multiple dynamic potential field models; During the flight operation of multiple UAVs, using the UAV control twin space, combining a deadlock detection mechanism and a deadlock resolution mechanism, performing dynamic route regulation, and iteratively updating the positions of the UAVs until all UAVs reach their respective target points, where the UAV control twin space is used to analyze target attraction, obstacle repulsion, and mutual repulsion between UAVs.

2. The method for controlling unmanned aerial vehicle traffic integrating dynamic obstacle avoidance according to claim 1, wherein Establishing a dynamic potential field centered on the UAVs includes: Setting a basic perception range and a speed factor, where the basic perception range is the radius of the potential field in a stationary state, and the speed factor is the influence coefficient of speed on the radius of the potential field; Adding the product of the UAV speed and the speed factor to the basic perception range to obtain an initial dynamic perception range; Multiplying the initial dynamic perception range by the actual environmental complexity to form a dynamic potential field centered on the UAVs, where the potential field strength of the UAV is negatively correlated with the distance between the UAV and obstacles or other UAVs.

3. The method for controlling an unmanned aerial vehicle traffic according to claim 2, characterized in that, The calculation method of the actual environmental complexity includes: Calculating the ratio of the actual environmental interference factor to the standard environmental interference factor, and calculating the environmental interference complexity by weighted calculation, where the environmental interference factor includes at least wind speed, temperature, and air pressure; Calculating the ratio of the actual static obstacle parameters to the standard static obstacle parameters, and calculating the static obstacle complexity by weighted calculation, where the static obstacle parameters include the position, size, and area density of static obstacles; Calculating the ratio of the actual dynamic obstacle parameters to the standard dynamic obstacle parameters, denoted as the dynamic obstacle complexity, where the dynamic obstacle parameter is the occurrence probability of dynamic obstacles, obtained by statistically analyzing the occurrence ratio of dynamic obstacles within a preset time range; Weightedly fusing the environmental interference complexity, static obstacle complexity, and dynamic obstacle complexity to obtain the environmental complexity.

4. The method for controlling an unmanned aerial vehicle traffic according to claim 3, characterized in that, Obtaining the static obstacle parameters of the target flight area, and building a UAV control twin space in combination with the multiple dynamic potential field models includes: Taking the static obstacle parameters of the target flight area as static constraints, taking non-overlapping flight paths as dynamic constraints, and taking the shortest total flight distance as the goal, planning flight paths according to the starting positions and target positions of each UAV in multiple UAVs, and generating multiple flight paths; Building a UAV control twin space according to the multiple flight paths and multiple dynamic potential field models.

5. The method for controlling an unmanned aerial vehicle traffic according to claim 1, characterized in that, Using the UAV control twin space to analyze target attraction, obstacle repulsion, and mutual repulsion between UAVs includes: In the UAV control twin space, calculating the direction vector from the current position of the UAV to the target position, and multiplying the direction vector by the attraction coefficient to obtain the target attraction; Calculating the relative distance from the UAV to the obstacle. If the relative distance is less than the dynamic perception range of the dynamic potential field, calculating the obstacle repulsion, where the repulsion direction is the direction away from the obstacle, and the repulsion magnitude is negatively correlated with the relative distance; Calculate the relative distance between drones. When the relative distance between drones is less than the safety distance, calculate the direction and magnitude of the repulsive force between the two drones to obtain the repulsive force between drones. Among them, the direction of the repulsive force is the unit vector connecting the two drones, and the magnitude of the repulsive force is negatively correlated with the relative distance between drones.

6. The method for controlling unmanned aerial vehicle traffic integrating dynamic obstacle avoidance according to claim 5, characterized in that, Set up a deadlock detection mechanism, including: Collect the position information of the drone in the continuous K frames within a preset time window to obtain a sequence of position coordinates, where K is an integer greater than or equal to 3; Analyze and obtain the actual path length and effective displacement distance of the drone according to the sequence of position coordinates, and set the ratio of the effective displacement distance to the actual path length as the trajectory efficiency; Set a displacement threshold and a trajectory efficiency threshold. When the displacement of the drone in continuous K frames is less than the displacement threshold and the trajectory efficiency is lower than the trajectory efficiency threshold, it is determined that the drone is in a potential deadlock state.

7. The method for controlling an unmanned aerial vehicle traffic according to claim 6, characterized in that, Set up a deadlock resolution mechanism, including: When the relative distance between two drones is less than the safety distance and they are in a potential deadlock state, trigger the deadlock resolution mechanism, then evaluate the urgency of the two drones respectively, set the one with a higher urgency score as the high priority, and set the one with a lower urgency score as the low priority; Reduce the repulsive force weight of the high-priority drone, increase the repulsive force weight of the low-priority drone, and add a vertical force component and a horizontal component perpendicular to the repulsive force direction to the low-priority drone to guide it to perform three-dimensional avoidance; Set the number of frames for deadlock resolution. If the duration exceeds the number of frames for deadlock resolution, cancel the priority setting and restore the normal obstacle avoidance mode of the drone.

8. The method for controlling an unmanned aerial vehicle traffic according to claim 7, wherein Evaluate the urgency of the two drones respectively, including: Obtain the flight characteristic information and task type of the two drones. Among them, the flight characteristic information includes the remaining distance and remaining battery; Match and obtain the task importance according to the task type; Based on the task importance, remaining distance and remaining battery, comprehensively evaluate the urgency of the two drones respectively. Among them, the task importance is positively correlated with the urgency, and the remaining distance and remaining battery are negatively correlated with the urgency.

9. The method for controlling an unmanned aerial vehicle traffic according to claim 7, wherein The execution of route dynamic regulation includes: Set up a deadlock warning mechanism. When the distance between two drones is less than the warning distance and the relative speed direction points to each other, and at the same time the trajectory efficiency is low, trigger the deadlock resolution mechanism in advance; Predict the potential collision time of the two drones. When the potential collision time is less than the set threshold, perform preventive avoidance in advance according to the priorities of the two drones; Among them, the prediction of the potential collision time of the two drones includes: Conduct flight path deviation analysis according to the flight control system accuracy error, actual environment interference factor and dynamic obstacle appearance probability to obtain the flight path deviation ratio; According to the flight path deviation ratio, expand the current flight paths of the two drones to obtain two predicted path coverage ranges; Use the drone control twin space to perform collision simulation of the two drones according to the two predicted path coverage ranges. When the two predicted path coverage ranges meet, record the meeting time point as the potential collision time.

10. The method for controlling an unmanned aerial vehicle traffic with integrated dynamic obstacle avoidance according to claim 5, wherein, Iteratively update the positions of the drones until all drones reach their respective target points, including: Add the target gravitational force, the obstacle repulsive force, and the mutual repulsive force between the drones to obtain the resultant flight force; Update the speed and position of the drone according to the current speed, current position, and resultant force of the drone to obtain the new speed and new position. Among them, the new speed is the product of the inertia coefficient and the current speed plus the product of the difference between 1 and the inertia coefficient and the resultant force, and the new position is calculated based on the new speed and the current position; Check whether all drones have reached their respective target points. If so, end the iteration; otherwise, continue to update the position until all drones reach their respective target points.

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