Unmanned aerial vehicle traffic control method fusing dynamic obstacle avoidance

Abstract

The present application relates to the unmanned aerial vehicle traffic control method of fusing dynamic obstacle avoidance, relates to the unmanned aerial vehicle cooperative control field, including: the dynamic potential field is established with the unmanned aerial vehicle as the center;Obtain static obstacle parameters, combine multiple dynamic potential field models to build unmanned aerial vehicle control twin space;When multiple unmanned aerial vehicles are flying, the route dynamic regulation and control are executed by using the unmanned aerial vehicle control twin space, combining the deadlock detection mechanism and the deadlock solution mechanism, and the position of the unmanned aerial vehicle is iteratively updated until all unmanned aerial vehicles reach their target points. Through the present application, the local minimum, route conflict and deadlock problem of the traditional unmanned aerial vehicle control method cannot be effectively solved. Through dynamic sensing range adjustment, the unmanned aerial vehicle can efficiently perceive. Through the deadlock detection and multidimensional solution mechanism, the deadlock problem in flight can be solved. Through the priority dynamic allocation and vertical separation strategy, the obstacle avoidance efficiency of multiple unmanned aerial vehicles is optimized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of unmanned aerial vehicle cooperative control, and in particular to an unmanned aerial vehicle traffic control method fusing dynamic obstacle avoidance. BACKGROUND

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

[0003] Currently, common unmanned aerial vehicle obstacle avoidance and path planning methods mainly include artificial potential field method, rapid extended random tree method, A* algorithm, etc. Among them, the artificial potential field method is widely used in real-time path planning of unmanned aerial vehicles due to its simplicity, intuition, high computational efficiency and other characteristics. However, the traditional artificial potential field method has some limitations in dealing with multi-unmanned aerial vehicle cooperative obstacle avoidance problems, mainly in the following aspects:

[0004] Fixed perception range: The traditional method usually uses a fixed perception range to detect obstacles and other unmanned aerial vehicles. This approach cannot be dynamically adjusted according to the motion state of the unmanned aerial vehicle, resulting in insufficient response at high speed and redundant calculation at low speed.

[0005] Single obstacle avoidance strategy: Existing methods mostly avoid obstacles in the horizontal direction, lacking the ability to use three-dimensional space for omnidirectional obstacle avoidance, limiting the efficiency of obstacle avoidance.

[0006] Deadlock problem: In a multi-unmanned aerial vehicle system, when two or more unmanned aerial vehicles approach each other and their paths intersect, they are likely to fall into a deadlock state of mutual avoidance. The traditional method lacks an effective deadlock detection and resolution mechanism.

[0007] Insufficient coordination: Coordination between multiple unmanned aerial vehicles usually relies on a simple repulsive force model, lacking priority management and cooperative decision-making mechanisms, reducing the overall efficiency of the system.

[0008] Therefore, there is an urgent need for a multi-unmanned aerial vehicle cooperative obstacle avoidance and deadlock resolution method that can solve the above problems to improve the cooperative flight efficiency and safety of multi-unmanned aerial vehicle systems in complex environments. SUMMARY

[0009] The present application provides a kind of unmanned aerial vehicle traffic control method fusing dynamic obstacle avoidance to solve the problem that traditional unmanned aerial vehicle control method cannot effectively solve the local minimum of multi-unmanned aerial vehicle system, route conflict and deadlock.

[0010] The technical solution of the present application to solve the above technical problems is as follows:

[0011] The present invention provides a UAV traffic control method integrating dynamic obstacle avoidance, comprising: initializing state parameters of multiple UAVs, and establishing a dynamic potential field centered on the UAV to obtain multiple dynamic potential field models, wherein the dynamic potential field is used to form a dynamic perception range; obtaining 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 multi-UAV flight operations, utilizing the UAV control twin space, combined with a deadlock detection mechanism and a deadlock resolution mechanism, performing dynamic route regulation, and iteratively updating the UAV positions until all UAVs reach their respective target points, wherein the UAV control twin space is used to analyze target gravity, obstacle repulsion, and mutual repulsion between UAVs.

[0012] Preferably, the drone traffic control method integrating dynamic obstacle avoidance also includes: setting a basic perception range and a speed factor, wherein the basic perception range is the potential field radius in a stationary state, and the speed factor is the influence coefficient of the speed on the potential field radius; multiplying the product of the drone speed and the speed factor plus the basic perception range to obtain an initial dynamic perception range; multiplying the actual environment complexity by the initial dynamic perception range to form a dynamic potential field centered on the drone, wherein the potential field strength of the drone is negatively correlated with the distance between the drone and obstacles or other drones.

[0013] Preferably, the UAV traffic control method integrating dynamic obstacle avoidance also includes: calculating the ratio of the actual environmental interference factor to the standard environmental interference factor, and performing weighted calculation to obtain the environmental interference complexity, wherein the environmental interference factor includes at least wind speed, temperature and air pressure; calculating the ratio of the actual static obstacle parameter to the standard static obstacle parameter, and performing weighted calculation to obtain the static obstacle complexity, wherein 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, and setting it as the dynamic obstacle complexity, wherein the dynamic obstacle parameter is the probability of occurrence of a dynamic obstacle, which is obtained by statistically calculating the proportion of dynamic obstacles occurring within a preset time range; and weightedly integrating the environmental interference complexity, static obstacle complexity and dynamic obstacle complexity to obtain the environmental complexity.

[0014] Preferably, the drone traffic control method integrating dynamic obstacle avoidance also includes: taking the static obstacle parameters of the target flight area as static constraints, taking the non-overlapping flight paths as dynamic constraints, taking the shortest total flight distance as the goal, performing flight path planning according to the starting position and target position of each drone in the multiple drones, and generating multiple flight paths; building a drone control twin space based on the multiple flight paths and multiple dynamic potential field models.

[0015] Preferably, the drone traffic control method integrating dynamic obstacle avoidance also includes: in the drone control twin space, calculating the direction vector from the current position of the drone to the target position, multiplying the direction vector by the gravity coefficient to obtain the target gravity; calculating the relative distance from the drone to the obstacle, if the relative distance is less than the dynamic perception range of the dynamic potential energy field, calculating the obstacle repulsion, wherein 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; calculating the relative distance between the drones, when the relative distance between the drones is less than the safety distance, calculating the direction and magnitude of the mutual repulsion between the two drones, and obtaining the mutual repulsion between the drones, wherein the direction of the mutual repulsion is the unit vector connecting the two drones, and the magnitude of the mutual repulsion is negatively correlated with the relative distance between the drones.

[0016] Preferably, the UAV traffic control method integrating dynamic obstacle avoidance further includes: collecting the position information of the UAV in K consecutive frames within a preset time window to obtain a position coordinate sequence, where K is an integer greater than or equal to 3; obtaining the actual path length and effective displacement distance of the UAV based on the position coordinate sequence analysis, 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, and when the UAV displacement in K consecutive 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.

[0017] Preferably, the drone traffic control method integrating dynamic obstacle avoidance also includes: when the relative distance between two drones is less than the safe distance and is in a potential deadlock state, the deadlock resolution mechanism is triggered, and the two drones are evaluated for urgency respectively, and the one with a larger urgency score is set as a high priority, and the one with a smaller urgency score is set as a low priority; the mutual repulsion weight of the high-priority drone is reduced, and the mutual repulsion weight of the low-priority drone is increased, and a vertical force component and a horizontal component perpendicular to the mutual repulsion direction are added to the low-priority drone to guide it to perform three-dimensional avoidance; the number of frames for deadlock resolution is set, and if the duration exceeds the number of frames for deadlock resolution, the priority setting is canceled and the normal obstacle avoidance mode of the drone is restored.

[0018] Preferably, the UAV traffic control method integrating dynamic obstacle avoidance also includes: obtaining flight characteristic information and mission types of the two UAVs, wherein the flight characteristic information includes remaining distance and remaining power; obtaining mission importance according to the mission type matching; and performing a comprehensive urgency evaluation on the two UAVs based on the mission importance, remaining distance and remaining power, wherein the mission importance is positively correlated with the urgency, and the remaining distance and remaining power are negatively correlated with the urgency.

[0019] Preferably, the unmanned aerial vehicle traffic control method fusing dynamic obstacle avoidance further comprises: setting a deadlock warning mechanism, when the distance between two unmanned aerial vehicles is less than a warning distance and the relative velocity direction points to each other, and the trajectory efficiency is low, triggering the deadlock solution mechanism in advance; predicting the potential collision time of the two unmanned aerial vehicles, when the potential collision time is less than a set threshold, according to the priority of the two unmanned aerial vehicles, performing preventive avoidance in advance; wherein the potential collision time of the two unmanned aerial vehicles is predicted, including: according to the flight control system accuracy error, the actual environmental disturbance factor and the dynamic obstacle appearance probability, the flight path deviation is analyzed to obtain the flight path deviation ratio; according to the flight path deviation ratio, the current flight path of the two unmanned aerial vehicles is expanded to obtain two predicted path coverage ranges; using the unmanned aerial vehicle control twin space, the collision simulation of the two unmanned aerial vehicles is performed according to the two predicted path coverage ranges, when the two predicted path coverage ranges are connected, the connected time point is recorded as the potential collision time.

[0020] Preferably, the unmanned aerial vehicle traffic control method fusing dynamic obstacle avoidance further comprises: adding the target attractive force, the obstacle repulsive force and the mutual repulsion force between unmanned aerial vehicles to obtain the flight resultant force; updating the speed and position of the unmanned aerial vehicle according to the current speed, the current position and the resultant force to obtain a new speed and a new position, wherein the new speed is the product of the inertia coefficient and the current speed plus the difference between 1 minus the inertia coefficient and the product of the resultant force, and the new position is calculated according to the new speed and the current position; checking whether all unmanned aerial vehicles have reached their respective target points, if yes, ending the iteration, otherwise, continuously updating the position until all unmanned aerial vehicles reach their respective target points.

[0021] The beneficial effects of the present application are: by establishing a dynamic potential field centered on the unmanned aerial vehicle, a plurality of dynamic potential field models are obtained, wherein the dynamic potential field is used to form a dynamic sensing range; then the static obstacle parameters of the target flight area are obtained, and the unmanned aerial vehicle control twin space is built in combination with the plurality of dynamic potential field models; then in the multi-unmanned aerial vehicle flight operation, the unmanned aerial vehicle control twin space is used in combination with the deadlock detection mechanism and the deadlock solution mechanism to perform route dynamic regulation and update the position of the unmanned aerial vehicle iteratively until all unmanned aerial vehicles reach their respective target points, wherein the unmanned aerial vehicle control twin space is used for target attractive force, obstacle repulsive force and mutual repulsion force analysis between unmanned aerial vehicles. That is, through dynamic sensing range adjustment, efficient sensing of obstacles and other unmanned aerial vehicles by the unmanned aerial vehicle can be realized; through intelligent deadlock detection and multi-dimensional solution mechanism, the deadlock problem in multi-unmanned aerial vehicle flight is successfully solved; through priority dynamic allocation and vertical separation strategy, the obstacle avoidance efficiency of the multi-unmanned aerial vehicle is optimized, thereby significantly improving the obstacle avoidance efficiency and safety of the multi-unmanned aerial vehicle system in complex environments. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1A flow chart of the UAV traffic control method integrating dynamic obstacle avoidance provided by the present invention;

[0023] Figure 2 A schematic diagram of the process of establishing a dynamic potential energy field centered on a drone in the drone traffic control method integrated with dynamic obstacle avoidance provided by the present invention. DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0025] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the specified features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0026] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or illustration". Any embodiment of the present invention described as "for example" is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, 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 widest scope consistent with the principles and features disclosed herein.

[0027] Example 1, as Figure 1 As shown, an embodiment of the present invention provides a UAV traffic control method integrating dynamic obstacle avoidance, which specifically includes the following steps:

[0028] S10: Initialize state parameters of multiple drones and establish a dynamic potential energy field centered on the drone to obtain multiple dynamic potential energy field models, wherein the dynamic potential energy field is used to form a dynamic perception range.

[0029] Further, if Figure 2 As shown, step S10 of the present invention further includes:

[0030] Set the basic perception range and speed factor, where the basic perception range is the radius of the potential energy field in a stationary state, and the speed factor is the coefficient of the influence of speed on the potential energy field radius; add the basic perception range to the product of the drone speed and the speed factor to obtain the initial dynamic perception range.

[0031] Specifically, first, the state parameters of multiple UAVs are initialized, that is, the initial position, speed, mission objectives and other state parameters are set for each UAV. Then, a dynamic potential energy field centered on the UAV is established. The dynamic potential energy field is used to form a dynamic perception range, and multiple dynamic potential energy field models are obtained.

[0032] The method for establishing a dynamic potential energy field centered on a drone is as follows: First, a basic perception range and a speed factor are set. The basic perception range defines the radius of the drone's potential energy field when stationary, and the speed factor represents the weight of the impact of flight speed on the potential energy field radius. For example, the basic perception range is set to 4.5 meters, and the speed factor is set to 5. Next, the product of the drone's speed and the speed factor is added to the basic perception range, and the sum of the two is used as the initial dynamic perception range. In other words, when the drone is stationary, it has a default perception range, called the basic perception range. Once it begins flying, this perception range is dynamically expanded based on its speed. The faster the speed, the larger the perception range. For example, assuming a basic perception range of 4.5 meters, a speed factor of 5, and a flight speed of 10 meters per second, the initial dynamic perception range is 10*5+4.5, or 54.5 meters.

[0033] The actual environment complexity is multiplied by the initial dynamic perception range to form a dynamic potential energy field centered on the drone, wherein the strength of the drone's potential energy field is negatively correlated with the distance between the drone and obstacles or other drones.

[0034] Furthermore, the present invention further comprises the steps of:

[0035] The ratio of the actual environmental interference factor to the standard environmental interference factor is calculated, and the environmental interference complexity is obtained by weighted calculation, where the environmental interference factors include at least wind speed, temperature, and air pressure. The ratio of the actual static obstacle parameter to the standard static obstacle parameter is calculated, and the static obstacle complexity is obtained by weighted calculation, where the static obstacle parameters include the location, size, and area density of static obstacles. The ratio of the actual dynamic obstacle parameter to the standard dynamic obstacle parameter is calculated and set as the dynamic obstacle complexity, where the dynamic obstacle parameter is the probability of occurrence of dynamic obstacles, which is obtained by counting the proportion of dynamic obstacles occurring within a preset time range. The environmental interference complexity, static obstacle complexity, and dynamic obstacle complexity are weightedly integrated to obtain the environmental complexity.

[0036] Specifically, first, obtain the environmental interference factor, which includes but is not limited to wind speed, temperature and air pressure. Here, we mainly consider the impact of natural environmental factors on the flight of the UAV. Usually, wind speed is the largest, followed by air pressure (affecting lift), and temperature is second (affecting batteries / sensors); then, obtain the standard environmental interference factor, where the standard environmental interference factor is the optimal flight environment parameter of the UAV, which can be set according to the actual scenario; then, calculate the ratio of the actual environmental interference factor to the standard environmental interference factor, that is, analyze the degree of deviation between the actual environmental interference factor and the standard environmental interference factor, and perform weighted calculation on the deviation degrees of multiple interference factors to obtain the environmental interference complexity, which reflects the degree of influence of the current environment on the flight of the UAV.

[0037] Next, obtain the static obstacle parameters, including the location, size (average width, height or volume of the obstacle) and area density (the number of obstacles or the proportion of blocked area within a specified range, such as an average of 0.1 obstacles per square meter). Static obstacles refer to objects whose positions do not change, such as buildings, trees, telephone 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 (such as density importance 40%, size 30%, distribution distance 30%), and finally take a weighted average to obtain the static obstacle complexity, which is used to evaluate the "danger level" or "complexity" of static obstacles to flight.

[0038] Next, the dynamic obstacle parameter is obtained. The dynamic obstacle parameter represents the probability of a dynamic obstacle appearing. It is calculated by counting the proportion of dynamic obstacles appearing within a preset timeframe (e.g., the last week). For example, if a bird appears twice out of 20 observations within a week, the probability of appearance is 2 / 20, which equals 0.1. Dynamic obstacles, such as birds, are those whose position and state may change. Next, the ratio of the actual dynamic obstacle parameter to the standard dynamic obstacle parameter is calculated and set as the dynamic obstacle complexity. A higher dynamic obstacle complexity indicates a greater frequency and impact of dynamic obstacles (such as vehicles, pedestrians, and birds) in the environment, leading to a higher flight risk. The drone may require more advanced warning and more complex obstacle avoidance strategies. Because each complexity value has a different impact on the overall environmental complexity, a weight is assigned to each complexity value. This weight is typically determined based on actual conditions and mission requirements and can be determined through experience or simulation testing. The sum of the weights of the three complexity terms should be 1. Finally, based on the weighted fusion of the environmental interference complexity, static obstacle complexity, and dynamic obstacle complexity, the environmental complexity is calculated to help the drone system determine flight risk.

[0039] Furthermore, the actual environment complexity is multiplied by the initial dynamic perception range, and the product of the two is used as the dynamic potential field centered on the drone, and the potential field strength of the drone is set, wherein 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 drone is, the greater the strength of the potential field. This means that the potential field will be adjusted according to the distance to the obstacle or other drone. The closer the distance is, the greater the attraction or repulsion it receives. 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.

[0040] By combining environmental complexity and dynamic perception range, the drone's perception capabilities can be dynamically adjusted; the larger the perception range, the more obstacles or other drones the drone can perceive in advance, and the higher the flight flexibility and safety; and the negative correlation between potential field strength and distance ensures that the drone can automatically adjust its flight trajectory to avoid collisions during flight by increasing its sensitivity to close-range obstacles.

[0041] S20: Obtain static obstacle parameters of the target flight area, and build a UAV control twin space in combination with the multiple dynamic potential energy field models.

[0042] Furthermore, step S20 of the present invention further includes:

[0043] Taking the static obstacle parameters of the target flight area as static constraints, the non-overlapping flight paths as dynamic constraints, and the shortest total flight distance as the goal, flight path planning is performed according to the starting position and target position of each UAV in the multi-UAV to generate multiple flight paths; a UAV control twin space is constructed based on the multiple flight paths and multiple dynamic potential energy field models.

[0044] Specifically, the static obstacle parameters of the target flight area serve as static constraints. Static obstacle parameters refer to fixed obstacles within the flight area, such as buildings, hills, and trees. The presence of these obstacles creates static constraints on the flight path, and drones must avoid them to prevent collisions. Therefore, during path planning, the location, size, and regional density of these obstacles must be taken into account to ensure that the flight path does not cross any static obstacles. A dynamic constraint is flight path non-overlap, meaning that each drone's path must avoid crossing or overlapping with other drones to reduce mutual interference and collision risk. This constraint requires the path planning algorithm to not only ensure that each drone avoids static obstacles but also avoid conflicts between paths. Minimizing the total flight distance is one of the objectives of path planning. Drones should strive to choose the shortest flight path to reduce energy consumption, improve efficiency, and shorten flight time. While ensuring obstacle avoidance and path non-overlap, an optimization algorithm is used to minimize the flight distance of each drone. Flight path planning is performed based on the starting position and target position of each UAV in the multi-UAV. A* algorithm, Dijkstra algorithm, genetic algorithm or particle swarm optimization algorithm can be used to calculate the optimal path from the starting position to the target position of each UAV, and avoid overlapping with the paths of other UAVs during the calculation process to generate multiple flight paths.

[0045] Then, a drone control twin space is built based on the multiple flight paths and multiple dynamic potential field models. The twin space is a virtual copy of the physical system or environment used for real-time monitoring, simulation, and optimization. For drone systems, controlling the twin space refers to mapping multiple drones, flight areas, and their environmental factors (such as obstacles, weather, and the positions of other drones) 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 the position, speed, flight path, and other parameters of each drone in real time, and integrate them with the environmental parameters in the flight area (such as wind speed, air pressure, and temperature). Static obstacles (buildings, terrain, etc.) and dynamic obstacles (such as other drones and flying objects) in the flight area are mapped into the twin space, and the status of dynamic obstacles (such as position, speed, and probability of occurrence) needs to be continuously updated. Environmental interference factors (such as wind speed, temperature, and air pressure) can affect the flight stability and path planning of the drone. Therefore, in the twin space, changes in environmental parameters need to affect the drone's potential field, flight speed, and path adjustment.

[0046] By building a control twin space in the entire flight area, efficient coordination and path optimization of multi-UAV flight missions can be achieved; the twin space provides each UAV with a real-time simulation and feedback environment, which can handle multiple factors such as static obstacles, dynamic constraints, flight path optimization, etc., and adjust and optimize the flight path in real time through a dynamic potential field model, which not only improves the safety of the flight mission, but also improves flight efficiency.

[0047] S30: During multi-UAV flight operations, the UAV control twin space is utilized, combined with the deadlock detection mechanism and the deadlock resolution mechanism, to perform dynamic route control and iteratively update the UAV positions until all UAVs reach their respective target points. The UAV control twin space is used to analyze target gravity, obstacle repulsion, and mutual repulsion between UAVs.

[0048] Furthermore, step S30 of the present invention further includes:

[0049] In the UAV control twin space, the direction vector from the current position of the UAV to the target position is calculated, and the direction vector is multiplied by the gravity coefficient to obtain the target gravity; the relative distance from the UAV to the obstacle is calculated. If the relative distance is less than the dynamic perception range of the dynamic potential energy field, the obstacle repulsion is calculated, 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; the relative distance between the UAVs is calculated. When the relative distance between the UAVs is less than the safety distance, the direction and magnitude of the mutual repulsive force between the two UAVs are calculated to obtain the mutual repulsive force between the 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 the UAVs.

[0050] Specifically, first, in the UAV control twin space, the direction vector from the current position of the UAV to the target position is calculated. The direction vector indicates the direction from the current position to the target position. The size of the vector (that is, its modulus) can represent the straight-line distance from the current position of the UAV to the target position; then, the direction vector is multiplied by the gravity coefficient, and the product of the two is used as the target gravity, where the gravity coefficient is 0.02. The target gravity refers to the force with which the UAV is attracted by the target, and its size is related to the distance between the current position of the UAV and the target position.

[0051] Next, the relative distance from the drone to the obstacle is calculated. This distance represents the straight-line distance between the drone's current position and the obstacle. The system then determines whether this distance is less than the dynamic sensing range. If so, the obstacle is affecting the drone, and the obstacle repulsion force is calculated. The obstacle repulsion is a force that is negatively correlated with the relative distance between the drone and the obstacle. That is, the smaller the distance, the greater the repulsion, and the direction of the repulsion is away from the obstacle. For example, for a cylindrical obstacle, the actual distance is calculated by subtracting the obstacle radius from the horizontal distance from the center of the obstacle. When the distance is less than the sensing range, the repulsion force is calculated, with the direction away from the obstacle center and the magnitude of the repulsion negatively correlated with the actual distance. For a rectangular obstacle, the distance from the drone to the nearest point of the obstacle is calculated. When the distance is less than the sensing range, the repulsion force is calculated, with the direction away from the nearest point and the magnitude of the repulsion negatively correlated with the distance. When the drone's altitude is lower than the obstacle and the horizontal distance approaches the obstacle edge, the upward repulsion force component is increased to guide the drone upward over the obstacle. By calculating the obstacle repulsion in real time, the flight control system can dynamically adjust the flight trajectory of the drone to ensure obstacle avoidance and safe flight.

[0052] Next, the relative distance between the two drones is calculated, representing the straight-line distance between them. A safe distance is obtained, which can be set based on the drone type and flight scenario. For example, a safe distance of 20 meters is set. When the relative distance between the drones is less than the safe distance, which is the minimum safe separation that must be maintained between drones, the two drones are at risk of a collision or are flying too close together. The mutual repulsion between them needs to be calculated to avoid a collision. The direction and magnitude of the mutual repulsion between the two drones are calculated to obtain the mutual repulsion. The direction of the mutual repulsion is the unit vector connecting the two drones, and the magnitude of the mutual repulsion is negatively correlated with the relative distance between the drones. The direction of the mutual repulsion between the drones is from one drone to the other, i.e., the unit direction vector between the two drones. The magnitude of the mutual repulsion is generally negatively correlated with the relative distance between the drones: the smaller the distance, the greater the mutual repulsion. Through this mechanism, when the relative distance between the two drones is too close, the mutual repulsion forces will force them to move away from each other, thus avoiding collision and ensuring flight safety. This mechanism is particularly important for multi-UAV collaborative operations and dynamic obstacle avoidance.

[0053] Furthermore, step S30 of the present invention further includes:

[0054] The UAV's position information is collected for K consecutive frames within a preset time window to obtain a position coordinate sequence, where K is an integer greater than or equal to 3. The actual path length and effective displacement distance of the UAV are analyzed based on the position coordinate sequence, and the ratio of the effective displacement distance to the actual path length is set as the trajectory efficiency. A displacement threshold and a trajectory efficiency threshold are set. When the UAV's displacement for K consecutive frames is less than the displacement threshold and the trajectory efficiency is lower than the trajectory efficiency threshold, the UAV is determined to be in a potential deadlock state.

[0055] Specifically, the drone's position information is collected for K consecutive frames within a preset time window (e.g., 10 seconds) to obtain a position coordinate sequence, where K is an integer greater than or equal to 3. Next, the actual path length and effective displacement distance of the drone are analyzed based on this position coordinate sequence. The actual path length refers to the total path length traversed by the drone from its starting position to its ending position, which can be obtained by calculating and summing the displacements between consecutive frames. The effective displacement distance refers to the straight-line distance from the drone's starting position to its final position. The ratio of the effective displacement distance to the actual path length is then defined as the trajectory efficiency, which reflects whether the drone is effectively moving toward its target. A low trajectory efficiency indicates that the drone may be wandering or deviating from its intended path. For example, a trajectory efficiency close to 1 indicates that the drone is flying along the shortest path and is therefore efficient. A trajectory efficiency far less than 1 indicates that the drone's flight path is deviating or may be stuck at a specific location.

[0056] Set a displacement threshold and trajectory efficiency threshold. These thresholds can be customized based on the flight scenario and flight requirements. For example, set the displacement threshold to 5 meters and the trajectory efficiency threshold to 0.8. If the drone's displacement for K consecutive frames is less than the displacement threshold and its trajectory efficiency is less than the trajectory efficiency threshold, the drone is considered to be in a potential deadlock state.

[0057] Furthermore, step S30 of the present invention further includes:

[0058] When the relative distance between two drones is less than the safe distance and they are in a potential deadlock state, the deadlock resolution mechanism is triggered, and the urgency of the two drones is evaluated separately. The one with a larger urgency score is set as a high priority, and the one with a smaller urgency score is set as a low priority.

[0059] Furthermore, the present invention further comprises the steps of:

[0060] acquire flight characteristic information and task type of the two unmanned aerial vehicles, wherein the flight characteristic information comprises residual distance and residual power; acquire task importance according to the task type; and comprehensively evaluate the urgency of the two unmanned aerial vehicles based on the task importance, the residual distance and the residual power, wherein the task importance is positively correlated with the urgency, and the residual distance and the residual power are negatively correlated with the urgency.

[0061] Specifically, when the relative distance between the two unmanned aerial vehicles is less than the safe distance and the two unmanned aerial vehicles are in a potential deadlock state, a deadlock resolution mechanism is triggered, and then the urgency of the two unmanned aerial vehicles is evaluated. By comprehensively considering the task importance, the residual flight distance and the residual power, the urgency score of each unmanned aerial vehicle is obtained. The unmanned aerial vehicle with a higher urgency score is set as a high priority, and the unmanned aerial vehicle with a lower urgency score is set as a low priority. Through the urgency evaluation, the two unmanned aerial vehicles can be prioritized according to the task importance, the residual power and the residual distance of the unmanned aerial vehicles, so as to reasonably allocate tasks and resources and avoid competition conflicts.

[0062] The method for evaluating the urgency is as follows. First, flight characteristic information and task type of the two unmanned aerial vehicles are acquired, wherein the flight characteristic information comprises residual distance and residual power. The residual distance refers to the remaining flight distance of the unmanned aerial vehicle to the target position, and the residual power refers to the current residual power of the battery of the unmanned aerial vehicle. Then, task importance is acquired according to the task type (such as emergency rescue, patrol, monitoring, etc.). Each task has a task importance score. Generally, an emergency task (such as rescue) has a higher task importance, while a non-emergency task (such as patrol) has a lower task importance. The task importance is positively correlated with the urgency, that is, the more urgent the task is, the higher the urgency is. Then, the urgency of the two unmanned aerial vehicles is comprehensively evaluated based on the task importance, the residual distance and the residual power. The task importance is positively correlated with the urgency, and the residual distance and the residual power are negatively correlated with the urgency. That is, the greater the task importance is, the greater the urgency is; the smaller the residual distance and the residual power are, the greater the urgency is. Through the comprehensive evaluation of the urgency, the system can reasonably allocate tasks, and ensure that the unmanned aerial vehicle with a high priority can complete an emergency task or adjust a flight path in priority. This mechanism ensures that the unmanned aerial vehicle with a high priority or an important task can obtain a priority right, and improves the overall efficiency and resource utilization of the system.

[0063] The repulsion force weight of the unmanned aerial vehicle with a high priority is reduced, and the repulsion force weight of the unmanned aerial vehicle with a low priority is increased. A vertical force component and a horizontal component perpendicular to the direction of the repulsion force are added to the unmanned aerial vehicle with a low priority, so as to guide the unmanned aerial vehicle to perform three-dimensional avoidance. A deadlock resolution duration is set. If the duration exceeds the deadlock resolution duration, the priority setting is cancelled, and the normal obstacle avoidance mode of the unmanned aerial vehicle is restored.

[0064] Specifically, to reduce interference with high-priority drones, the weight of their mutual repulsion force is lowered. This prevents high-priority drones from being overly constrained during urgent missions and ensures they can quickly and accurately avoid obstacles and other drones. Conversely, the weight of the mutual repulsion force for low-priority drones is increased, allowing them to more proactively avoid collisions when encountering other drones, ensuring that high-priority drones have sufficient space to carry out their missions. Furthermore, for low-priority drones, obstacle avoidance not only considers horizontal forces but also vertical forces and horizontal force components perpendicular to the direction of the mutual repulsion force, guiding them to perform three-dimensional avoidance. Specifically, when a drone faces an obstacle or another drone, in addition to horizontal thrust, vertical avoidance is also considered. For example, if another drone approaches vertically, the low-priority drone can be guided to adjust its flight altitude upward or downward. This force component can help low-priority drones avoid direct collisions with other drones and more flexibly adjust their flight paths within confined space. For example, if the mutual repulsion force of two drones points to the horizontal plane, the avoidance force of the low-priority drone will not only be distributed along the horizontal direction, but also consider the changes in the vertical direction to ensure more diverse obstacle avoidance actions.

[0065] Set the number of frames required to resolve a deadlock, with a limit of 10. This means that to prevent the system from entering a deadlock state, a set duration can be set. This is the maximum number of frames the deadlock resolution can last if a drone is in a potential deadlock state for an extended period. If the deadlock resolution duration exceeds the set duration, the priority setting is canceled and the drone returns to normal obstacle avoidance mode. This prevents the system from over-interfering with a particular drone's trajectory, which could reduce efficiency. Each time a drone is in a potential deadlock state, the system records the duration of that state. If the maximum number of frames exceeds this limit (for example, 10), the priority adjustment is automatically canceled and normal obstacle avoidance mode is restored. The deadlock resolution duration is set to prevent the system from over-interfering with the trajectory of low-priority drones and to ensure that normal obstacle avoidance mode can be restored if a deadlock state persists for an extended period, thereby improving system flexibility and efficiency. These strategies can effectively improve the safety and efficiency of multi-drone collaborative missions, avoid conflicts between drones, and ensure stable system operation.

[0066] Furthermore, step S30 of the present invention further includes:

[0067] A deadlock warning mechanism is set up. When the distance between the two UAVs is less than the warning distance and the relative speed direction is pointing towards each other, and the trajectory efficiency is low, the deadlock resolution mechanism is triggered in advance; the potential collision time of the two UAVs is predicted. When the potential collision time is less than the set threshold, preventive avoidance is performed in advance according to the priority of the two UAVs; wherein, the prediction of the potential collision time of the two UAVs includes: performing flight path deviation analysis based on the flight control system accuracy error, actual environmental interference factors and the probability of occurrence of dynamic obstacles to obtain the flight path deviation ratio; according to the flight path deviation ratio, the current flight paths of the two UAVs are expanded to obtain two predicted path coverage ranges; using the UAV control twin space, the collision simulation of the two UAVs is performed according to the two predicted path coverage ranges. When the two predicted path coverage ranges are connected, the connection time point is recorded as the potential collision time.

[0068] Specifically, a deadlock warning mechanism is established. Its purpose is to preemptively trigger a deadlock resolution mechanism when two drones approach and pose a potential risk of deadlock, thus avoiding an unresolvable deadlock. Deadlock typically occurs when the two drones are very close and their relative velocity is pointing toward each other, making proper avoidance impossible. First, a warning distance is defined. The warning mechanism is triggered when the distance between the two drones falls below this warning distance. Then, the relative velocity between the two drones is calculated. If the relative velocities are pointing toward each other, this indicates that the two drones are moving toward each other and pose a collision risk. Trajectory efficiency is the ratio of the effective displacement of a drone to the actual flight path length. Low trajectory efficiency indicates poor movement efficiency, potentially leading to a deadlock. Therefore, if the trajectory efficiency of two drones falls below a preset threshold, they are considered to be at potential risk of deadlock. If these conditions are met (i.e., the distance between the two drones is less than the warning distance, their relative velocity is pointing toward each other, and the trajectory efficiency is low), the deadlock resolution mechanism is preemptively triggered, initiating obstacle avoidance or adjusting flight paths.

[0069] Next, the potential collision time between the two drones is predicted. This potential collision time prediction mechanism estimates the potential collision time between the two drones in advance and takes preventative avoidance measures before a collision occurs. This mechanism dynamically adjusts the drones' flight strategies based on their motion states, flight paths, and priorities. To prevent collisions, a collision time threshold, such as 5 seconds, can be set. If the calculated potential collision time is less than 5 seconds, the system deems the collision risk high and triggers an avoidance strategy. The priority of the two drones' missions determines which drone to prioritize. If the two drones have different priorities, the higher-priority drone will perform avoidance maneuvers to ensure it can complete its more important mission. Conversely, the lower-priority drone will adaptively adjust its path to avoid collision with the higher-priority drone.

[0070] The method for predicting the potential collision time between two drones is as follows. First, a flight path deviation analysis is performed based on the flight control system's accuracy error, actual environmental interference factors, and the probability of dynamic obstacles. The flight control system plans the flight path based on sensor data. However, due to factors such as sensor accuracy and system errors, the actual flight path will deviate from the planned path. These errors need to be estimated and compensated. External environmental factors such as wind speed, temperature changes, and air pressure changes can affect the drone's flight trajectory. Environmental factors such as wind speed can alter flight speed and direction, necessitating adjustments to the flight path based on these environmental interference factors. The presence of dynamic obstacles (e.g., birds and other flying objects) can also affect the flight path. The probability of dynamic obstacles is assessed using historical data and sensor information, and the flight path deviation is adjusted accordingly. Based on these factors, the flight path deviation ratio is calculated. Assuming the deviation ratio is P, it represents the degree of deviation between the planned and actual paths. This can be analyzed using historical data or predicted using a machine learning model.

[0071] Then, based on the flight path deviation ratio, the current flight paths of the two drones are expanded. Based on the flight path expansion, the coverage ranges of the two predicted paths are obtained. The coverage range represents the spatial area that the two drones may occupy during flight. The purpose of path expansion is to provide a wider coverage range and to account for the impact of path deviation, environmental interference, and dynamic obstacles. Next, the flight paths of the drones are simulated using the control twin space to predict whether the two drones will collide and calculate the potential collision time. By simulating the extended flight paths of the two drones 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 connect, it indicates that the drones will collide at a certain point in time. The time point when the predicted path coverage ranges connect is recorded. This time point is the potential collision time. If the time the two paths connect is less than a set threshold (for example, 5 seconds), it is considered that the two drones are at risk of collision.

[0072] Furthermore, step S30 of the present invention further includes:

[0073] The target gravity, obstacle repulsion, and mutual repulsion between drones are added to obtain the flight resultant force; the speed and position of the drone are updated according to the current speed, current position, and resultant force of the drone to obtain a new speed and a new position, where the new speed is the product of the inertia coefficient and the current speed plus 1 minus the product of the difference between 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.

[0074] Specifically, the target's gravitational force, the obstacle's repulsive force, and the mutual repulsive force between drones are first added together to obtain the flight resultant force. The flight resultant force is the combined effect of all the forces acting on the drone during flight, which determines its direction and speed. Next, the drone's speed and position are updated based on its current speed, current position, and the resultant force. The updated speed is calculated based on the relationship between the inertia coefficient, current speed, and the flight resultant force. The inertia coefficient is typically used to control the system's response speed and indicates its sensitivity to the resultant force. The inertia coefficient is 0.8. New speed = inertia coefficient × current speed + (1 − inertia coefficient) × resultant force. The next position is then calculated based on the new speed and current position, serving as the new position. After each iterative update, it is necessary to check whether all drones have reached the target point. This is determined by comparing the distance between the drone's current position and the target position. If the distance between the drone's current position and the target point is less than a preset threshold (for example, 1 meter), the drone is considered to have reached the target point. If all drones have reached their respective target points, the mission ends and the iteration stops. Otherwise, position updates continue until all drones have completed the mission. This approach enables collaborative obstacle avoidance and mission planning in a multi-drone system, ensuring that drones can both effectively avoid obstacles and execute their missions as intended.

[0075] The UAV traffic control method integrating dynamic obstacle avoidance provided by the embodiments of the present invention has at least the following technical effects:

[0076] By establishing a dynamic potential energy field centered on a drone, multiple dynamic potential energy field models are obtained, wherein the dynamic potential energy field is used to form a dynamic perception range. Next, the static obstacle parameters of the target flight area are obtained, and the drone control twin space is constructed by combining the multiple dynamic potential energy field models. Then, during multi-drone flight operations, the drone control twin space is used to dynamically adjust the route and iteratively update the drone positions, combining deadlock detection and deadlock resolution mechanisms, until all drones reach their respective target points. 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, drones can achieve efficient perception of obstacles and other drones. Through intelligent deadlock detection and a multi-dimensional resolution mechanism, the deadlock problem in multi-drone flight is successfully resolved. Through dynamic priority allocation and vertical separation strategies, the obstacle avoidance efficiency of multiple drones is optimized, significantly improving the obstacle avoidance efficiency and safety of the multi-drone system in complex environments.

[0077] Although preferred embodiments of the present invention have been described, additional changes and modifications to these embodiments may occur to those skilled in the art once the basic inventive concepts become known.

[0078] 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 equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A UAV traffic control method integrating dynamic obstacle avoidance is characterized by: Methods include: Initialize the state parameters of multiple drones and establish a dynamic potential energy field centered on the drone to obtain multiple dynamic potential energy field models. The dynamic potential energy field is used to form a dynamic perception range. Obtain static obstacle parameters of the target flight area and build a UAV control twin space by combining the multiple dynamic potential energy field models; During multi-UAV flight operations, the UAV control twin space is used to perform dynamic route control and iteratively update the UAV positions in combination with deadlock detection and resolution mechanisms until all UAVs reach their respective target points. The UAV control twin space is used to analyze target attraction, obstacle repulsion, and mutual repulsion between UAVs. Among them, the establishment of a dynamic potential energy field centered on the drone includes: Set the basic sensing range and speed factor. The basic sensing range is the radius of the potential energy field in a stationary state, and the speed factor is the coefficient of influence of speed on the radius of the potential energy field. The initial dynamic sensing range is obtained by multiplying the speed of the drone by the speed factor and adding the basic sensing range. Multiplying the initial dynamic perception range by the actual environment complexity to form a dynamic potential energy field centered on the drone, wherein the strength of the drone's potential energy field is negatively correlated with the distance between the drone and obstacles or other drones; The calculation method of the actual environment complexity includes: Calculate the ratio of the actual environmental interference factor to the standard environmental interference factor, and perform weighted calculation to obtain the environmental interference complexity, where the environmental interference factors include at least wind speed, temperature, and air pressure; Calculate the ratio of actual static obstacle parameters to standard static obstacle parameters, and perform weighted calculation to obtain static obstacle complexity. Static obstacle parameters include the location, size, and regional density of static obstacles. Calculate the ratio of the actual dynamic obstacle parameter to the standard dynamic obstacle parameter and set it as the dynamic obstacle complexity. The dynamic obstacle parameter is the probability of occurrence of dynamic obstacles, which is obtained by counting the proportion of dynamic obstacles appearing within a preset time range. The environmental interference complexity, the static obstacle complexity and the dynamic obstacle complexity are weightedly integrated to obtain the environmental complexity.

2. The UAV traffic control method integrating dynamic obstacle avoidance according to claim 1 is characterized in that: Obtain the static obstacle parameters of the target flight area and build a UAV control twin space based on the multiple dynamic potential energy field models, including: Taking the static obstacle parameters of the target flight area as static constraints, the non-overlapping flight paths as dynamic constraints, and the shortest total flight distance as the goal, flight path planning is performed based on the starting and target positions of each UAV in the multi-UAV network to generate multiple flight paths; A drone control twin space is built based on the multiple flight paths and multiple dynamic potential energy field models.

3. The UAV traffic control method integrating dynamic obstacle avoidance according to claim 1 is characterized in that: The UAV-controlled twin space is used to analyze target attraction, obstacle repulsion, and mutual repulsion between UAVs, including: In the UAV control twin space, a direction vector from the current position of the UAV to the target position is calculated, and the direction vector is multiplied by the gravity coefficient to obtain the target gravity; Calculate the relative distance between the drone and the obstacle. If the relative distance is less than the dynamic sensing range of the dynamic potential energy field, calculate the obstacle repulsion force. The direction of the repulsion force is away from the obstacle, and the magnitude of the repulsion force is negatively correlated with the relative distance. The relative distance between the UAVs is calculated. When the relative distance is less than the safe distance, the direction and magnitude of the mutual repulsion between the two UAVs are calculated to obtain the mutual repulsion between the UAVs. 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 the UAVs.

4. The UAV traffic control method integrating dynamic obstacle avoidance according to claim 3 is characterized in that: Set up deadlock detection mechanisms, including: Collect the position information of the UAV in K consecutive frames within a preset time window to obtain a position coordinate sequence, where K is an integer greater than or equal to 3; Obtaining the actual path length and effective displacement distance of the UAV according to the position coordinate sequence analysis, and setting the ratio of the effective displacement distance to the actual path length as the trajectory efficiency; The displacement threshold and trajectory efficiency threshold are set. When the displacement of the UAV in K consecutive frames is less than the displacement threshold and the trajectory efficiency is lower than the trajectory efficiency threshold, the UAV is determined to be in a potential deadlock state.

5. The UAV traffic control method integrating dynamic obstacle avoidance according to claim 4 is characterized in that: Set up deadlock resolution mechanisms, including: When the relative distance between two drones is less than the safe distance and they are in a potential deadlock state, the deadlock resolution mechanism is triggered. The two drones are evaluated for urgency respectively, and the one with the larger urgency score is set as high priority, and the one with the smaller urgency score is set as low priority. Reduce the mutual repulsion weight of high-priority drones and increase the mutual repulsion weight of low-priority drones. Add a vertical force component and a horizontal component perpendicular to the mutual repulsion direction to the low-priority drones to guide them to perform three-dimensional avoidance. Set the number of frames for deadlock resolution. If the duration exceeds the number of frames for deadlock resolution, the priority setting will be canceled and the drone will resume normal obstacle avoidance mode.

6. The UAV traffic control method integrating dynamic obstacle avoidance according to claim 5 is characterized in that: Conduct urgency evaluation on the two drones, including: Obtain flight characteristic information and mission types of the two unmanned aircraft, wherein the flight characteristic information includes remaining distance and remaining battery power; Obtaining task importance according to the task type matching; The urgency of the two drones was comprehensively evaluated based on mission importance, remaining distance and remaining battery power. The mission importance was positively correlated with urgency, while the remaining distance and remaining battery power were negatively correlated with urgency.

7. The UAV traffic control method integrating dynamic obstacle avoidance according to claim 5 is characterized in that: The dynamic control of the route execution includes: Set up a deadlock warning mechanism. When the distance between two drones is less than the warning distance and their relative speed is pointing towards each other, and the trajectory efficiency is low, the deadlock resolution mechanism is triggered in advance. Predict the potential collision time of two drones. When the potential collision time is less than the set threshold, preventive avoidance is performed in advance based on the priority of the two drones. The method of predicting the potential collision time between two UAVs includes: Perform flight path deviation analysis based on the flight control system accuracy error, actual environmental interference factors, and the probability of dynamic obstacles appearing, and obtain the flight path deviation ratio; According to the flight path deviation ratio, the current flight paths of the two UAVs are expanded to obtain two predicted path coverages; The UAV is used to control the twin space and perform a collision simulation of the two UAVs according to the two predicted path coverages. When the two predicted path coverages are connected, the connection time point is recorded as the potential collision time.

8. The UAV traffic control method integrating dynamic obstacle avoidance according to claim 3 is characterized in that: Iteratively update the drone positions until all drones reach their respective target points, including: Adding the target gravitational force, the obstacle repulsive force, and the mutual repulsive force between UAVs to obtain a flight resultant force; Update the drone's speed and position based on its current speed, current position, and resultant force to obtain new speed and new position. The new speed is the product of the inertia coefficient and the current speed plus 1 minus the product of the difference between the inertia coefficient and the resultant force. The new position is calculated based on the new speed and current position. Check whether all drones have reached their respective target points. If so, end the iteration; otherwise, continue updating the positions until all drones reach their respective target points.

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