A method and device for resolving local dynamic interlocking of unmanned aerial vehicle traffic flow

Abstract

This application provides a method and apparatus for resolving local dynamic interlocks in unmanned aerial vehicle (UAV) traffic flow. The method includes: in response to the absence of local dynamic interlocks between UAVs, setting the UAVs to a cruise state; in response to the occurrence of local dynamic interlocks between the UAVs, setting the UAVs to a hovering state; calculating the minimum conflict resolution time slot between hovering UAVs; inserting the obtained minimum conflict resolution time slot into the time axis of the original flight plan and updating the estimated arrival time; when the minimum conflict resolution time slot ends, detecting the local spatiotemporal saturation between UAVs; if the spatiotemporal saturation is greater than or equal to a spatiotemporal saturation threshold, the UAVs continue to hover and the minimum conflict resolution time slot is recalculated; if the spatiotemporal saturation is lower than the spatiotemporal saturation threshold, the UAVs are switched back to cruise state and route network reconstruction is performed. Through this method, this application senses airspace congestion thresholds and provides a buffer, avoiding collisions and system crashes.

Description

Technical Field

[0001] This application relates to the field of airspace cooperative control technology, and in particular to a method and apparatus for resolving local dynamic interlocks in UAV traffic flow. Background Technology

[0002] With the rapid rise of the global low-altitude economy, the demand for urban air traffic is also increasing, and application scenarios such as low-altitude logistics, inspection, and passenger transport are becoming increasingly diverse. This has led to a sharp increase in air traffic density, and the airspace environment is exhibiting highly dynamic and constrained characteristics, resulting in growing pressure on safety assurance.

[0003] Currently, in practical UAV route planning and conflict resolution, most strategies rely on speed adjustment or simple heading changes. These methods perform well in low-density, sparse traffic flow scenarios, maintaining safe distances by fine-tuning aircraft speed vectors. However, academic research and practical operation have revealed that when UAV flow density exceeds a certain threshold (i.e., airspace becomes saturated), the degrees of freedom within the airspace decrease sharply. Simple speed adjustments can trigger a "chain reaction," causing conflicts to propagate and amplify among multiple aircraft, ultimately leading to local dynamic interlocking.

[0004] Local dynamic interlocking manifests as a situation where, under current spatiotemporal resource constraints, regardless of speed adjustments or minor course tweaks, a drone cannot pass through a specific area without violating safety margins; it is trapped at a spatiotemporal resource interlocking point. In this situation, traditional algorithms often fail to find a feasible solution, leading to computational crashes or collisions. To overcome the bottlenecks in high-density air traffic development and achieve efficient utilization of airspace resources while providing a safety net in extreme situations, developing a method to address resource saturation and local dynamic interlocking is of significant practical importance. Summary of the Invention

[0005] This application provides a method and apparatus for resolving local dynamic interlocks in UAV traffic flow, so as to avoid the risk of system collapse caused by the solution space of UAV route planning being an empty set, and to achieve smooth control of airspace traffic.

[0006] Firstly, this application provides a method for resolving local dynamic interlocks in unmanned aerial vehicle (UAV) traffic flow, which is executed by a computing device. The computing device can be understood as a computer or similar device, and is not limited thereto in this application. The method includes:

[0007] The system determines whether a local dynamic interlock has occurred between drones. If no local dynamic interlock has occurred, the drones are set to cruise mode. If a local dynamic interlock has occurred, the drones are set to hover mode. The minimum conflict resolution time slot between hovering drones is calculated, representing the minimum waiting time required to release the dynamic interlock. The calculated minimum conflict resolution time slot is inserted into the timeline of the drones' original flight plan, updating the drones' estimated arrival times. When the minimum conflict resolution time slot ends, the local spatiotemporal saturation between drones is detected. If the spatiotemporal saturation is greater than or equal to the spatiotemporal saturation threshold, the drones continue to hover and the minimum conflict resolution time slot is recalculated. If the spatiotemporal saturation is lower than the spatiotemporal saturation threshold, the drones are switched back to cruise mode, and route network reconstruction is performed.

[0008] By employing the aforementioned method, this application first determines whether local dynamic interlocks occur between drones. If a local dynamic interlock occurs, the drones are set to a hovering state. By introducing the hovering state, a "time-for-space" buffer mechanism is provided when the system is about to enter a resource interlock state, effectively avoiding the risk of collisions and system crashes caused by an empty solution space. Simultaneously, the minimum conflict resolution time slot between drones is calculated to obtain the minimum waiting time required to release the dynamic interlock. This minimum conflict resolution time slot is then inserted into the timeline of the drones' original flight plans to update their estimated arrival times. This time slot insertion mechanism performs micro-level peak shaving and valley filling on high-density flows, avoiding the avalanche effect of congestion and ensuring the continuous operation of drone traffic flow under high load. Furthermore, when the minimum conflict resolution time slot ends, the local spatiotemporal saturation between drones is detected, enabling a keen perception of the critical point of airspace congestion. This not only allows for reconciliation when spatial saturation is low but also enables non-intervention when spatial saturation is high, thereby maximizing airspace traffic efficiency while ensuring absolute safety.

[0009] In the aforementioned method for resolving local dynamic interlocks in drone traffic flow, determining whether local dynamic interlocks occur between drones includes:

[0010] Based on the physical boundary of the airspace to be planned, the airspace is discretized into a three-dimensional airspace grid to obtain three-dimensional airspace grid cells. The spatiotemporal saturation of the three-dimensional airspace grid cells is calculated. If the spatiotemporal saturation is lower than the spatiotemporal saturation threshold, it is determined that no local dynamic interlock has occurred between the current UAVs. If the spatiotemporal saturation is greater than or equal to the spatiotemporal saturation threshold, the multi-UAV state prediction envelope is calculated based on the UAV's kinematic constraints. The environmental constraints of the UAVs are obtained based on the three-dimensional airspace grid cells. The multi-UAV state prediction envelope and the environmental constraints are superimposed and analyzed. If there is a feasible solution that satisfies the safe interval within the multi-UAV state prediction envelope, it is determined that no local dynamic interlock has occurred between the current UAVs. If there is no feasible solution that satisfies the safe interval within the multi-UAV state prediction envelope, it is determined that local dynamic interlock has occurred between the current UAVs.

[0011] Through the above methods, this application first discretizes the airspace into a three-dimensional airspace grid cell. Simultaneously, it defines a control model for the mixed state of UAVs, introducing a hovering state on top of the existing cruise state. This provides a "time-for-space" buffer mechanism when the system is about to enter a resource interlock state, breaking through the limitations of traditional single continuous flow models. Furthermore, by calculating spatiotemporal saturation and monitoring local spatiotemporal saturation in real time, it can keenly perceive the critical point of airspace congestion. Finally, by calculating the multi-UAV state prediction envelope, it determines whether dynamic interlocking has occurred between UAVs, allowing for the assessment of further countermeasures based on the UAV state.

[0012] In the aforementioned method for resolving local dynamic interlocks in UAV traffic flow, the calculation of the spatiotemporal saturation of the three-dimensional spatial grid cells includes:

[0013] ;

[0014] ;

[0015] in, This represents the spatiotemporal saturation of a three-dimensional spatial grid cell; Indicates the current grid cell, Indicates time, Static obstacle density, For dynamic traffic flow, The maximum throughput of the current grid cell is represented by α and β, which are weighting coefficients. For grid neighborhood The proportion of internal obstacle mesh, For characteristic functions, | | represents the total number of neighboring grid cells; j represents the grid index, indicating a specific grid cell within the neighborhood; V obs This represents the set of grid cells occupied by static obstacles; if Exceeding the preset threshold If so, the area is marked as a high-saturation risk zone.

[0016] Using the aforementioned method, the spatiotemporal saturation index of airspace units is defined based on parameters such as static obstacle density, dynamic traffic flow, and the maximum capacity of the designated unit, thereby achieving efficient utilization of airspace resources and a safety net in extreme situations.

[0017] In the aforementioned method for resolving local dynamic interlocks in UAV traffic flow, the calculation of the minimum conflict resolution time slot between hovering UAVs includes:

[0018] Calculate the minimum waiting time required to release the current dynamic interlock, i.e., the minimum conflict resolution time slot. Minimum conflict resolution slot Obtained through iterative search with discrete time steps, let ,in, Find the smallest integer , so that: ;

[0019] Where E represents the envelope; This represents a time slot, i.e., a local time variable within the prediction time window; This represents the minimum conflict resolution time slot; n represents an integer. Represents the smallest integer; Indicates a future time window; This represents the discrete time step, which is the time resolution used when performing the minimum conflict resolution time slot search; It is a conflict-free free spacetime resource; therefore, it can be concluded that... ;in, This indicates the candidate waiting time that is postponed when the drone is hovering.

[0020] By using the above method, the minimum conflict resolution time slot is obtained through iterative search based on the discrete time step, which can accurately calculate the time required to release the UAV's dynamic interlock, thus minimizing the time loss caused by the dynamic interlock to the UAV.

[0021] The aforementioned method for resolving local dynamic interlocks in UAV traffic flow also includes: defining a hybrid state control model for the UAV, where the hybrid state includes cruise state and hovering state; the hybrid state control model for the UAV is used to continuously update the UAV's position according to a preset route in cruise state, and its objective function is constructed based on operational efficiency and safety interval; in hovering state, the UAV maintains its spatial position coordinates unchanged, only increasing its dwell time in the time dimension.

[0022] By employing the above methods, the drone can perform well in cruise mode without experiencing local dynamic interlocks, and can autonomously complete its cruise mission. At the same time, in hover mode, the drone is kept hovering in place, providing some time and opportunity to resolve dynamic interlocks.

[0023] In the aforementioned method for resolving local dynamic interlocks in UAV traffic flow, the calculation of the multi-UAV state prediction envelope includes:

[0024] Based on the kinematic constraints of the UAV, the multi-aircraft state prediction envelope of the UAV is calculated within a future time window. The prediction envelope represents the set of spatiotemporal locations that the UAV may reach under all feasible control inputs.

[0025] Using the above method, this application predicts the prediction envelope, which covers the set of positions of the UAV under all feasible control inputs, and can accurately obtain multi-aircraft state prediction results.

[0026] Secondly, this application also provides a device for resolving local dynamic interlocks in UAV traffic flow, used to implement the aforementioned method for resolving local dynamic interlocks in UAV traffic flow. The device includes: a judgment module, a calculation module, an update module, and a detection module.

[0027] The system includes the following modules: a judgment module to determine whether a local dynamic interlock has occurred between UAVs; if no local dynamic interlock has occurred, the UAVs are set to cruise mode; a judgment module to set the UAVs to hover mode if a local dynamic interlock has occurred; a calculation module to calculate the minimum conflict resolution time slot between hovering UAVs, where the minimum conflict resolution time slot represents the minimum waiting time required to release the dynamic interlock; an update module to insert the calculated minimum conflict resolution time slot into the timeline of the UAVs' original flight plan and update the UAVs' estimated arrival time; a detection module to detect the local spatiotemporal saturation between UAVs when the minimum conflict resolution time slot ends; if the spatiotemporal saturation is greater than or equal to a spatiotemporal saturation threshold, the UAVs continue to hover and the minimum conflict resolution time slot is recalculated; and a detection module to switch the UAVs back to cruise mode and perform route network reconstruction if the spatiotemporal saturation is lower than the spatiotemporal saturation threshold.

[0028] Thirdly, this application also provides a computing device, comprising: a memory for storing program instructions; and a processor for calling the program instructions stored in the memory and executing the method described in the first aspect according to the obtained program instructions.

[0029] Fourthly, this application also provides a computer-readable storage medium storing computer-readable instructions, which, when read and executed by a computer, implement the method described in the first aspect.

[0030] Fifthly, this application provides a computer program product including a computer program executable by a computer device, which, when run on the computer device, causes the computer device to perform the method described in the first aspect.

[0031] Beneficial effects:

[0032] 1. Significantly improves the survivability and robustness of the system: This invention breaks through the limitations of the traditional single continuous flow model. By introducing a hovering state, it provides a "time-for-space" buffer mechanism when the system is about to enter a resource interlock state, effectively avoiding the risk of collisions and system crashes caused by an empty solution space.

[0033] 2. Achieving smooth control of airspace traffic: Through real-time monitoring of local spatiotemporal saturation, this invention can keenly detect the critical point of airspace congestion. By utilizing a time-slot insertion mechanism to smooth out peaks and valleys at the microscopic level for high-density flow, it avoids the avalanche effect of congestion and ensures the continuous operation capability of UAV traffic flow under high load.

[0034] 3. Balancing operational efficiency and safety baseline: The hybrid state control model design prioritizes operational efficiency under normal conditions, switching to conservative control only when an interlock risk is detected, thereby maximizing airspace passage efficiency while ensuring absolute safety. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 A flowchart illustrating a method for resolving local dynamic interlocks in UAV traffic flow, provided in Embodiment 1 of this application;

[0037] Figure 2 A schematic diagram of a UAV local dynamic interlock judgment method for a UAV traffic flow local dynamic interlock resolution method provided in this application embodiment;

[0038] Figure 3 This is a schematic diagram of the local dynamic interlock of a method for resolving local dynamic interlocks in UAV traffic flow provided in an embodiment of this application;

[0039] Figure 4 A flowchart illustrating a method for resolving local dynamic interlocks in UAV traffic flow provided in Embodiment 2 of this application.

[0040] Figure 5 A structural schematic diagram of a computing device provided by an embodiment of the present application. Specific embodiments

[0041] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0042] In the following embodiments of the present application, "and / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "At least one (or more) of the following" or similar expressions refer to any combination of these items, including any combination of single item (or more) or plural items (or more). For example, at least one (or more) of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, c can be single or multiple. The singular expression forms "one", "a kind of", "the", "above-mentioned", "this", and "this one" are also intended to include expressions such as "one or more" unless there is a clear indication to the contrary in the context. And, unless otherwise stated, the ordinal numbers such as "first", "second", etc. mentioned in the embodiments of the present application are used to distinguish multiple objects and are not used to limit the order, time sequence, priority, or importance of multiple objects.

[0043] Describing a reference to "an embodiment" or "some embodiments" etc. in the specification of the present application means that specific features, structures, or characteristics described in combination with this embodiment are included in one or more embodiments of the present application. Thus, statements such as "in an embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless otherwise specifically emphasized in other ways. The terms "comprising", "including", "having" and their variants all mean "including but not limited to" unless otherwise specifically emphasized in other ways.

[0044] Embodiment 1

[0045] Embodiment 1 of the present application provides a method for local dynamic interlock resolution of unmanned aerial vehicle traffic flow. This method is executed by a computing device, which can be understood as a device such as a computer, and the present application does not limit this here. The process of this method is as follows Figure 1 As shown, it includes:

[0046] Step 101: Determine whether a local dynamic interlock has occurred between the drones. If no local dynamic interlock has occurred between the drones, proceed to step 102; if a local dynamic interlock has occurred between the drones, proceed to step 103.

[0047] Step 102: In response to the absence of local dynamic interlock between drones, the drones are set to cruise mode.

[0048] Cruise mode, also known as cruise state, is applicable to non-interlocked states. In this state, the UAV continues to travel along a preset route and continuously updates its position. Its objective function is constructed based on operational efficiency and safety intervals. When it is determined that no local dynamic interlock has occurred between UAVs, if the current state of the UAV is cruise mode, it remains in the current state; if the current state of the UAV is hovering, it is switched to cruise mode to perform regular route travel and tracking.

[0049] Step 103: In response to the occurrence of local dynamic interlock between drones, the drones are set to hovering state.

[0050] The hovering state, also known as hovering mode, is applicable to interlocked states. In this state, the UAV maintains its spatial position coordinates unchanged, only increasing its hovering duration in the time dimension. When a local dynamic interlock is detected between UAVs, the state of the UAV is forcibly switched to hovering mode. In hovering mode, the three-dimensional spatial coordinates of the UAV are locked, causing it to perform an aerial hovering and waiting operation until a command to resume cruise state is received.

[0051] In one possible implementation, this application defines a hybrid state control model for an unmanned aerial vehicle (UAV), comprising a cruise state and a hovering state. The cruise state is applicable to non-dynamically interlocked states, where the UAV continuously updates its position according to a preset route, and its objective function is constructed based on operational efficiency and a safety interval. The hovering state is applicable to dynamically interlocked states, where the UAV maintains its spatial position coordinates unchanged, only increasing its dwell time in the time dimension.

[0052] Specifically, drones are prone to local dynamic interlocking in high-density scenarios. The method for determining local dynamic interlocking of drones in this application is as follows: Figure 2 As shown, it includes the following steps:

[0053] Step 201: Based on the physical boundary of the airspace to be planned, the airspace is discretized into a three-dimensional airspace grid to obtain three-dimensional airspace grid cells.

[0054] Specifically, based on the physical boundaries of the airspace to be planned, a three-dimensional coordinate system is established, and the airspace is divided into three-dimensional grid cells with independent attributes using the raster method. The center coordinates of each grid cell are then determined. and attributes.

[0055] Step 202: Define the spatiotemporal saturation index of the airspace unit, calculate the spatiotemporal saturation of the three-dimensional airspace grid unit, and if the spatiotemporal saturation is lower than the spatiotemporal saturation threshold, it is determined that no local dynamic interlock has occurred between the current UAVs.

[0056] Local spatiotemporal saturation index This is used to quantify the traffic congestion level of a current airspace unit. Specifically, the spatiotemporal saturation index of the airspace unit is defined, including:

[0057] ;

[0058] ;

[0059] in, This represents the spatiotemporal saturation of a three-dimensional spatial grid cell; Indicates the current grid cell, Indicates time, Static obstacle density, For dynamic traffic flow, This represents the maximum throughput capacity of the unit. and These are weighting coefficients; For grid neighborhood The proportion of internal obstacle mesh, For characteristic functions, | | represents the total number of neighboring grid cells; j represents the grid index, indicating a specific grid cell within the neighborhood; V obs This represents the set of grid cells occupied by static obstacles; if Exceeding the preset threshold If so, the area is marked as a high-saturation risk zone.

[0060] When the spatiotemporal saturation of the spatial domain Greater than or equal to When the spatiotemporal saturation of the current airspace is sufficient to meet the driving requirements of the UAV in the current airspace, it is determined that there is no local dynamic interlock, and the UAV enters the cruise state.

[0061] When the spatiotemporal saturation of the spatial domain Less than If the spatiotemporal saturation of the current airspace cannot meet the driving requirements of the UAV in the current airspace, it is determined that there may be a local dynamic interlock. At this time, the content of step 203 continues to be executed.

[0062] Step 203: If the spatiotemporal saturation is greater than or equal to the spatiotemporal saturation threshold, calculate the multi-aircraft state prediction envelope of the UAV based on the kinematic constraints of the UAV.

[0063] Specifically, based on the kinematic constraints of the UAV, the multi-aircraft state prediction envelope of the UAV within a future time window is calculated. The prediction envelope represents the set of spatiotemporal locations that the UAV may reach under all feasible control inputs.

[0064] Step 204: Obtain the environmental constraints of the UAV based on the three-dimensional spatial grid cells, and perform superposition analysis on the multi-UAV state prediction envelope and the environmental constraints. If there is a feasible solution that satisfies the safety interval within the multi-UAV state prediction envelope, it is determined that no local dynamic interlock has occurred between the current UAVs; if there is no feasible solution that satisfies the safety interval within the multi-UAV state prediction envelope, it is determined that a local dynamic interlock has occurred between the current UAVs.

[0065] Specifically, the schematic diagram of local dynamic interlock is as follows: Figure 3 As shown, in a gridded airspace network, there are UAVs 1, 2 and 3 traveling according to their respective preset routes. If they travel according to their preset routes, there will be times in the airspace network when the safe interval is not met due to factors such as heading and speed. At the same time, interlocking conflicts will occur between the UAVs. The area where the interlocking conflict occurs is the local dynamic interlocking area.

[0066] Step 104: Calculate the minimum conflict resolution time slot between hovering drones. The minimum conflict resolution time slot is used to represent the minimum waiting time required to release the dynamic interlock.

[0067] Specifically, calculate the minimum waiting time required to release the current dynamic interlock, i.e., the minimum conflict resolution time slot. ;

[0068] Minimum Conflict Resolution Slot Obtained through iterative search with discrete time steps, let ( Find the smallest integer. , so that:

[0069] ;

[0070] Where E represents the envelope; This represents a time slot, i.e., a local time variable within the prediction time window; This represents the minimum conflict resolution time slot; n represents an integer. Represents the smallest integer; Indicates a future time window; This represents the discrete time step, which is the time resolution used when performing the minimum conflict resolution time slot search; It is a conflict-free free spacetime resource;

[0071] Therefore, ;

[0072] in, This indicates the candidate waiting time that is postponed when the drone is hovering.

[0073] Step 105: Insert the calculated minimum conflict resolution time slot into the timeline of the UAV's original flight plan to update the UAV's estimated arrival time.

[0074] Specifically, when performing the minimum conflict resolution time slot insertion operation, the necessary waiting window is allocated on the time axis; logically, this is equivalent to adding a waiting window of duration to the current waypoint. The time intervals, for example, if the original flight plan for a drone was to fly straight for 1-40 seconds and ascend for 40-70 seconds, after inserting a minimum conflict resolution time slot, the drone would fly straight for 1-40 seconds, wait for 40-80 seconds, and ascend for 80-110 seconds. It should be noted that the above timeline is merely a simple example, and this application does not limit it.

[0075] Step 106: When the minimum conflict resolution time slot ends, detect the local spatiotemporal saturation between UAVs; if the spatiotemporal saturation is greater than or equal to the spatiotemporal saturation threshold, proceed to step 107; if the spatiotemporal saturation is lower than the spatiotemporal saturation threshold, proceed to step 108.

[0076] Specifically, when the minimum conflict resolution slot At the end, the environmental saturation index was checked again. .

[0077] like This indicates that the area is still in a state of high saturation and congestion. This indicates that congestion has eased.

[0078] Step 107: If the spatiotemporal saturation is greater than or equal to the spatiotemporal saturation threshold, the UAV continues to hover and the minimum conflict resolution time slot is recalculated.

[0079] like This indicates that the area remains in a state of high saturation and congestion. The drone will continue to hover and wait, and the time slot will be recalculated or extended. .

[0080] Step 106: If the spatiotemporal saturation is lower than the spatiotemporal saturation threshold, switch the UAV back to cruise mode and perform route network reconstruction.

[0081] like This indicates that congestion has eased, and the system state is switched back to cruise mode, triggering route reconfiguration. The A* algorithm is then used, based on the updated initial time. Search for the optimal path connecting the current point and the target point.

[0082] This application breaks through the limitations of the traditional single continuous flow model. By introducing a hovering mode, it provides a "time-for-space" buffer mechanism when the system is about to enter a resource interlock state, effectively avoiding the risk of collisions and system crashes caused by an empty solution space.

[0083] Example 2

[0084] Embodiment 2 of this application provides a method for resolving local dynamic interlocks in UAV traffic flow based on Embodiment 1. This method is executed by a computing device, which can be understood as a computer or other similar device, and is not limited thereto in this application. The method flow is as follows: Figure 4 As shown, it includes:

[0085] Step 401: The input layer obtains urban airspace environmental data and UAV flight parameters.

[0086] Specifically, the information acquisition process takes place at the input layer. The urban airspace environment data includes the city's Geographic Information System (GIS) and no-fly zone information, while the UAV flight parameters include the UAV's mission OD pair and performance data. It should be noted that the specific content of the aforementioned urban airspace environment data and UAV flight parameters is merely an example and may include other content; this application does not limit this.

[0087] Step 402: Discretization of the airspace environment and construction of the control model.

[0088] Accurate definition of the environment and model is fundamental to resolving high-density traffic flow conflicts. Specifically, the process includes the following steps:

[0089] Step 1: Discretize the three-dimensional spatial grid.

[0090] This step involves establishing a three-dimensional coordinate system based on building distribution, no-fly zones, and delivery station locations for urban logistics and distribution scenarios. The continuous airspace to be planned is then discretized into three-dimensional grid cells using a raster method.

[0091] First, generate a single-layer raster and set the spatial grid resolution parameters. The grid resolution parameter is not limited, for example .

[0092] Secondly, determine the center coordinates of each grid cell. And physical properties.

[0093] Finally, based on the location of static obstacles (such as tall buildings), the grid containing the obstacles is marked as an impassable area, and the remaining grid is marked as free airspace.

[0094] Step 2, Define the mixed state control model

[0095] To adapt to complex traffic flow, this application predefines the dual-mode control logic of the UAV, designing two states for the UAV: ​​cruise state and hovering state; cruise state is also known as mode one, cruise mode; hovering state is also known as mode two, hovering mode.

[0096] Specifically, Mode 1: Cruise mode. Suitable for non-interlocked, conventional scenarios. The UAV continuously updates its position following a preset route, with the control objective being to minimize flight time and energy consumption.

[0097] Mode 2: Hovering mode. Suitable for high-risk interlocking scenarios. The drone maintains its three-dimensional spatial coordinates. It remains unchanged, only consuming time resources to perform hovering and waiting in the air.

[0098] Step 3: Define the local spatiotemporal saturation index.

[0099] To assess the congestion status of airspace units in real time, a local spatiotemporal saturation index is defined. For any mesh element At any moment The saturation is calculated using the following formula:

[0100] ;

[0101] ;

[0102] in, This represents the spatiotemporal saturation of a three-dimensional spatial grid cell; Indicates the current grid cell, Indicates time, Static obstacle density, For dynamic traffic flow, This represents the maximum throughput capacity of the unit. and These are weighting coefficients; For grid neighborhood The proportion of internal obstacle mesh, For characteristic functions, | | represents the total number of neighboring grid cells; j represents the grid index, indicating a specific grid cell within the neighborhood; V obs This represents the set of grid cells occupied by static obstacles.

[0103] Through the above three steps, the construction of the airspace environment gridding and control model is realized.

[0104] Step 403, Dynamic Status Monitoring and Interlock Early Warning.

[0105] During the drone's mission, real-time monitoring of system status, dynamic status monitoring, and interlock early warning specifically include:

[0106] Step 1, determine spatial saturation Is it less than the preset threshold? If not, and the spatial saturation is greater than or equal to the preset threshold, the system mode is set to cruise state, i.e., system mode Mode 1; if yes, and the spatial saturation is less than the preset threshold, the multi-machine state prediction envelope is calculated.

[0107] Specifically, if spatial saturation Greater than or equal to the preset threshold If the current spatial saturation between drones is sufficient to meet normal driving requirements, the system mode is adjusted to cruise mode, i.e., Mode 1.

[0108] If space saturation Less than the preset threshold If the spatial saturation between the current drones is insufficient to meet the normal operation of the drones, then the multi-drone state prediction envelope is calculated to determine whether there is dynamic local interlocking between the drones.

[0109] Step 2: Calculate the multi-machine state prediction envelope;

[0110] Specifically, based on the kinematic constraints of the UAV, its future time window is calculated. Multi-machine state prediction envelope The kinematic constraints of the UAV include maximum speed, minimum speed, turning radius, etc. The multi-aircraft state prediction envelope represents the set of spatiotemporal positions that the UAV may reach under all feasible control inputs.

[0111] Specifically, the kinematic constraints are as follows:

[0112] (1) Kinematic constraint model of UAV

[0113] Assume the first in the spatial domain The state vector of the drone is ,in In three-dimensional space coordinates, For flight speed, Let be the heading angle. The control input vector of the UAV is defined as... ,in For tangential acceleration, The horizontal yaw rate, The climb angle of the flight path.

[0114] Based on this, the kinematic equations of the UAV can be listed as follows:

[0115] ;

[0116] in, For the climb angle, For tangential acceleration control input, For angular velocity control input; Indicates the first A drone at all times The x-coordinate of the position; Indicates the first A drone at all times The y-direction position coordinate; Indicates the first A drone at all times The z-axis position coordinates, i.e., the height; Indicates the first A drone at all times The magnitude of the flight speed; Indicates the first A drone at all times The heading angle.

[0117] Furthermore, the variables marked with "·" in the formula represent the first derivative of the corresponding state quantity with respect to time, that is, Represents the velocity component in the x-direction; Indicates the velocity component in the y-direction; This represents the velocity component in the z-direction, used to represent the climb / descent speed; This represents the rate of change of velocity, also known as tangential acceleration. This represents the rate of change of heading angle, which is the yaw rate / turning rate.

[0118] To ensure the physical feasibility of flight, the UAV must meet the following state constraints (velocity) and control input constraints (acceleration, climb angle, turn rate):

[0119] Speed ​​constraint: Limits the speed of the drone.

[0120]

[0121] in, This is the minimum speed limit, usually 0. This is the maximum speed limit.

[0122] Acceleration constraints: limited by the dynamic and aerodynamic characteristics of the UAV

[0123]

[0124] in, This is a minimum acceleration limit, typically 0. Maximum acceleration limit.

[0125] Turning radius constraint: Limits the maximum angular velocity to prevent overload during maneuvering.

[0126]

[0127] in, This represents the minimum turning radius of the drone.

[0128] Climb angle constraint: limited by the lift characteristics of the UAV

[0129] ;

[0130] Based on the above kinematic model, the multi-machine state prediction envelope is... Can be defined as a drone At the present moment status Departure, in the future time window Within, by applying all feasible control input sequences that satisfy the constraints. The set of all reachable three-dimensional spatial locations.

[0131] The mathematical formula for calculating the multi-machine state prediction envelope is as follows:

[0132]

[0133] in, for The instantaneously reachable cross section at time t is specifically defined as:

[0134] ;

[0135] in, Relative to the current time The future time offset can be understood as a local time variable within the prediction time window, therefore... Indicates a future time within the prediction window; Indicates the first drones in the future The instantaneous reachable section at a given moment is the set of all possible spatial locations that can be reached at that moment. Represents a three-dimensional spatial position vector, i.e. . It represents three-dimensional Euclidean space. The state extraction matrix is ​​represented by the state vector. Includes position, velocity, and attitude. T denotes the vector transpose symbol; matrix Used to extract the first three position coordinates, i.e. .

[0136] (2) State evolution and constraint formulas:

[0137] ;

[0138] (3) Control input constraints:

[0139] ;

[0140] in, This represents the integral variable, which is the time from the current moment to the future offset. The intermediate time parameter between them, and The difference is Used to predict time intervals, while This indicates a specific instant within the prediction interval; express The system state vector of the drone at any given time. The system of kinematic equations for the unmanned aerial vehicle (UAV) The variable representing the integral is the variable from the current time step. The starting time increment, with a value range of 100. arrive , Indicates in The control input vector applied at any given time, including acceleration and angular velocity. This represents the set of feasible control inputs, which is determined by the physical properties of the UAV.

[0141] By constructing the above envelope This allows kinematic constraints to be transformed into intuitive geometric volumes. In the subsequent step S202, it is only necessary to detect whether this geometric volume intersects with the obstacle mesh or other machine envelopes (i.e., This allows for the determination of potential conflicts, thus avoiding the need to search for an infinite number of trajectories one by one and significantly improving computational efficiency.

[0142] Step 3: Determine whether there is a local dynamic interlock between the drones. If not, the drones can meet the normal driving requirements. At this time, the system mode is adjusted to cruise mode, i.e., Mode 1. If there is an interlock, the drones cannot meet the normal driving requirements. At this time, the system mode is adjusted to hover mode, i.e., Mode 2.

[0143] Specifically, the predicted envelope An analysis is performed in conjunction with environmental constraints. If there is no feasible solution that satisfies the safety interval within the prediction envelope, meaning that all feasible trajectories would lead to a collision or violation of the interval rule, then the current state is determined to be trapped in a spatiotemporal resource interlock point, confirming that a local dynamic interlock has occurred.

[0144] Step 404: Execute the dual-modal control strategy.

[0145] Specifically, based on the monitoring results of step 403, specific control commands are executed to switch the UAV's system mode to a mode that meets the current requirements.

[0146] Step 1: Perform mode switching judgment.

[0147] If the determination result in step 403 is that there is no local dynamic interlock, the system will remain in cruise mode 1 and fly as planned.

[0148] If the determination result in step 403 is that a local dynamic interlock has occurred, the system will forcibly trigger a switching command to switch the UAV state from cruise state Mode 1 to hover state Mode 2.

[0149] Step 2: Execute hovering wait control while hovering.

[0150] After entering hover mode 2, the flight control system locks the current position coordinates and controls the drone to enter a stable hovering state, no longer moving forward, and waiting for conflict resolution instructions.

[0151] Step 405, time slot insertion and network reconstruction.

[0152] Calculating the minimum conflict resolution time slot, for hovering drones, involves using time-dimensional scheduling to trade off spatial mobility feasibility. Specifically, this includes the following steps:

[0153] Step 1: Calculate the minimum conflict resolution time slot.

[0154] The time slot calculation module is activated to calculate the minimum waiting time required to release the current interlock, i.e., the conflict resolution time slot. .

[0155] Conflict resolution slots Obtained through iterative search with discrete time steps. Let ( Find the smallest integer. Make:

[0156]

[0157] Where E represents the envelope; This represents a time slot, i.e., a local time variable within the prediction time window; This represents the minimum conflict resolution time slot; n represents an integer. Represents the smallest integer; Indicates a future time window; This represents the discrete time step, which is the time resolution used when performing the minimum conflict resolution time slot search; It is a conflict-free free spacetime resource.

[0158] From this we can obtain ;

[0159] in, This indicates the candidate waiting time that is postponed when the drone is hovering.

[0160] Step 2: Perform the minimum conflict resolution slot insertion operation.

[0161] Sure Then, it is inserted into the drone's flight plan timeline. Logically, this is equivalent to adding a time period of [duration missing] at the current waypoint. The time interval.

[0162] Step 3, route network reconstruction and restoration.

[0163] When the gap At the end, the environmental saturation index was checked again. .

[0164] like This indicates that the area remains in a state of high saturation and congestion. The drone will continue to hover in Mode 2 and wait, while recalculating or extending the time slot. .like This indicates that congestion has eased, so the system state is switched back to Mode 1, triggering route reconfiguration. The A* algorithm is then used, based on the updated initial time. It searches for the optimal path connecting the current point and the target point, thereby achieving conflict-free route planning.

[0165] Step 406: The output layer constructs conflict-free routes.

[0166] Through the above methods, this application achieves the elimination of the partial interlocking state of UAVs in a partially interlocked state and the construction of conflict-free flight paths, realizing smooth control of airspace traffic, taking into account both operational efficiency and safety bottom line, which is of great significance for promoting the safe development of the low-altitude economy.

[0167] Example 3

[0168] Embodiment 3 of this application provides a device for resolving local dynamic interlocks in UAV traffic flow, which is used to implement the aforementioned method for resolving local dynamic interlocks in UAV traffic flow. The device includes a judgment module, a calculation module, an update module, and a detection module.

[0169] The system includes the following modules: a judgment module to determine whether a local dynamic interlock has occurred between UAVs; if no local dynamic interlock has occurred, the UAVs are set to cruise mode; a judgment module to set the UAVs to hover mode if a local dynamic interlock has occurred; a calculation module to calculate the minimum conflict resolution time slot between hovering UAVs, where the minimum conflict resolution time slot represents the minimum waiting time required to release the dynamic interlock; an update module to insert the calculated minimum conflict resolution time slot into the timeline of the UAVs' original flight plan and update the UAVs' estimated arrival time; a detection module to detect the local spatiotemporal saturation between UAVs when the minimum conflict resolution time slot ends; if the spatiotemporal saturation is greater than or equal to a spatiotemporal saturation threshold, the UAVs continue to hover and the minimum conflict resolution time slot is recalculated; and a detection module to switch the UAVs back to cruise mode and perform route network reconstruction if the spatiotemporal saturation is lower than the spatiotemporal saturation threshold.

[0170] Example 4

[0171] Having introduced a device for resolving local dynamic interlocks in unmanned aerial vehicle traffic flow according to an exemplary embodiment of this application, we will now introduce a computing device according to another exemplary embodiment of this application.

[0172] Those skilled in the art will understand that various aspects of this application can be implemented as a system, method, or program product. Therefore, various aspects of this application can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, collectively referred to herein as a "circuit," "module," or "system."

[0173] In some possible implementations, the computing device according to this application may include at least one processor and at least one memory. The memory stores a computer program that, when executed by the processor, causes the processor to perform the steps in the UAV traffic flow local dynamic interlock resolution method according to various exemplary embodiments of this application described above.

[0174] The following reference Figure 5 To describe a computing device 130 according to this embodiment of the present application. Figure 5 The computing device 130 shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this application. Figure 5 As shown, the computing device 130 is presented in the form of a general-purpose smart terminal (or Bluetooth headset). The components of the computing device 130 may include, but are not limited to: at least one processor 131, at least one memory 132, and a bus 133 connecting different system components (including memory 132 and processor 131).

[0175] Bus 133 represents one or more of several bus architectures, including a memory bus or memory controller, peripheral bus, processor, or local bus using any of the various bus architectures. Memory 132 may include readable media in the form of volatile memory, such as random access memory (RAM) 1321 and / or cache memory 1322, and may further include read-only memory (ROM) 1323. Memory 132 may also include a program / utility 1325 having a set (at least one) of program modules 1324, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0176] The computing device 130 can also communicate with one or more external devices 134 (e.g., keyboard, pointing device, etc.), and / or with any device that enables the computing device 130 to communicate with one or more other smart terminals (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 135. Furthermore, the computing device 130 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 136. As shown, network adapter 136 communicates with other modules used in the computing device 130 via bus 133. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with the computing device 130, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0177] In some possible implementations, various aspects of the UAV traffic flow local dynamic interlock resolution method provided in this application can also be implemented in the form of a program product, which includes a computer program. When the program product is run on a computer device, the computer program is used to cause the computer device to perform the steps in the UAV traffic flow local dynamic interlock resolution method according to various exemplary embodiments of this application described above.

[0178] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0179] The program product for time-domain noise processing according to the embodiments of this application may employ a portable compact disc read-only memory (CD-ROM) and include a computer program, and may run on a smart terminal. However, the program product of this application is not limited thereto. In this document, the readable storage medium may be any tangible medium that contains or stores a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0180] A readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a readable computer program. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0181] It should be noted that although several units or sub-units of the device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0182] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0183] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable access frequency prediction device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable access frequency prediction device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0184] These computer program instructions may also be stored in a computer-readable storage medium capable of directing a computer or other programmable access predictive device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0185] These computer program instructions can also be loaded onto a computer or other programmable access predictive device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0186] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0187] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for resolving local dynamic interlock of unmanned aerial vehicle traffic flow, characterized in that, include: Determine whether a local dynamic interlock has occurred between the drones; if no local dynamic interlock has occurred between the drones, then set the drone to cruise mode. In response to the occurrence of a local dynamic interlock between the drones, the drones are set to a hovering state; Calculate the minimum conflict resolution time slot between the hovering drones, whereby the minimum conflict resolution time slot represents the minimum waiting time required to release the dynamic interlock; The calculated minimum conflict resolution time slot is inserted into the timeline of the original flight plan of the UAV to update the expected arrival time of the UAV; When the minimum conflict resolution time slot ends, the local spatiotemporal saturation between the UAVs is detected; If the spatiotemporal saturation is greater than or equal to the spatiotemporal saturation threshold, the UAV continues to hover and recalculates the minimum conflict resolution time slot. If the spatiotemporal saturation is lower than the spatiotemporal saturation threshold, the UAV will be switched back to cruise mode and route network reconstruction will be performed.

2. The method according to claim 1, characterized in that, The determination of whether local dynamic interlocking occurs between drones includes: Based on the physical boundary of the airspace to be planned, the airspace is discretized into a three-dimensional airspace grid to obtain three-dimensional airspace grid cells. Calculate the spatiotemporal saturation of the three-dimensional spatial grid cells. If the spatiotemporal saturation is lower than the spatiotemporal saturation threshold, it is determined that no local dynamic interlock has occurred between the current UAVs. If the spatiotemporal saturation is greater than or equal to the spatiotemporal saturation threshold, the multi-machine state prediction envelope of the UAV is calculated based on the kinematic constraints of the UAV. The environmental constraints of the UAV are obtained based on the three-dimensional spatial grid cells. The multi-UAV state prediction envelope is superimposed and analyzed with the environmental constraints. If there is a feasible solution that satisfies the safety interval within the multi-UAV state prediction envelope, it is determined that no local dynamic interlock has occurred between the current UAVs. If there is no feasible solution that satisfies the safety interval within the multi-UAV state prediction envelope, it is determined that a local dynamic interlock has occurred between the current UAVs.

3. The method according to claim 2, characterized in that, The calculation of the spatiotemporal saturation of the three-dimensional spatial grid cell includes: ； ； Among them, the The spatiotemporal saturation of a three-dimensional spatial grid cell is represented; Indicates the current grid cell, the Indicates time, the The static barrier density, the For dynamic traffic flow, the The maximum throughput of the current grid cell, where α and β are weighting coefficients; For grid neighborhood The proportion of the internal obstacle mesh, the For the characteristic function, the | | represents the total number of neighboring grid cells; j represents the grid index, indicating a specific grid cell within the neighborhood; V obs Represents the set of grid cells occupied by static obstacles; if the Exceeding the preset threshold If so, the area is marked as a high-saturation risk zone.

4. The method according to claim 1, characterized in that, The calculation of the minimum conflict resolution time slot between the hovering drones includes: The minimum conflict resolution time slot Obtained through iterative search with discrete time steps, let ,in, Find the smallest integer , so that: ； Wherein, E represents the envelope; the The time slot refers to a local time variable within the prediction time window; the... This represents the minimum conflict resolution time slot; n represents an integer, and the... Represents the smallest integer; the Indicates a future time window; the The discrete time step represents the time resolution used when performing the minimum conflict resolution time slot search; the... It is a conflict-free free spacetime resource; Therefore, ; Among them, the This indicates the candidate waiting time that is postponed when the drone is hovering.

5. The method according to claim 1, characterized in that, The method further includes: Define a hybrid state control model for the UAV, wherein the hybrid state includes cruise state and hovering state; The hybrid state control model of the UAV is used to enable the UAV to continuously update its position according to a preset route in the cruise state. Its objective function is constructed based on operating efficiency and safety interval. In the hovering state, the drone maintains its spatial position coordinates unchanged, only increasing the dwell time in the time dimension.

6. The method according to claim 2, characterized in that, The calculation of the multi-aircraft state prediction envelope of the UAV includes: Based on the kinematic constraints of the UAV, the multi-aircraft state prediction envelope of the UAV is calculated within a future time window. The prediction envelope represents the set of spatiotemporal locations that the UAV may reach under all feasible control inputs.

7. A device for resolving local dynamic interlocks in unmanned aerial vehicle (UAV) traffic flow, characterized in that, include: The judgment module is used to determine whether a local dynamic interlock has occurred between the drones. If no local dynamic interlock has occurred between the drones, the drones are set to cruise mode. The judgment module is also used to set the drone to a hovering state in response to the occurrence of local dynamic interlock between the drones. The calculation module is used to calculate the minimum conflict resolution time slot between the hovering drones, and the minimum conflict resolution time slot is used to represent the minimum waiting time required to release the dynamic interlock; The update module is used to insert the calculated minimum conflict resolution time slot into the timeline of the original flight plan of the UAV, and update the expected arrival time of the UAV. The detection module is used to detect the local spatiotemporal saturation between UAVs when the minimum conflict resolution time slot ends; if the spatiotemporal saturation is greater than or equal to the spatiotemporal saturation threshold, the UAV continues to hover and the minimum conflict resolution time slot is recalculated. The detection module is also used to switch the UAV back to cruise mode and perform route network reconstruction if the spatiotemporal saturation is lower than the spatiotemporal saturation threshold.

8. A computing device, characterized in that, Its features include: Memory, used to store program instructions; A processor is configured to invoke program instructions stored in the memory and execute the method as described in any one of claims 1-6 according to the obtained program instructions.

9. A computer-readable storage medium, characterized in that, Includes computer-readable instructions that, when read and executed by a computer, cause the method as described in any one of claims 1 to 6 to be implemented.

10. A computer program product, characterized in that, It includes a computer program executable by a computer device, which, when run on the computer device, causes the computer device to perform the steps of the method according to any one of claims 1 to 6.

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