A method and system for regional route collaborative scheduling of logistics drones
By constructing a drone monitoring platform and a force field simulation obstacle avoidance model, the problems of computational complexity and communication bandwidth bottlenecks in large-scale logistics drone collaborative scheduling were solved, enabling efficient and real-time drone collaborative flight and obstacle avoidance.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHENZHEN ZHIHUI QICE TECH CO LTD
- Filing Date
- 2026-03-25
- Publication Date
- 2026-05-26
AI Technical Summary
In existing technologies, large-scale logistics drone regional route collaborative scheduling relies on centralized optimization algorithms, which leads to an explosive increase in the computational complexity of the central server and faces real-time computing power pressure and communication bandwidth bottlenecks.
By constructing a drone monitoring platform and utilizing a force field simulation obstacle avoidance model, distributed computing and autonomous obstacle avoidance can be achieved based on the drone's flight information and mission level, thereby reducing computational complexity and improving robustness.
It enables efficient and real-time obstacle avoidance for large-scale UAV cooperative flight, reduces the computational load on the central processing unit, and improves the robustness and scalability of the system.
Smart Images
Figure CN121903326B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) scheduling technology, and particularly relates to a regional route collaborative scheduling method and system for logistics UAVs. Background Technology
[0002] Drone scheduling refers to the unified management of multiple drones' task allocation, path planning, flight control, and resource coordination through an intelligent system to achieve efficient, safe, and orderly aerial operations. This process comprehensively considers factors such as mission requirements, airspace restrictions, battery life, weather conditions, and real-time dynamics, using algorithms to optimize flight plans, avoid conflicts, and support emergency response and data transmission. It is widely used in logistics distribution, inspection and monitoring, agricultural plant protection, and urban governance, and is one of the core supporting technologies for low-altitude economic operation.
[0003] In current drone scheduling processes, regional route collaborative scheduling for large-scale logistics drones mainly relies on centralized optimization algorithms. The scheduling center uniformly calculates and assigns precise trajectories to each drone. The computational complexity of the central server increases explosively with the number of drones, facing enormous real-time computing power pressure and communication bandwidth bottlenecks. Therefore, designing a large-scale drone collaborative scheduling method that is efficient, real-time, highly robust, and computationally lightweight has become an urgent technical challenge. Summary of the Invention
[0004] The purpose of this invention is to provide a regional route collaborative scheduling method for logistics drones, which aims to solve the problem that the computational complexity of the central server is exponentially increasing with the number of drones, resulting in huge real-time computing power pressure and communication bandwidth bottlenecks.
[0005] This invention is implemented as follows: a regional route collaborative scheduling method for logistics drones, the method comprising:
[0006] Construct a drone monitoring platform to obtain flight information of all drones in the current airspace. The flight information includes at least flight speed, flight path, real-time positioning, and mission level.
[0007] Based on the preset minimum interference distance, determine whether there is flight interference, obtain the flight information of the UAV involved in the flight interference, and send it to the corresponding UAV;
[0008] The drones calculate yaw information based on flight information and a built-in force field simulation obstacle avoidance model, and control each drone to fly based on the yaw information.
[0009] When the distance between drones is greater than the minimum interference distance, the return-to-home information is output based on the force field simulation obstacle avoidance model to control the drones to return to their flight path.
[0010] Preferably, the step of determining whether flight interference exists based on a preset minimum interference distance, obtaining the flight information of the UAVs involved in the flight interference, and sending it to the corresponding UAVs includes:
[0011] Traverse each drone, and set a spherical detection range with the drone as the center and the preset minimum interference distance as the radius;
[0012] Based on the positioning information of each drone, determine whether there are other drones and obtain the number of the drone within the spherical detection range;
[0013] The flight information of the drone is obtained based on its identification number, and the flight information is sent to each drone involved in the flight intervention.
[0014] Preferably, the step of calculating yaw information using the UAV based on flight information and a built-in force field simulation obstacle avoidance model, and controlling each UAV to fly based on the yaw information, includes:
[0015] Construct a force field simulation obstacle avoidance model, obtain flight information of the UAVs involved in the intervention, and extract the mission level of each UAV;
[0016] Based on the mission level of the UAV, construct the UAV force field simulation parameters and elastic regression parameters, and determine the simulated resultant force received by the UAV at this time based on the UAV force field simulation parameters and elastic regression parameters;
[0017] The yaw direction and yaw speed of each UAV are determined by the simulated resultant force, yaw information is generated, and the corresponding UAV is controlled to fly based on the yaw information.
[0018] Preferably, the step of outputting return-to-home information based on the force field simulation obstacle avoidance model and controlling the drones to return to their flight path when the distance between the drones is greater than the minimum interference distance includes:
[0019] In real time, determine whether the distance between drones is greater than the minimum interference distance. If so, determine that the drones have left the interference area and start controlling the drones to return to the initial path.
[0020] The return-to-home information of the current UAV is calculated based on the elastic regression parameters in the force field simulation obstacle avoidance model.
[0021] Based on the return-to-home information, the drone is controlled to gradually return to its initial path.
[0022] Preferably, the simulated resultant force is expressed as:
[0023]
[0024] in, To simulate the resultant force, For elastic regressive force, For the first to participate in the intervention The drone is the first The repulsive force of a drone.
[0025] Another object of the present invention is to provide a regional route collaborative scheduling system for logistics drones, the system comprising:
[0026] The data monitoring module is used to build a drone monitoring platform, which obtains flight information of all drones in the current airspace. The flight information includes at least flight speed, flight path, real-time positioning and mission level.
[0027] The interference determination module is used to determine whether flight interference exists based on a preset minimum interference distance, obtain the flight information of the UAVs involved in the flight interference, and send it to the corresponding UAVs.
[0028] The yaw control module is used to calculate yaw information for each UAV based on flight information and a built-in force field simulation obstacle avoidance model, and then control the flight of each UAV based on the yaw information.
[0029] The yaw return module is used to control the drones to return to their flight path when the distance between drones is greater than the minimum interference distance, based on the force field simulation obstacle avoidance model, and outputs return-to-home information.
[0030] Preferably, the interference determination module includes:
[0031] The detection area construction unit is used to traverse each drone and set a spherical detection range with the drone as the center and a preset minimum interference distance as the radius.
[0032] The drone determination unit is used to determine whether there are other drones based on the positioning information of each drone and to obtain the number of the drone within the spherical detection range.
[0033] The information interaction unit is used to obtain the flight information of the UAV based on its number and send the flight information to each UAV involved in the flight intervention.
[0034] Preferably, the yaw control module includes:
[0035] The parameter setting unit is used to construct a force field simulation obstacle avoidance model, obtain flight information of the UAVs participating in the interference, and extract the mission level of each UAV.
[0036] The simulation calculation unit is used to construct the drone force field simulation parameters and elastic regression parameters based on the drone's mission level, and to determine the simulated resultant force received by the drone at the current time based on the drone force field simulation parameters and elastic regression parameters.
[0037] The yaw control unit is used to determine the yaw direction and yaw speed of each UAV based on the simulated resultant force, generate yaw information, and control the flight of the corresponding UAV based on the yaw information.
[0038] Preferably, the yaw return module includes:
[0039] The interference determination unit is used to determine in real time whether the distance between drones is greater than the minimum interference distance. If so, it determines that the drone has left the interference area and starts to control the drone to return to the initial path.
[0040] The return-to-home calculation unit is used to calculate based on the elastic regression parameters in the force field simulation obstacle avoidance model and output the current return-to-home information of the UAV.
[0041] The return-to-home control unit is used to control the drone to gradually return to its initial path based on the return-to-home information.
[0042] Preferably, the simulated resultant force is expressed as:
[0043]
[0044] in, To simulate the resultant force, For elastic regressive force, For the first to participate in the intervention The drone is the first The repulsive force of a drone.
[0045] This invention provides a regional route collaborative scheduling method for logistics drones. It treats each drone as an intelligent agent moving autonomously in a three-dimensional force field, decoupling complex global path planning from real-time obstacle avoidance, thus greatly reducing the dimensionality and complexity of distributed computing. At the same time, by introducing quality parameters bound to task priority, it embodies the collaborative behavior of high-priority routes being stable and low-priority routes actively avoiding obstacles in distributed parallel computing. This not only ensures the timeliness of critical tasks but also enables efficient collision-free flight of large-scale drone fleets, significantly reducing the computational load on the central processing unit and improving the overall robustness and scalability of the system. Attached Figure Description
[0046] Figure 1 A flowchart of a regional route collaborative scheduling method for logistics drones provided in an embodiment of the present invention;
[0047] Figure 2 A flowchart illustrating the steps of determining whether flight interference exists based on a preset minimum interference distance, obtaining flight information of the UAV involved in the flight interference, and sending it to the corresponding UAV, as provided in this embodiment of the invention.
[0048] Figure 3The flowchart illustrates the steps of calculating yaw information using a UAV based on flight information and a built-in force field simulation obstacle avoidance model, and controlling each UAV to fly based on the yaw information, as provided in this embodiment of the invention.
[0049] Figure 4 A flowchart of the steps for controlling the UAVs to return to their flight path when the distance between UAVs is greater than the minimum interference distance, provided by an embodiment of the present invention;
[0050] Figure 5 This invention provides an architecture diagram of a regional route collaborative scheduling system for logistics drones.
[0051] Figure 6 This is an architecture diagram of an interference determination module provided in an embodiment of the present invention;
[0052] Figure 7 An architecture diagram of a yaw control module provided in an embodiment of the present invention;
[0053] Figure 8 This is an architecture diagram of a yaw regression module provided in an embodiment of the present invention;
[0054] Figure 9 This is a schematic diagram of yaw force calculation provided in an embodiment of the present invention. Detailed Implementation
[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.
[0056] like Figure 1 The diagram shows a flowchart of a regional route collaborative scheduling method for logistics drones provided by an embodiment of the present invention. The method includes:
[0057] S100, construct a drone monitoring platform to obtain flight information of all drones in the current airspace. The flight information includes at least flight speed, flight path, real-time positioning, and mission level.
[0058] In this step, a drone monitoring platform is constructed to comprehensively manage various drones in this field. The platform primarily provides data relay services. Each drone can only enter the current airspace with the permission of the monitoring platform. During flight, drones need to report their flight information in real time, including at least flight speed, flight path, real-time location, and mission level. The higher the mission level, the smaller the avoidance scale during flight; the lower the mission level, the larger the avoidance scale. The monitoring platform sets initial paths for each drone, and the drones will fly along these initial paths after startup.
[0059] S200 determines whether flight interference exists based on a preset minimum interference distance, acquires the flight information of the UAV involved in the flight interference, and sends it to the corresponding UAV.
[0060] In this step, the presence of flight interference is determined based on a preset minimum interference distance. During the drone's flight, the drone monitoring platform is responsible for determining whether there are other drones around each drone and whether there is a risk of collision. Specifically, a monitoring area is constructed based on the current drone and a preset safety range. If other drones are present in the monitoring area, flight interference is determined, and all drones located in the same monitoring area are included in the scope of this joint avoidance. If drone A and drone B belong to monitoring area 1, and drone B and drone C belong to monitoring area 2, then the two monitoring areas are merged into monitoring area 3. Drones A, B, and C are all drones in monitoring area 3, and the three groups of drones perform joint avoidance, obtaining the flight information of all drones in the same monitoring area and sending it to at least two drones in that monitoring area.
[0061] The S300 uses flight information and a built-in force field simulation obstacle avoidance model to calculate yaw information for each UAV, and then controls the flight of each UAV based on the yaw information.
[0062] In this step, the UAV calculates yaw information based on flight information and a built-in force field simulation obstacle avoidance model, constructing a three-dimensional force field space. The UAV's current position, flight speed, and direction are marked in this three-dimensional space. The UAV is considered as a sphere with mass, and spheres repel each other. The mass of the sphere is set according to the UAV's mission level; the higher the mission level, the larger the mass of the sphere corresponding to the UAV. The smaller the distance between the spheres, the greater the repulsive force between them. Simultaneously, an elastic return force is set based on the UAV's initial path. This elastic return force guides the UAV back to the initial path after obstacle avoidance, and its direction points towards the initial path. For a given UAV, the simulated net force is the force field exerted on it by other UAVs. The resultant force of the repulsive force and the elastic regressive force generated by the initial path is calculated. All forces are vectors. The component of this simulated resultant force in the plane perpendicular to the initial path is calculated. Based on this component and the mass of the UAV, the current yaw direction and yaw speed are determined to obtain yaw information. Based on this, the yaw information of each UAV is determined to independently control each UAV to fly until there are no other UAVs in the UAV's monitoring area. Then, the UAV is controlled to return to the initial path based solely on the elastic regressive force. During the avoidance and return process, the UAV monitoring platform determines whether the avoidance is successful based on the distance between the UAVs. If the distance between the UAVs is less than a preset value, the avoidance is determined to be a failure. The UAV monitoring platform then takes over or controls some UAVs to hover so that they can pass in turn.
[0063] When the distance between drones is greater than the minimum interference distance, the S400 outputs return-to-home information based on the force field simulation obstacle avoidance model, and controls the drones to return to their flight path.
[0064] In this step, when the distance between the drones is greater than the minimum interference distance, it can be determined that the obstacle avoidance has been completed. Then, the drone needs to be controlled to return to the initial path. At this time, in the force field simulation obstacle avoidance model, the force field repulsion force of other drones on the current drone disappears, and only the elastic return force exists. Based on the elastic return force, the drone is controlled to gradually approach the initial path and continue to fly along the initial path.
[0065] like Figure 2 As shown, in a preferred embodiment of the present invention, the step of determining whether flight interference exists based on a preset minimum interference distance, obtaining the flight information of the UAV involved in the flight interference, and sending it to the corresponding UAV includes:
[0066] S201: Traverse each drone and set a spherical detection range with the drone as the center and the preset minimum interference distance as the radius.
[0067] In this step, each drone is traversed, and one drone is selected at a time. A spherical detection zone is set with that drone as the center and a preset minimum interference distance as the radius. If other drones are present within this spherical detection zone, it indicates that there is a risk of collision.
[0068] S202, based on the positioning information of each UAV, determine whether there are other UAVs and obtain the number of the UAV within the spherical detection range.
[0069] In this step, the location information of each drone is used to determine whether there are other drones. If there are other drones, they are avoided and all drones located in the same spherical detection interval are grouped together. If two spherical detection intervals have overlapping drones, the spherical detection intervals are merged and the number of all drones in the spherical detection interval is determined.
[0070] S203 obtains the flight information of the UAV based on its serial number and sends the flight information to each UAV involved in the flight intervention.
[0071] In this step, the flight information of the drone is obtained based on its serial number. The drone's unique identity can be determined based on its serial number. The flight information of the drone can be obtained from the drone monitoring platform. The flight information of all drones in the same spherical detection range is synchronized to at least two drones. The two drones that receive the flight information serve as the computing center for this avoidance control.
[0072] like Figure 3 As shown, in a preferred embodiment of the present invention, the step of calculating yaw information by the UAV based on flight information and a built-in force field simulation obstacle avoidance model, and controlling each UAV to fly based on the yaw information, includes:
[0073] S301 constructs a force field simulation obstacle avoidance model, obtains flight information of the UAVs involved in the intervention, and extracts the mission level of each UAV.
[0074] In this step, a three-dimensional coordinate system is constructed, and the UAVs participating in the interference are marked in this three-dimensional coordinate system. The flight information of each UAV, including its flight path, is extracted and marked in the three-dimensional coordinate system. The mission level is also extracted, and the initial parameters of the force field simulation obstacle avoidance model are set based on the mission level.
[0075] S302, constructs UAV force field simulation parameters and elastic regression parameters based on the UAV's mission level, and determines the simulated resultant force received by the current UAV based on the UAV force field simulation parameters and elastic regression parameters.
[0076] In this step, the force field simulation parameters and elastic regression parameters of the UAV are constructed based on the UAV's mission level. The forces experienced by the UAV are mainly divided into two parts: one part is the force field repulsion force from other UAVs, and the other part is the elastic regression force from the initial path. The resultant force of multiple forces is the simulated resultant force experienced by the current UAV. The simulation mass of the UAV is set based on the UAV's mission level. The higher the mission level, the greater the corresponding simulation mass.
[0077] The elastic regression force is used to guide the drone back and follow the predetermined route, and is expressed as:
[0078]
[0079] In the formula, For elastic regressive force, The target point is located on the initial path. This is the current location of the drone. This represents the distance from the current location to the target point. The distance is a power of 3, set to 3. The gravitational coefficient, This is the velocity deviation vector, which is the difference between the current velocity and the desired flight velocity. The damping coefficient;
[0080] Based on this, the elastic regression force experienced by the drone from the initial path is calculated and used to guide the drone back to the initial path;
[0081] Current drones Received other drones Force field repulsion Represented as:
[0082]
[0083] in, Based on the basic repulsion coefficient, For drones The simulation quality, Positively correlated with the mission level of the drone The distance between the two drones This is the smoothing constant, which can be set to 0.1. For drones Real-time location, For drones Real-time location;
[0084] Based on this, we can deduce the drone. The simulated resultant force is :
[0085]
[0086] At least two UAVs that receive flight information complete the calculation of the simulated resultant force on each UAV within the detection area. That is, the simulated resultant force on the same UAV is calculated by each of the two UAVs at least once.
[0087] S303 determines the yaw direction and yaw speed of each UAV based on simulated resultant force, generates yaw information, and controls the flight of the corresponding UAV based on the yaw information.
[0088] In this step, the yaw direction and yaw speed of each UAV are determined based on the simulated resultant force. A yaw plane is constructed according to the initial path of each UAV. The yaw plane is always perpendicular to the initial path, and the current position of the UAV is located within this yaw plane. The component of the simulated resultant force for each UAV within the corresponding yaw plane is calculated, that is, the simulated resultant force is projected onto the yaw plane, and this component force is defined as the yaw force. Figure 9 As shown, yaw acceleration is calculated based on the simulated mass of the UAV and yaw force. The yaw acceleration is integrated to obtain the yaw speed. The yaw direction of the UAV is determined based on the yaw force. That is, the UAV only deviates from the initial path within the yaw plane. The flight speed of the UAV in the direction of the initial path is not affected by the yaw avoidance process. Based on this, the yaw information of each UAV is obtained. The yaw information of the same UAV is calculated at least twice. The yaw information is sent to the UAV monitoring center for comparison and verification. If the two are consistent, the yaw information is sent to each UAV through the UAV monitoring platform. The UAV then flies according to the yaw information, which can realize the automatic obstacle avoidance of the UAV. Moreover, the higher the mission level of the UAV, the smaller its obstacle avoidance deviation, so as to prioritize its flight stability.
[0089] like Figure 4 As shown, in a preferred embodiment of the present invention, the step of outputting return-to-home information based on a force field simulation obstacle avoidance model and controlling the drones to return to their flight path when the distance between the drones is greater than the minimum interference distance includes:
[0090] S401 determines in real time whether the distance between drones is greater than the minimum interference distance. If so, it determines that the drones have left the interference area and begins to control them to return to the initial path.
[0091] In this step, it is determined in real time whether the distance between the drones is greater than the minimum interference distance. If it is, it means that there is no risk of collision between the drones and the drones can start returning to the initial path. Otherwise, it means that there is still a risk of collision between the drones and the obstacle avoidance operation needs to continue.
[0092] S402 calculates and outputs the current UAV's return-to-home information based on the elastic regression parameters in the force field simulation obstacle avoidance model.
[0093] S403 controls the drone to gradually return to its initial path based on return-to-home information.
[0094] In this step, the calculation is performed based on the elastic regression parameters in the force field simulation obstacle avoidance model. At this time, other drones do not exert force repulsion on this drone, so the simulated resultant force equals the elastic regression force, expressed as:
[0095]
[0096] Similarly, return-to-home information is generated based on the components of the current simulated resultant force in the yaw plane, which is used to control the UAV to return to the initial path.
[0097] like Figure 5 As shown, an embodiment of the present invention provides a regional route collaborative scheduling system for logistics drones, the system comprising:
[0098] The data monitoring module 100 is used to build a drone monitoring platform. The drone monitoring platform obtains the flight information of all drones in the current airspace. The flight information includes at least flight speed, flight path, real-time positioning and mission level.
[0099] In this system, the data supervision module 100 constructs a drone supervision platform. Based on the drone supervision platform, it comprehensively manages various drones in this field, mainly providing data relay services. Each drone can only enter the current airspace with the permission of the drone supervision platform. During flight, the drone needs to report its flight information in real time. The flight information includes at least flight speed, flight path, real-time positioning, and mission level. The higher the mission level, the smaller the avoidance scale during flight, and the lower the mission level, the larger the avoidance scale during flight. The drone supervision platform sets an initial path for each drone, and the drone will fly along the initial path after startup.
[0100] The interference determination module 200 is used to determine whether flight interference exists based on a preset minimum interference distance, obtain the flight information of the UAVs involved in the flight interference, and send it to the corresponding UAVs.
[0101] In this system, the interference determination module 200 determines whether flight interference exists based on a preset minimum interference distance. During the flight of the UAV, the UAV monitoring platform is responsible for determining whether there are other UAVs around each UAV and whether there is a risk of collision. Specifically, a monitoring area is constructed based on the current UAV and a preset safety range. If other UAVs are present in the monitoring area, flight interference is determined to exist. All UAVs located in the same monitoring area are included in the scope of joint avoidance. If UAV A and UAV B belong to the same monitoring area 1, and UAV B and UAV C belong to the same monitoring area 2, the two monitoring areas are merged into monitoring area 3. UAVs A, B, and C are all UAVs in monitoring area 3. The three groups of UAVs perform joint avoidance, obtain the flight information of all UAVs in the same monitoring area, and send it to at least two UAVs in the same monitoring area.
[0102] The yaw control module 300 is used to calculate yaw information for each UAV based on flight information and a built-in force field simulation obstacle avoidance model, and to control the flight of each UAV based on the yaw information.
[0103] In this system, the yaw control module 300 calculates yaw information based on the UAV's flight information and the built-in force field simulation obstacle avoidance model, constructing a three-dimensional force field space. The UAV's current position, flight speed, and direction are marked in this three-dimensional space. The UAV is considered a sphere with mass, and spheres repel each other. The mass of the sphere is set according to the UAV's mission level; the higher the mission level, the larger the mass of the sphere corresponding to the UAV. The smaller the distance between the spheres, the greater the repulsive force. Simultaneously, an elastic return force is set based on the UAV's initial path. This elastic return force guides the UAV back to the initial path after obstacle avoidance, and its direction points towards the initial path. For a given UAV, the simulated net force is the force exerted by other UAVs on it. The resultant force of the generated repulsive force and the elastic regressive force generated by the initial path is a vector force. The component of this simulated resultant force in the plane perpendicular to the initial path is calculated. Based on this component and the mass of the UAV, the current yaw direction and yaw speed are determined to obtain yaw information. Based on this, the yaw information of each UAV is determined to independently control each UAV to fly until there are no other UAVs in the UAV's monitoring area. Then, the UAV is controlled to return to the initial path based solely on the elastic regressive force. During the avoidance and return process, the UAV monitoring platform determines whether the avoidance is successful based on the distance between the UAVs. If the distance between the UAVs is less than a preset value, the avoidance is determined to be a failure. The UAV monitoring platform then takes over or controls some UAVs to hover so that they can pass in turn.
[0104] The yaw return module 400 is used to output return-to-home information based on a force field simulation obstacle avoidance model when the distance between UAVs is greater than the minimum interference distance, and to control the UAVs to return to their flight path.
[0105] In this system, the yaw return module 400 can determine that the obstacle avoidance has been completed when the distance between the UAVs is greater than the minimum interference distance. Then, it needs to control the UAV to return to the initial path. At this time, in the force field simulation obstacle avoidance model, the force field repulsion force of other UAVs on the current UAV disappears, and only the elastic return force exists. Based on the elastic return force, the UAV is controlled to gradually approach the initial path and continue to fly along the initial path.
[0106] like Figure 6 As shown, in a preferred embodiment of the present invention, the interference determination module 200 includes:
[0107] The detection area construction unit 201 is used to traverse each UAV and set a spherical detection range with the UAV as the center and a preset minimum interference distance as the radius.
[0108] In this module, the detection area construction unit 201 traverses each UAV, selects one UAV at a time, and sets a spherical detection range with the UAV as the center and a preset minimum interference distance as the radius. If there are other UAVs in the spherical detection range, it indicates that there is a risk of collision.
[0109] The drone determination unit 202 is used to determine whether there are other drones based on the positioning information of each drone and to obtain the number of the drone within the spherical detection range.
[0110] In this module, the UAV determination unit 202 determines whether there are other UAVs based on the positioning information of each UAV. If there are other UAVs, it avoids them and groups all UAVs located in the same spherical detection interval together. If there are UAVs corresponding to two spherical detection intervals that overlap, the above-mentioned spherical detection intervals are merged, and the number of all UAVs in the spherical detection interval is determined.
[0111] The information interaction unit 203 is used to obtain the flight information of the UAV based on its number and send the flight information to each UAV involved in the flight intervention.
[0112] In this module, the information interaction unit 203 obtains the flight information of the UAV based on its number. The unique identity of the UAV can be determined based on its number. The flight information of the UAV can be obtained from the UAV monitoring platform. The flight information of all UAVs in the same spherical detection range is synchronized to at least two UAVs. The two UAVs that receive the flight information serve as the calculation center for this avoidance control.
[0113] like Figure 7 As shown, in a preferred embodiment of the present invention, the yaw control module 300 includes:
[0114] The parameter setting unit 301 is used to construct a force field simulation obstacle avoidance model, obtain the flight information of the UAVs participating in the interference, and extract the mission level of each UAV.
[0115] In this module, the parameter setting unit 301 constructs a three-dimensional coordinate system, marks the UAVs participating in the interference in the three-dimensional coordinate system, extracts the flight information of each UAV, including the flight path, marks it in the three-dimensional coordinate system, extracts the mission level, and sets the initial parameters of the force field simulation obstacle avoidance model based on the mission level.
[0116] The simulation calculation unit 302 is used to construct the UAV force field simulation parameters and elastic regression parameters based on the UAV's mission level, and to determine the simulated resultant force received by the UAV at the current time based on the UAV force field simulation parameters and elastic regression parameters.
[0117] In this module, the simulation calculation unit 302 constructs the drone force field simulation parameters and elastic regression parameters based on the drone's mission level. The forces experienced by the drone are mainly divided into two parts: one part is the force field repulsion force from other drones, and the other part is the elastic regression force from the initial path. The resultant force of multiple forces is the simulated resultant force experienced by the current drone. The simulation mass is set for the drone based on its mission level. The higher the mission level, the greater the corresponding simulation mass.
[0118] The elastic regression force is used to guide the drone back and follow the predetermined route, and is expressed as:
[0119]
[0120] In the formula, For elastic regressive force, The target point is located on the initial path. This is the current location of the drone. This represents the distance from the current location to the target point. The distance is a power of 3, set to 3. The gravitational coefficient, This is the velocity deviation vector, which is the difference between the current velocity and the desired flight velocity. The damping coefficient;
[0121] Based on this, the elastic regression force experienced by the drone from the initial path is calculated and used to guide the drone back to the initial path;
[0122] Current drones Received other drones Force field repulsion Represented as:
[0123]
[0124] in, Based on the basic repulsion coefficient, For drones The simulation quality, Positively correlated with the mission level of the drone The distance between the two drones This is the smoothing constant, which can be set to 0.1. For drones Real-time location, For drones Real-time location;
[0125] Based on this, we can deduce the drone. The simulated resultant force is :
[0126]
[0127] At least two UAVs that receive flight information complete the calculation of the simulated resultant force on each UAV within the detection area. That is, the simulated resultant force on the same UAV is calculated by each of the two UAVs at least once.
[0128] The yaw control unit 303 is used to determine the yaw direction and yaw speed of each UAV based on the simulated resultant force, generate yaw information, and control the flight of the corresponding UAV based on the yaw information.
[0129] In this module, the yaw control unit 303 determines the yaw direction and yaw speed of each UAV based on the simulated resultant force. A yaw plane is constructed based on the initial path of each UAV, always perpendicular to the initial path, and the current position of the UAV lies within this yaw plane. The component of the simulated resultant force for each UAV within the corresponding yaw plane is calculated; that is, the simulated resultant force is projected onto the yaw plane, and this component is defined as the yaw force. Based on the simulated mass of the UAV and the yaw force, the yaw acceleration is calculated. Integrating the yaw acceleration yields the yaw speed. The yaw force determines the yaw direction and yaw speed of each UAV. The yaw direction of the drone, that is, the drone deviates from the initial path only within the yaw plane. The drone's flight speed in the direction of the initial path is not affected by the yaw avoidance process. Based on this, the yaw information of each drone is obtained. The yaw information of the same drone is calculated at least twice. The yaw information is sent to the drone monitoring center for comparison and verification. If the two are consistent, the yaw information is sent to each drone through the drone monitoring platform. The drone then flies according to the yaw information, which can realize the drone's automatic obstacle avoidance. Moreover, the higher the mission level of the drone, the smaller its obstacle avoidance deviation, so as to prioritize its flight stability.
[0130] like Figure 8 As shown, in a preferred embodiment of the present invention, the yaw return module 400 includes:
[0131] The interference determination unit 401 is used to determine in real time whether the distance between the drones is greater than the minimum interference distance. If so, it determines that the drones have left the interference area and starts to control the drones to return to the initial path.
[0132] In this module, the interference determination unit 401 determines in real time whether the distance between the drones is greater than the minimum interference distance. If it is, it means that there is no risk of collision between the drones and the drones can start returning to the initial path. Otherwise, it means that there is still a risk of collision between the drones and the obstacle avoidance operation needs to continue.
[0133] The return-to-home calculation unit 402 is used to calculate based on the elastic regression parameters in the force field simulation obstacle avoidance model and output the current return-to-home information of the UAV.
[0134] The return-to-home control unit 403 is used to control the drone to gradually return to its initial path based on the return-to-home information.
[0135] In this module, the elastic regression parameters in the force field simulation obstacle avoidance model are used for calculation. At this point, other drones do not exert any force repulsion on this drone, so the simulated resultant force equals the elastic regression force, expressed as:
[0136]
[0137] Similarly, return-to-home information is generated based on the components of the current simulated resultant force in the yaw plane, which is used to control the UAV to return to the initial path.
[0138] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A regional route collaborative scheduling method for logistics drones, characterized in that, The method includes: Construct a drone monitoring platform to obtain flight information of all drones in the current airspace. The flight information includes at least flight speed, flight path, real-time positioning, and mission level. Based on the preset minimum interference distance, determine whether there is flight interference, obtain the flight information of the UAV involved in the flight interference, and send it to the corresponding UAV; The drones calculate yaw information based on flight data and a built-in force field simulation obstacle avoidance model, and then control each drone to fly based on the yaw information. Construct a force field simulation obstacle avoidance model, obtain flight information of the UAVs involved in the intervention, and extract the mission level of each UAV; Based on the mission level of the UAV, construct the UAV force field simulation parameters and elastic regression parameters, and determine the simulated resultant force received by the UAV at this time based on the UAV force field simulation parameters and elastic regression parameters; Based on the drone's mission level, simulated parameters of the drone's force field and elastic regression parameters are constructed. The forces experienced by the drone are divided into two parts: one part is the repulsive force from the force field of other drones, and the other part is the elastic regression force from the initial path. The resultant force of multiple forces is the simulated resultant force experienced by the current drone. The simulation mass of the drone is set based on its mission level. The higher the mission level, the greater the corresponding simulation mass. The elastic regression force is used to guide the drone back and follow the predetermined route, and is expressed as: ； In the formula, For elastic regressive force, The target point is located on the initial path. This is the current location of the drone. This represents the distance from the current location to the target point. The distance is a power of 3, set to 3. The gravitational coefficient, This is the velocity deviation vector, which is the difference between the current velocity and the desired flight velocity. The damping coefficient; Based on this, the elastic regression force experienced by the drone from the initial path is calculated and used to guide the drone back to the initial path; Current drones Received other drones Force field repulsion Represented as: ； in, Based on the basic repulsion coefficient, For drones The simulation quality, Positively correlated with the mission level of the drone The distance between the two drones The smoothing constant is set to 0.
1. For drones Real-time location, For drones Real-time location; Based on this, we can deduce the drone. The simulated resultant force is : ； At least two UAVs that receive flight information complete the calculation of the simulated resultant force on each UAV within the detection area; that is, the simulated resultant force on the same UAV is calculated by each of the two UAVs at least once. Based on the simulated resultant force, the yaw direction and yaw speed of each UAV are determined, yaw information is generated, and the flight of the corresponding UAV is controlled based on the yaw information; When the distance between drones is greater than the minimum interference distance, the return-to-home information is output based on the force field simulation obstacle avoidance model to control the drones to return to their flight path.
2. The regional route collaborative scheduling method for logistics drones according to claim 1, characterized in that, The steps of determining whether flight interference exists based on a preset minimum interference distance, obtaining flight information of the UAVs involved in the flight interference, and sending it to the corresponding UAVs include: Traverse each drone, and set a spherical detection range with the drone as the center and the preset minimum interference distance as the radius; Based on the positioning information of each drone, determine whether there are other drones and obtain the number of the drone within the spherical detection range; The flight information of the drone is obtained based on its identification number, and the flight information is sent to each drone involved in the flight intervention.
3. The regional route collaborative scheduling method for logistics drones according to claim 1, characterized in that, The step of controlling the drones to return to their flight path by outputting return-to-home information based on a force field simulation obstacle avoidance model when the distance between the drones is greater than the minimum interference distance includes: In real time, determine whether the distance between drones is greater than the minimum interference distance. If so, determine that the drones have left the interference area and start controlling the drones to return to the initial path. The return-to-home information of the current UAV is calculated based on the elastic regression parameters in the force field simulation obstacle avoidance model. Based on the return-to-home information, the drone is controlled to gradually return to its initial path.
4. A regional route collaborative scheduling system for logistics drones, characterized in that, The system includes: The data monitoring module is used to build a drone monitoring platform, which obtains flight information of all drones in the current airspace. The flight information includes at least flight speed, flight path, real-time positioning and mission level. The interference determination module is used to determine whether flight interference exists based on a preset minimum interference distance, obtain the flight information of the UAVs involved in the flight interference, and send it to the corresponding UAVs. The yaw control module is used to calculate yaw information for each UAV based on flight data and a built-in force field simulation obstacle avoidance model, and then control the flight of each UAV based on the yaw information. Construct a force field simulation obstacle avoidance model, obtain flight information of the UAVs involved in the intervention, and extract the mission level of each UAV; Based on the mission level of the UAV, construct the UAV force field simulation parameters and elastic regression parameters, and determine the simulated resultant force received by the UAV at this time based on the UAV force field simulation parameters and elastic regression parameters; Based on the drone's mission level, simulated parameters of the drone's force field and elastic regression parameters are constructed. The forces experienced by the drone are divided into two parts: one part is the repulsive force from the force field of other drones, and the other part is the elastic regression force from the initial path. The resultant force of multiple forces is the simulated resultant force experienced by the current drone. The simulation mass of the drone is set based on its mission level. The higher the mission level, the greater the corresponding simulation mass. The elastic regression force is used to guide the drone back and follow the predetermined route, and is expressed as: ； In the formula, For elastic regressive force, The target point is located on the initial path. This is the current location of the drone. This represents the distance from the current location to the target point. The distance is a power of 3, set to 3. The gravitational coefficient, This is the velocity deviation vector, which is the difference between the current velocity and the desired flight velocity. The damping coefficient; Based on this, the elastic regression force experienced by the drone from the initial path is calculated and used to guide the drone back to the initial path; Current drones Received other drones Force field repulsion Represented as: ； in, Based on the basic repulsion coefficient, For drones The simulation quality, Positively correlated with the mission level of the drone The distance between the two drones The smoothing constant is set to 0.
1. For drones Real-time location, For drones Real-time location; Based on this, we can deduce the drone. The simulated resultant force is : ； At least two UAVs that receive flight information complete the calculation of the simulated resultant force on each UAV within the detection area; that is, the simulated resultant force on the same UAV is calculated by each of the two UAVs at least once. Based on the simulated resultant force, the yaw direction and yaw speed of each UAV are determined, yaw information is generated, and the flight of the corresponding UAV is controlled based on the yaw information; The yaw return module is used to control the drones to return to their flight path when the distance between drones is greater than the minimum interference distance, based on the force field simulation obstacle avoidance model, and outputs return-to-home information.
5. The regional route collaborative scheduling system for logistics drones according to claim 4, characterized in that, The interference determination module includes: The detection area construction unit is used to traverse each drone and set a spherical detection range with the drone as the center and a preset minimum interference distance as the radius. The drone determination unit is used to determine whether there are other drones based on the positioning information of each drone and to obtain the number of the drone within the spherical detection range. The information interaction unit is used to obtain the flight information of the UAV based on its number and send the flight information to each UAV involved in the flight intervention.
6. The regional route collaborative scheduling system for logistics drones according to claim 4, characterized in that, The yaw return module includes: The interference determination unit is used to determine in real time whether the distance between drones is greater than the minimum interference distance. If so, it determines that the drone has left the interference area and starts to control the drone to return to the initial path. The return-to-home calculation unit is used to calculate based on the elastic regression parameters in the force field simulation obstacle avoidance model and output the current return-to-home information of the UAV. The return-to-home control unit is used to control the drone to gradually return to its initial path based on the return-to-home information.