Control method and system of mobile unmanned aerial vehicle hangar

Abstract

The invention discloses a control method and system of a mobile unmanned aerial vehicle hangar, the hangar is fixedly installed on an inspection vehicle, and the method comprises the following steps: dividing an operation area; the unmanned aerial vehicle flies to an operation area; generating a hangar prediction position; generating an unmanned aerial vehicle planning path; the unmanned aerial vehicle executes the operation task along the unmanned aerial vehicle planning path; correcting the predicted position of the hangar and the planned path of the unmanned aerial vehicle; and the unmanned aerial vehicle returns to the hangar after completing operation. According to the control method and system of the mobile unmanned aerial vehicle hangar, the task area is decomposed into a plurality of unit areas, the predicted position of the hangar is corrected when the unmanned aerial vehicle executes the operation tasks through the predicted position of the hangar or the target position input by the personnel, the planned path of the unmanned aerial vehicle is generated, and therefore the moving path of the unmanned aerial vehicle in the remaining operation tasks is updated; compared with an existing device, the problem that the endurance of the unmanned aerial vehicle is affected by position change of the mobile hangar is solved, and the return flight efficiency and practicability are improved.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) technology, and particularly relates to a control method and system for a mobile UAV hangar. Background Technology

[0002] The global market for drones has grown significantly in recent years and has become an important tool for commercial, government, and consumer applications. They can support solutions in many fields and are widely used in construction, oil and gas, energy, agriculture, disaster relief, and other sectors.

[0003] Currently, drones take off from a hangar, fly to the work area, begin operations, and then fly back to the hangar after completing their tasks. However, with the increasing prevalence of vehicle-mounted mobile drone hangars, the hangars move with the inspection vehicle during operations. The change in the hangar's position before and after the operation may result in the drone being located far from the hangar at the end of the operation, affecting the drone's endurance and return efficiency.

[0004] To address these issues, we propose a control method and system for mobile unmanned aerial vehicle (UAV) hangars. Summary of the Invention

[0005] The purpose of this invention is to solve the problem in the prior art that the change of the location of mobile hangar affects the endurance and return efficiency of drones, and to propose a control method and system for mobile drone hangars.

[0006] The first aspect of this application provides a control method for a mobile unmanned aerial vehicle (UAV) hangar, comprising the following steps: S1: dividing the work area and moving the hangar to the vicinity of the work area; S2: the UAV takes off from the hangar and flies to the work area; S3: generating a predicted hangar position based on the hangar's moving direction and speed, combined with the time required for the UAV operation; S4: generating a planned path for the UAV based on the predicted hangar position or a target position input by the user; S5: the UAV performs the operation task along the planned path; S6: correcting the predicted hangar position based on the hangar's real-time moving direction and speed, and correcting the planned UAV path based on the hangar's real-time predicted position; S7: the UAV completes the operation and flies back into the hangar.

[0007] Preferably, step S1 includes the following steps: S11: The operator divides the work area; S12: Based on the geometry and obstacle information of the work area, the work area is decomposed into multiple unit areas C_i (i=1,2,...,n) using a grid method or convex decomposition algorithm; S13: The hangar is moved to the vicinity of the work area, and the nearest unit area is taken as the starting area C_start.

[0008] Preferably, in step S2, the drone takes off from the hangar and flies along a straight path to the starting area C_start.

[0009] Preferably, step S3 includes the following steps: S31: Obtain the real-time movement direction vector v_t, velocity s_t, and estimated remaining operation time T_remaining of the hangar at time t, which together constitute the state vector State_t=(p_t,v_t,s_t,T_remaining), where p_t is the current position of the hangar; S32: Based on the state vector, calculate the predicted position P_predicted of the hangar using a linear motion prediction model, with the following formula:

[0010] P_predicted=p_t+(v_t / ||v_t||)*s_t*T_remaining

[0011] Where ||v_t|| is the magnitude of the direction vector, used for normalization; S33: output hangar predicted position P_predicted.

[0012] Preferably, in step S4, A*-based sequence planning is used to generate a path Path={C_start,C_i,...,P_goal} that starts from the starting region C_start, sequentially visits all unit regions C_i, and finally reaches the destination location P_goal (i.e., the hangar prediction location or the personnel input location);

[0013] The path cost function f(n) is: f(n) = g(n) + h(n);

[0014] Where: g(n) is the actual cost from the starting region C_start to the unit region C_i, and h(n) is the heuristically estimated cost from the unit region C_i to the destination location P_goal.

[0015] Preferably, step S6 includes: S61: repeating step S3 to obtain a new predicted position P_new, and updating the library predicted position by ΔP; S62: if ΔP is greater than a preset threshold, then taking the current unit area of ​​the UAV as the starting point and the updated hangar predicted position as the ending point, regenerating a movement path covering the remaining unit area.

[0016] Preferably, in step S7, the return path of the UAV to the hangar uses the Dynamic Window Method (DWA) for local real-time obstacle avoidance and trajectory smoothing.

[0017] Preferably, the method further includes step S8: during the operation of the drone, the drone battery level E_current is monitored in real time. If the battery level is lower than the safety threshold E_safe, the operation is immediately interrupted and the drone returns to base.

[0018] Preferably, the formula for calculating the safety threshold E_safe in step S8 is:

[0019] E_safe=k*(P_return / v_avg)*P_power

[0020] Where: P_return is the distance from the drone's current position to the hangar's real-time position, v_avg is the drone's average return speed, P_power is the drone's average power during flight, and k is the safety factor.

[0021] The second aspect of this application provides a control system for a mobile unmanned aerial vehicle (UAV) hangar, characterized in that it includes: a UAV, a hangar, and an inspection vehicle, wherein the hangar is fixedly installed on the inspection vehicle, the UAV and the hangar are detachably connected, and the inspection vehicle includes a processor, which invokes instructions to implement the operation of any of the methods described in the first aspect embodiment.

[0022] In summary, the technical effects and advantages of this invention are as follows: The control method and system for the mobile drone hangar decomposes the task area into multiple unit areas. By predicting the hangar's location or inputting the target location by personnel, a planned path for the drone is generated. When the drone is performing its task, the predicted hangar location is corrected, thereby updating the drone's movement path for the remaining tasks. Compared with existing devices, this avoids the problem of the mobile hangar's location changing affecting the drone's endurance, and improves return-to-home efficiency and practicality. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the steps of the present invention;

[0024] Figure 2 The planned path for the drone during normal operation;

[0025] Figure 3 Plan paths for drones when the hangar is stationary or moving within a small area;

[0026] Figure 4 To correct the drone's planned path after the hangar's predicted location. Detailed Implementation

[0027] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0028] Reference Figure 1-4A control method for a mobile drone hangar includes the following steps: S1: Divide the work area and move the hangar to the vicinity of the work area; S2: The drone takes off from the hangar and flies to the work area; S3: Based on the hangar's moving direction and speed, and combined with the time required for the drone's operation, generate the predicted hangar position; S4: Generate the drone's planned path through the predicted hangar position or the target location input by the user; S5: The drone performs the operation task along the drone's planned path; S6: Based on the hangar's real-time moving direction and speed, correct the predicted hangar position, and based on the hangar's real-time predicted position, correct the drone's planned path; S7: The drone completes the operation and flies back into the hangar.

[0029] Step S1 includes the following steps: S11: The operator manually draws or defines the target work area by inputting geographic coordinates through the graphical interface of the inspection vehicle control station. The hangar moves with the inspection vehicle to the vicinity of the work area. S12: Based on the geometry and obstacle information of the work area, the work area is decomposed into multiple unit areas C_i (i=1,2,...,n) using either a grid method or a convex decomposition algorithm. Wherein: Grid method: A fixed-resolution grid is superimposed on the work area, and each grid cell is a unit area. This method is simple and intuitive, suitable for regular areas, but may have redundancy. Convex decomposition algorithm: Complex non-convex polygonal work areas are divided into multiple small, non-overlapping convex polygonal unit areas. This method can generate cells that better match the shape of the area, which is beneficial for planning efficient flight paths and avoiding blind spots for the UAV at convex corners.

[0030] During the decomposition of the work area, the system reads preset or real-time obstacle information (such as buildings and high-voltage lines) and marks the areas occupied by obstacles as "non-workable areas," excluding them from the valid areas to be decomposed. S13: The hangar moves to the vicinity of the work area and uses the nearest unit area as the starting area C_start.

[0031] In step S2, the system calculates the Euclidean distance between the center point of all unit areas and the current position of the hangar, and sets the unit area C_start with the smallest distance as the starting point of the UAV operation, so as to minimize the empty flight distance of the UAV from takeoff to the start of operation. The UAV takes off from the hangar and flies along a straight path to the starting area C_start.

[0032] Before takeoff, the drone performs a status self-check and data synchronization with the hangar (e.g., obtaining the latest hangar location and work area map). After takeoff, the drone climbs to a safe altitude and then, based on a positioning system such as GPS / BDS, plans a straight path from the hangar to C_start, and flies autonomously along this path. During flight, an onboard obstacle avoidance system is used to deal with sudden obstacles. Onboard obstacle avoidance systems are relatively mature and common in the drone field, and will not be elaborated on further.

[0033] Step S3 includes the following steps: S31: The hangar acquires its own pose information in real time through the vehicle-mounted inertial navigation unit, GPS, and other sensors of the inspection vehicle. Based on the number of remaining unvisited unit areas, the estimated operation time of each area, and the transfer time between areas, the total remaining operation time T_remaining is estimated, which together constitute the state vector State_t=(p_t,v_t,s_t,T_remaining), where p_t is the current position of the hangar; S32: Based on the state vector, the predicted position P_predicted of the hangar is calculated using a linear motion prediction model. The calculation formula is:

[0034] P_predicted = p_t + (v_t / ||v_t||) * s_t * T_remaining; where ||v_t|| is the magnitude of the direction vector, used for normalization;

[0035] This model is computationally efficient and is the preferred choice for most scenarios. For hangars with complex motion patterns, more advanced prediction models (such as Kalman filtering) can be used as alternatives. S33: Outputs the predicted hangar position P_predicted. The generated P_predicted will be used as the dynamically changing endpoint coordinates in the UAV's global path planning. Personnel can also directly input the predetermined destination in the inspection vehicle control station, in which case P_predicted will always be equal to the coordinates of the predetermined destination.

[0036] In step S4, A*-based sequence planning is used to generate a path Path={C_start,C_i,...,P_goal} that starts from the starting region C_start, visits all unit regions C_i in sequence, and finally reaches the destination location P_goal (i.e., the predicted hangar location or the personnel input location).

[0037] The path cost function f(n) is: f(n) = g(n) + h(n);

[0038] Where: g(n) is the actual cost from the starting region C_start to the unit region C_i, and h(n) is the heuristically estimated cost from the unit region C_i to the destination location P_goal.

[0039] In step S5, the drone automatically flies according to the planned path. When it enters the perception range of each unit area C_i, the drone adjusts its attitude (such as hovering, circling, or passing at a constant speed) and triggers its onboard payloads (such as high-definition cameras, multispectral sensors, and lidar) to collect data. At the same time, the drone continuously transmits its status (position, battery level, image data) back to the inspection vehicle control station.

[0040] Step S6 includes: S61: Repeat step S3 to obtain a new predicted position P_new, and update the hangar predicted position by ΔP. The system repeats the prediction process at a fixed frequency (e.g., once per minute). The update amount ΔP = ||P_new - P_old||, which is the Euclidean distance between the new and old predicted positions. S62: If ΔP is greater than a preset threshold, a new movement path covering the remaining unit area is generated, starting from the current unit area of ​​the UAV and ending at the updated hangar predicted position. The threshold is set based on the UAV's endurance, positioning accuracy, and safety margin (e.g., 50 meters). When ΔP exceeds this threshold, it indicates that the hangar movement has significantly changed the return-to-home environment, and continuing to execute the original path will result in an excessively long return-to-home distance or failure to land. At this time, the system will immediately interrupt the current path and, starting from the UAV's current position C_i and ending at P_new, re-execute the path planning in step S4 for the remaining unvisited unit area set to generate a new, globally better operation path Path.

[0041] In step S7, the return path of the UAV to the hangar employs the Dynamic Window (DWA) method for local real-time obstacle avoidance and trajectory smoothing. When the UAV receives the return command or automatically completes all its tasks, it first flies towards the hangar's real-time location. During the final approach phase, the DWA algorithm begins to operate. A dynamic window is formed around the UAV's current speed, simulating various feasible speed combinations (linear and angular velocities) within a short timeframe. Each speed combination is evaluated, and by selecting the speed with the highest overall evaluation, DWA can generate a smooth and safe trajectory in real time, enabling the UAV to flexibly avoid suddenly appearing obstacles (such as pedestrians and vehicles) and land smoothly and accurately on the moving hangar platform.

[0042] A control method for a mobile drone hangar further includes step S8: during drone operation, the drone battery level E_current is monitored in real time; if the battery level is lower than the safety threshold E_safe, the operation is immediately interrupted and the drone returns to base.

[0043] The formula for calculating the safety threshold E_safe in step S8 is:

[0044] E_safe=k*(P_return / v_avg)*P_power

[0045] Where: P_return is the distance from the drone's current position to the hangar's real-time position, v_avg is the drone's average return speed, P_power is the drone's average power during flight, and k is the safety factor. The system continuously compares E_current and E_safe. Once E_current ≤ E_safe, the system will immediately trigger the highest priority emergency return procedure, ignoring the current operation progress and directly calling step S7 to ensure that the drone has enough energy to safely return to the hangar.

[0046] A control system for a mobile drone hangar based on any of the above embodiments is characterized by comprising: a drone, a hangar, and an inspection vehicle. The hangar is fixedly mounted on the inspection vehicle, and the drone and the hangar are detachably connected. A control station is located inside the inspection vehicle, and the control station includes a processor. The processor invokes instructions to implement the operation of the aircraft operation method based on the mobile hangar in any of the above embodiments. Communication between the drone and the control station is achieved through long-distance communication technologies such as 4G / 5G cellular networks and radio data transmission. Communication between the drone and the hangar is achieved through short-range communication technologies such as Wi-Fi, Bluetooth, and UWB. The relative distance between the hangar and the control station is stable and can be achieved through wired or wireless connections.

[0047] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0048] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A control method for a mobile unmanned aerial vehicle (UAV) hangar, characterized in that, The process includes the following steps: S1: Divide the work area and move the hangar to the vicinity of the work area; S2: The drone takes off from the hangar and flies to the work area; S3: Based on the direction and speed of the hangar's movement and the time required for the drone's operation, generate the predicted location of the hangar; S4: Generate the drone's planned path based on the predicted location of the hangar or the destination location input by the personnel. S5: The drone executes its mission along the planned drone path; S6： Based on the real-time moving direction and speed of the hangar, the predicted position of the hangar is corrected, and based on the real-time predicted position of the hangar, the planned path of the UAV is corrected. S7: The drone has completed its operation and returned to the hangar.

2. The control method for a mobile unmanned aerial vehicle hangar according to claim 1, characterized in that, S1 includes the following steps: S11: The operator divides the work area; S12: Based on the geometry and obstacle information of the work area, the work area is decomposed into multiple unit areas C_i (i=1,2,...,n) using the grid method or convex decomposition algorithm; S13: The hangar is moved to the vicinity of the work area, and the nearest unit area is taken as the starting area C_start.

3. The control method for a mobile unmanned aerial vehicle hangar according to claim 1, characterized in that, In S2, the drone takes off from the hangar and flies along a straight path to the starting area C_start.

4. The control method for a mobile unmanned aerial vehicle hangar according to claim 1, characterized in that, S3 includes the following steps: S31: Obtain the real-time movement direction vector v_t, velocity s_t, and estimated remaining operation time T_remaining of the hangar at time t, which together constitute the state vector State_t=(p_t,v_t,s_t,T_remaining), where p_t is the current position of the hangar; S32: Based on the state vector, calculate the predicted position P_predicted of the hangar using a linear motion prediction model, with the following formula: P_predicted=p_t+(v_t / ||v_t||)*s_t*T_remaining Where ||v_t|| is the magnitude of the direction vector, used for normalization; S33: output hangar predicted position P_predicted.

5. The control method for a mobile unmanned aerial vehicle hangar according to claim 1, characterized in that, In step S4, A*-based sequence planning is used to generate a path Path={C_start,C_i,...,P_goal} that starts from the starting region C_start, visits all unit regions C_i in sequence, and finally reaches the destination location P_goal (i.e., the hangar prediction location or the personnel input location). The path cost function f(n) is: f(n) = g(n) + h(n); Where: g(n) is the actual cost from the starting region C_start to the unit region C_i, and h(n) is the heuristically estimated cost from the unit region C_i to the destination location P_goal.

6. The control method for a mobile unmanned aerial vehicle hangar according to claim 1, characterized in that, Step S6 includes: S61: Repeat step S3 to obtain a new predicted position P_new, and update the library predicted position by ΔP; S62: If ΔP is greater than a preset threshold, then take the current unit area of ​​the UAV as the starting point and the updated hangar predicted position as the ending point to regenerate a movement path covering the remaining unit area.

7. The control method for a mobile unmanned aerial vehicle hangar according to claim 1, characterized in that, In step S7, the return path of the UAV to the hangar is performed using the Dynamic Window Method (DWA) for local real-time obstacle avoidance and trajectory smoothing.

8. The control method for a mobile unmanned aerial vehicle hangar according to claim 1, characterized in that, It also includes step S8: During the drone operation, monitor the drone battery level E_current in real time. If the battery level is lower than the safety threshold E_safe, immediately interrupt the operation and execute the return to home.

9. The control method for a mobile unmanned aerial vehicle hangar according to claim 8, characterized in that, The formula for calculating the safety threshold E_safe in step S8 is as follows: E_safe=k*(P_return / v_avg)*P_power Where: P_return is the distance from the drone's current position to the hangar's real-time position, v_avg is the drone's average return speed, P_power is the drone's average power during flight, and k is the safety factor.

10. A control system for a mobile unmanned aerial vehicle (UAV) hangar, characterized in that, include: The system includes a drone, a hangar, and an inspection vehicle, wherein the hangar is fixedly mounted on the inspection vehicle, the drone is detachably connected to the hangar, and the inspection vehicle includes a processor that invokes instructions to perform the operation of any one of the methods described in claims 1-9.

Images