Unmanned aerial vehicle formation cooperative flight control method and system

Abstract

The invention provides an unmanned aerial vehicle formation cooperative flight control method and system, and relates to the technical field of cooperative control, and the method comprises the steps: determining a first planning path of each unmanned aerial vehicle based on the real-time task demands of a target unmanned aerial vehicle formation; establishing an initial path model based on the real-time task demand, and inputting each first planned path of the target unmanned aerial vehicle formation into the initial path model, thereby judging the feasibility of the first planned path; adjusting the first planning path of the corresponding unmanned aerial vehicle based on the feasibility of the first planning path so as to obtain a second planning path set; and carrying out cooperative flight based on the second planned path set, and carrying out dynamic adjustment on the second planned path set based on the real-time environment parameters. By analyzing the real-time task demand of the unmanned aerial vehicle formation, the first planned path is determined, the feasibility of the first planned path is analyzed, and path adjustment is performed, so that the flight condition of the unmanned aerial vehicle formation can be judged more timely and accurately, and more accurate flight control is realized.

Description

Technical Field

[0001] The present invention relates to the field of cooperative control technology, and in particular to a cooperative flight control method and system for an unmanned aerial vehicle formation. Background Art

[0002] At present, drone formation flying is widely used in military reconnaissance, target strike, civilian pesticide spraying, photovoltaic inspection and other fields.

[0003] However, traditional formation methods mostly use a physical leader leading the wingman, which carries the risk of the entire formation losing control if the leader loses control. In addition, when faced with complex environments and changing tasks, the traditional method's coordination efficiency and path planning and adjustment efficiency need to be improved.

[0004] Therefore, the present invention provides a method and system for controlling the coordinated flight of a UAV formation. Summary of the invention

[0005] The present invention provides a UAV formation cooperative flight control method and system, which are used to solve the problems of low cooperative efficiency and untimely update of planned paths in the prior art.

[0006] The present invention provides a UAV formation cooperative flight control method, comprising: Step 1: Determine the first planned path of each UAV in the target UAV formation based on the real-time mission requirements of the target UAV formation; Step 2: Establish an initial path model based on the real-time mission requirements of the target UAV formation, and input each first planned path of the target UAV formation into the initial path model to determine the feasibility of each first planned path; Step 3: adjusting the first planned path of the corresponding UAV based on the feasibility of the first planned path, thereby obtaining a second planned path set; Step 4: Perform collaborative flight based on the second planned path set, and dynamically adjust the second planned path set based on real-time environmental parameters.

[0007] According to the present invention, determining a first planned path for each UAV in a target UAV formation based on the real-time mission requirements of the target UAV formation includes: Step 11: Obtain the real-time mission requirements of the target UAV formation, and analyze the real-time mission requirements to determine the first mission requirements of each UAV in the target UAV formation; Step 12: Obtain real-time environmental parameters and determine the initial planning path of each UAV in combination with the first mission requirements of each UAV; Step 13: Obtain the equipment parameters of each UAV and determine whether each UAV can complete the flight of the corresponding initial planned path; If the UAV can complete the flight of the corresponding initial planned path, the initial planned path is used as the first planned path of the corresponding UAV; If the UAV cannot complete the flight of the corresponding initially planned path, it is necessary to re-analyze the real-time mission requirements of the target UAV formation.

[0008] According to the present invention, the feasibility of each first planned path is determined, including: Step 21: dividing the requirements based on the real-time mission requirements of the target UAV formation, and establishing an initial path model of the target UAV formation based on the requirements division result and the formation information of the target UAV formation; Step 22: judging whether each first planned path can satisfy the model constraint of the initial path model based on the model constraint of the initial path model, and if so, taking the first planned path of each UAV as the second planned path; Step 23: input each second planned path into the initial path model, thereby obtaining the first planned task result, thereby obtaining the first task set of each UAV in the target UAV formation; Step 24: Integrate each first planning task result in the first task set to obtain a comprehensive task planning result of the target UAV formation, and compare the comprehensive task planning result with the real-time task requirements to determine the planning feasibility of the comprehensive task planning result; If the comprehensive planning task result is not feasible, the real-time task requirements are classified according to the corresponding UAVs to obtain the first task requirement of each UAV; Step 25: Compare each first task requirement with the corresponding first planning task result to obtain a first comparison result, and determine a planning feasibility index S in combination with time feasibility; ; Where S is the planning feasibility index of the current UAV, is the planned relative position of the jth parameter at the i-th moment in the result of the first planning task, is the relative position of the jth parameter requirement at the i-th moment in the corresponding first task requirement, is the obstacle avoidance result at the i-th moment in the first planning task result, is the flight impact factor, is the flight impact of the remaining k-th UAV on the current UAV, is the relative position of the remaining k-th UAV to the current UAV, is the time feasibility impact weight, is the flight feasibility impact weight, where the obstacle avoidance result value is 0 or 1, 0 indicates successful obstacle avoidance, 1 indicates failed obstacle avoidance, m is the number of parameters in the first planning task result, n is the number of data acquisition moments in the first planning task result cycle, h is the number of remaining drones in the target drone formation, and exp is an exponential function with e as the base; Step 26: Determine the feasibility of the first planned path of the current UAV based on the planning feasibility index.

[0009] The method for determining the feasibility of the first planned path of the current UAV based on the planning feasibility index provided by the present invention includes: When the planning feasibility index is greater than the first preset feasibility index, it is determined that the current first planning path is feasible; When the planning feasibility index is less than the preset first feasibility index and greater than the second feasibility index, the corresponding planning path optimization scheme is extracted from the preset path adjustment database based on the planning feasibility index, so as to perform basic optimization on the first planning path of the current UAV, and perform feasibility judgment on the planning path again; When the planning feasibility index is less than the preset second feasibility index, it is determined that the current first planning path is not feasible.

[0010] According to the present invention, the first planned path of the corresponding UAV is adjusted based on the feasibility of the first planned path to obtain a second planned path set, including: Step 31: extracting the UAVs whose first planned paths are not feasible, and obtaining the corresponding first comparison results; Step 32: Determine the deviation type and deviation value between the first planning task result corresponding to the first planning path in the first comparison result and the real-time task requirement, and obtain a first deviation set; Step 33: based on each deviation type and the corresponding deviation value in the first deviation set, a corresponding planning adjustment strategy is selected from the deviation-strategy database to obtain a first planning adjustment strategy, thereby obtaining a first planning adjustment strategy set of the target UAV formation; Step 34: Adjust the first planned path based on the first planned adjustment strategy set to obtain a second planned adjustment path, thereby obtaining a second planned path set of the target UAV formation.

[0011] According to the present invention, the first planned path is adjusted based on the first planned adjustment strategy set to obtain a second planned adjustment path, thereby obtaining a second planned path set for the target UAV formation, including: Step 341: adjusting the corresponding first planning path based on each first planning adjustment strategy in the first planning adjustment strategy set to obtain an initial planning adjustment path; Step 342: combining each initial planned adjusted path of the target UAV formation with the first planned path that has not been adjusted to obtain an initial planned path set of the target UAV formation; Step 343: Determine whether there is a path conflict in the initial planned path set; If there is a path conflict, the initial planning adjustment path corresponding to the UAV with the path conflict is extracted and the path is adjusted again; If there is no path conflict, each initial planned adjustment path is used as the second planned adjustment path of the corresponding UAV, so as to obtain the second planned path set of the target UAV formation by combining the first planned path that has not been adjusted.

[0012] According to the present invention, the coordinated flight is performed based on the second planned path set, and the second planned path set is dynamically adjusted based on real-time environmental parameters, including: Step 41: performing coordinated flight of the target UAV formation based on the second planned path set to obtain a real-time flight path of the target UAV formation; Step 42: Compare the real-time flight path with the second planned path set, and determine the real-time path error at each flight detection moment; Step 43: obtaining the real-time environmental parameters of the target UAV formation, and obtaining the historical environmental parameters of the target UAV formation when performing historical coordinated flight to obtain the historical path error of the target UAV formation during historical coordinated flight; Step 44: adjusting the real-time path error based on the historical path error, thereby achieving real-time adjustment of the second planned path set.

[0013] The present invention provides a UAV formation cooperative flight control system, comprising: The first planning module is used to determine the first planning path of each UAV in the target UAV formation based on the real-time mission requirements of the target UAV formation; Feasibility judgment module: used to establish an initial path model based on the real-time mission requirements of the target UAV formation, and input each first planned path of the target UAV formation into the initial path model, so as to judge the feasibility of each first planned path; Path optimization module: used for adjusting the first planned path of the corresponding UAV based on the feasibility of the first planned path, so as to obtain a second planned path set; Collaborative processing module: used to perform collaborative flight based on the second planned path set, and dynamically adjust the second planned path set based on real-time environmental parameters.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: a UAV formation collaborative flight control method and system provided by the present invention analyzes the real-time mission requirements of the UAV formation to determine a first planned path, analyzes the feasibility of the first planned path, and adjusts the path, so as to more timely and accurately judge the flight conditions of the UAV formation and achieve more precise flight control. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 is a flow chart of a UAV formation cooperative flight control method provided by an embodiment of the present invention; Figure 2 It is a structural diagram of a UAV formation cooperative flight control system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Embodiment 1: The embodiment of the present invention provides a method for controlling the coordinated flight of a UAV formation. Figure 1 As shown, including: Step 1: Determine the first planned path of each UAV in the target UAV formation based on the real-time mission requirements of the target UAV formation; Step 2: Establish an initial path model based on the real-time mission requirements of the target UAV formation, and input each first planned path of the target UAV formation into the initial path model to determine the feasibility of each first planned path; Step 3: adjusting the first planned path of the corresponding UAV based on the feasibility of the first planned path, thereby obtaining a second planned path set; Step 4: Perform collaborative flight based on the second planned path set, and dynamically adjust the second planned path set based on real-time environmental parameters.

[0019] In this embodiment, the real-time task requirements include path requirements, stability requirements, time requirements, economic requirements, etc. Among them, the most important real-time task requirements are path requirements and time requirements.

[0020] In this embodiment, the first planned path refers to splitting the real-time mission requirements of the target UAV formation to determine the flight path and flight time requirements of each UAV, thereby determining the corresponding planned path.

[0021] In this embodiment, the initial path model is an initial path model of the target UAV formation determined according to real-time mission requirements, wherein the input of the initial path model is each first planned path, and the output result is the feasibility of the first planned path.

[0022] In this embodiment, feasibility refers to whether there are problems such as too long flight time and insufficient flight stability when the drone flies based on the first planned path, thereby affecting the completion of real-time mission requirements.

[0023] In this embodiment, the second planned path set refers to retaining feasible first planned paths and optimizing infeasible first planned paths, thereby obtaining a planned path based on the synthesis of each planned path.

[0024] In this embodiment, the real-time environmental parameters include parameters such as temperature, humidity, slope, obstacles, etc.

[0025] In this embodiment, dynamic adjustment refers to real-time adjustment of the flight path of each UAV according to real-time environmental parameters and the path deviation between the planned path and the real-time flight path.

[0026] The beneficial effect of the above technical solution is: by analyzing the real-time mission requirements of the UAV formation, the first planned path is determined, and the feasibility of the first planned path is analyzed and the path is adjusted, the flight conditions of the UAV formation can be judged more timely and accurately, and more precise flight control can be achieved.

[0027] Embodiment 2: Based on Example 1, determining a first planned path for each drone in the target drone formation based on the real-time mission requirements of the target drone formation includes: Step 11: Obtain the real-time mission requirements of the target UAV formation, and analyze the real-time mission requirements to determine the first mission requirements of each UAV in the target UAV formation; Step 12: Obtain real-time environmental parameters and determine the initial planning path of each UAV in combination with the first mission requirements of each UAV; Step 13: Obtain the equipment parameters of each UAV and determine whether each UAV can complete the flight of the corresponding initial planned path; If the UAV can complete the flight of the corresponding initial planned path, the initial planned path is used as the first planned path of the corresponding UAV; If the UAV cannot complete the flight of the corresponding initially planned path, it is necessary to re-analyze the real-time mission requirements of the target UAV formation.

[0028] In this embodiment, the real-time task requirements include path requirements, stability requirements, time requirements, economic requirements, etc. Among them, the most important real-time task requirements are path requirements and time requirements.

[0029] In this embodiment, the first mission requirement refers to analyzing the real-time mission requirements to determine the mission requirements for the target UAV formation to perform the flight mission.

[0030] In this embodiment, the real-time environmental parameters include: environmental temperature, humidity, wind force, pressure, slope, terrain and other parameters.

[0031] In this embodiment, the initial planned path refers to the planned path of each UAV in the target UAV formation determined according to the real-time environmental parameters and the first mission requirements.

[0032] In this embodiment, the first planned path refers to splitting the real-time mission requirements of the target UAV formation to determine the flight path and flight time requirements of each UAV, thereby determining the corresponding planned path.

[0033] The beneficial effect of the above technical solution is: by analyzing the real-time mission requirements of the UAV formation, the first planning path can be determined, the flight conditions of the UAV formation can be judged more timely and accurately, and more precise flight control can be achieved.

[0034] Embodiment 3: Based on the second embodiment, the feasibility of each first planned path is judged, including: Step 21: dividing the requirements based on the real-time mission requirements of the target UAV formation, and establishing an initial path model of the target UAV formation based on the requirements division result and the formation information of the target UAV formation; Step 22: judging whether each first planned path can satisfy the model constraint of the initial path model based on the model constraint of the initial path model, and if so, taking the first planned path of each UAV as the second planned path; Step 23: input each second planned path into the initial path model, thereby obtaining the first planned task result, thereby obtaining the first task set of each UAV in the target UAV formation; Step 24: Integrate each first planning task result in the first task set to obtain a comprehensive task planning result of the target UAV formation, and compare the comprehensive task planning result with the real-time task requirements to determine the planning feasibility of the comprehensive task planning result; If the comprehensive planning task result is not feasible, the real-time task requirements are classified according to the corresponding UAVs to obtain the first task requirement of each UAV; Step 25: Compare each first task requirement with the corresponding first planning task result to obtain a first comparison result, and determine a planning feasibility index S in combination with time feasibility; ; Where S is the planning feasibility index of the current UAV, is the planned relative position of the jth parameter at the i-th moment in the result of the first planning task, is the relative position of the jth parameter requirement at the i-th moment in the corresponding first task requirement, is the obstacle avoidance result at the i-th moment in the first planning task result, is the flight impact factor, is the flight impact of the remaining k-th UAV on the current UAV, is the relative position of the remaining k-th UAV to the current UAV, is the time feasibility impact weight, is the flight feasibility impact weight, where the obstacle avoidance result value is 0 or 1, 0 indicates successful obstacle avoidance, 1 indicates failed obstacle avoidance, m is the number of parameters in the first planning task result, n is the number of data acquisition moments in the first planning task result cycle, h is the number of remaining drones in the target drone formation, and exp is an exponential function with e as the base; Step 26: Determine the feasibility of the first planned path of the current UAV based on the planning feasibility index.

[0035] In this embodiment, the initial path model is an initial path model of the target UAV formation determined according to real-time mission requirements, wherein the input of the initial path model is each first planned path, and the output result is the feasibility of the first planned path.

[0036] In this embodiment, the model constraint conditions include domain integrity constraints, entity integrity constraints, referential integrity constraints, user-defined integrity constraints and other constraint conditions.

[0037] In this embodiment, the second planned path refers to judging whether each first planned path can satisfy the model constraints of the initial path model based on the model constraints of the initial path model. If each first planned path satisfies the model constraints of the initial path model, the first planned path of each UAV is used as the second planned path.

[0038] In this embodiment, the first planned mission result refers to the flight mission result of each UAV obtained by inputting each second planned path into the initial path model.

[0039] In this embodiment, the first task set is a set of first planned task results for each drone in the target drone formation.

[0040] In this embodiment, the integrated task planning result refers to integrating each first planned task result in the first task set, so as to obtain the task planning result corresponding to the target UAV formation under the second planned path based on the integrated result.

[0041] In this embodiment, planning feasibility refers to comparing the comprehensive task planning result with the real-time task requirement. If the comprehensive task planning result is not less than the real-time task requirement, the current second planned path is judged to be feasible. If the comprehensive task planning result is less than the real-time task requirement, the current second planned path is judged to be infeasible. At the same time, the real-time task requirements are classified according to the different requirements of the corresponding UAVs, and the first task requirements of each UAV are re-determined.

[0042] In this embodiment, the first comparison result refers to the comparison result between each first task requirement and the corresponding first planned task result.

[0043] In this embodiment, the planning feasibility index is comprehensively determined based on the first comparison result and the time feasibility of the target UAV formation.

[0044] In this embodiment, feasibility refers to whether there are problems such as too long flight time and insufficient flight stability when the drone flies based on the first planned path, thereby affecting the completion of real-time mission requirements.

[0045] The beneficial effect of the above technical solution is: by judging the planning feasibility index to analyze the feasibility of the first planned path, and then adjusting the path, more precise flight control of the UAV formation can be achieved.

[0046] Embodiment 4: Based on Example 3, determining the feasibility of the first planned path of the current UAV based on the planning feasibility index includes: When the planning feasibility index is greater than the first preset feasibility index, it is determined that the current first planning path is feasible; When the planning feasibility index is less than the preset first feasibility index and greater than the second feasibility index, the corresponding planning path optimization scheme is extracted from the preset path adjustment database based on the planning feasibility index, so as to perform basic optimization on the first planning path of the current UAV, and perform feasibility judgment on the planning path again; When the planning feasibility index is less than the preset second feasibility index, it is determined that the current first planning path is not feasible.

[0047] In this embodiment, the first preset feasibility index and the second preset feasibility index are determined based on the flight path of a drone formation of the same type as the target drone formation.

[0048] The beneficial effect of the above technical solution is: by judging the planning feasibility index to analyze the feasibility of the first planned path, and then adjusting the path, more precise flight control of the UAV formation can be achieved.

[0049] Embodiment 5: Based on Example 3, the first planned path of the corresponding UAV is adjusted based on the feasibility of the first planned path, so as to obtain a second planned path set, including: Step 31: extracting the UAVs whose first planned paths are not feasible, and obtaining the corresponding first comparison results; Step 32: Determine the deviation type and deviation value between the first planning task result corresponding to the first planning path in the first comparison result and the real-time task requirement, and obtain a first deviation set; Step 33: based on each deviation type and the corresponding deviation value in the first deviation set, a corresponding planning adjustment strategy is selected from the deviation-strategy database to obtain a first planning adjustment strategy, thereby obtaining a first planning adjustment strategy set of the target UAV formation; Step 34: Adjust the first planned path based on the first planned adjustment strategy set to obtain a second planned adjustment path, thereby obtaining a second planned path set of the target UAV formation.

[0050] In this embodiment, the first deviation set refers to a set consisting of deviation types and deviation values ​​determined based on a comparison result of the first planned task result corresponding to the first planned path and the real-time task requirement.

[0051] In this embodiment, the first planning adjustment strategy is a flight planning path adjustment strategy obtained by screening from a deviation-strategy database according to the deviation type and corresponding deviation value of the first deviation set.

[0052] In this embodiment, the first planning adjustment strategy set is a planning adjustment strategy set that includes each first planning adjustment strategy of the target UAV formation.

[0053] In this embodiment, the second planned adjusted path refers to a planned path obtained by adjusting the first planned path based on the first planned adjustment strategy set.

[0054] In this embodiment, the second planned path set refers to retaining feasible first planned paths and optimizing infeasible first planned paths, thereby obtaining a planned path based on the synthesis of each planned path.

[0055] The beneficial effect of the above technical solution is: by making adjustments based on the feasibility judgment results of the first planned path of the UAV formation, thereby adjusting the path for path analysis, path conflicts in the adjusted path can be avoided, the path planning efficiency can be improved, and the coordinated flight efficiency of the target UAV formation can be improved.

[0056] Embodiment 6: Based on Example 5, a second planned path set of the target UAV formation is obtained, including: Step 341: adjusting the corresponding first planning path based on each first planning adjustment strategy in the first planning adjustment strategy set to obtain an initial planning adjustment path; Step 342: combining each initial planned adjusted path of the target UAV formation with the first planned path that has not been adjusted to obtain an initial planned path set of the target UAV formation; Step 343: Determine whether there is a path conflict in the initial planned path set; If there is a path conflict, the initial planning adjustment path corresponding to the UAV with the path conflict is extracted and the path is adjusted again; If there is no path conflict, each initial planned adjustment path is used as the second planned adjustment path of the corresponding UAV, so as to obtain the second planned path set of the target UAV formation by combining the first planned path that has not been adjusted.

[0057] In this embodiment, the initial planned path set refers to a combination of each initial planned adjusted path of the target UAV formation and the first planned path that has not been adjusted.

[0058] In this embodiment, path conflict refers to the overlap of paths between the initially planned adjustment paths or first planned paths corresponding to different drones in the target drone formation, resulting in a risk of collision between the corresponding drones.

[0059] The beneficial effect of the above technical solution is that by performing path analysis on the initially planned adjustment path of the UAV formation, path conflicts in the adjusted path can be avoided, the path planning efficiency can be improved, and thus the coordinated flight efficiency of the target UAV formation can be improved.

[0060] Embodiment 7: Based on Example 5, collaborative flight is performed based on the second planned path set, and the second planned path set is dynamically adjusted based on real-time environmental parameters, including: Step 41: performing coordinated flight of the target UAV formation based on the second planned path set to obtain a real-time flight path of the target UAV formation; Step 42: Compare the real-time flight path with the second planned path set, and determine the real-time path error at each flight detection moment; Step 43: obtaining the real-time environmental parameters of the target UAV formation, and obtaining the historical environmental parameters of the target UAV formation when performing historical coordinated flight to obtain the historical path error of the target UAV formation during historical coordinated flight; Step 44: adjusting the real-time path error based on the historical path error, thereby achieving real-time adjustment of the second planned path set.

[0061] In this embodiment, the real-time flight path refers to the flight path of the target UAV formation when performing UAV coordinated flight according to the second planned path set.

[0062] In this embodiment, the real-time path error refers to a path error obtained by comparing the real-time flight path with the corresponding second planned path in the second planned path set.

[0063] In this embodiment, the real-time environmental parameters include parameters such as temperature, humidity, slope, obstacles, etc.

[0064] In this embodiment, the historical path error refers to the path error of the target UAV formation in the coordinated flight caused by the historical environmental parameters when the target UAV formation was performing the historical coordinated flight.

[0065] In this embodiment, dynamic adjustment refers to real-time adjustment of the flight path of each UAV according to real-time environmental parameters and the path deviation between the planned path and the real-time flight path.

[0066] The beneficial effect of the above technical solution is: by performing collaborative flight based on the second planned path set, the flight situation can be monitored in real time, error judgment can be made in time and path adjustment can be performed, so that flight control can be more precise.

[0067] Embodiment 8: The embodiment of the present invention provides a UAV formation cooperative flight control system, such as Figure 2 As shown, including: The first planning module is used to determine the first planning path of each UAV in the target UAV formation based on the real-time mission requirements of the target UAV formation; Feasibility judgment module: used to establish an initial path model based on the real-time mission requirements of the target UAV formation, and input each first planned path of the target UAV formation into the initial path model, so as to judge the feasibility of each first planned path; Path optimization module: used for adjusting the first planned path of the corresponding UAV based on the feasibility of the first planned path, so as to obtain a second planned path set; Collaborative processing module: used to perform collaborative flight based on the second planned path set, and dynamically adjust the second planned path set based on real-time environmental parameters.

[0068] The beneficial effects of the above technical solution are: analyzing the real-time mission requirements of the UAV formation to determine the first planned path, analyzing the feasibility of the first planned path, and making path adjustments, which can more timely and accurately judge the flight conditions of the UAV formation and achieve more precise flight control.

[0069] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A UAV formation cooperative flight control method, characterized in that: include: Step 1: Determine the first planned path of each UAV in the target UAV formation based on the real-time mission requirements of the target UAV formation; Step 2: Establish an initial path model based on the real-time mission requirements of the target UAV formation, and input each first planned path of the target UAV formation into the initial path model to determine the feasibility of each first planned path; Step 3: adjusting the first planned path of the corresponding UAV based on the feasibility of the first planned path, thereby obtaining a second planned path set; Step 4: Perform collaborative flight based on the second planned path set, and dynamically adjust the second planned path set based on real-time environmental parameters.

2. The method for controlling the coordinated flight of a UAV formation according to claim 1, characterized in that: Determining a first planned path for each UAV in the target UAV formation based on the real-time mission requirements of the target UAV formation includes: Step 11: Obtain the real-time mission requirements of the target UAV formation, and analyze the real-time mission requirements to determine the first mission requirements of each UAV in the target UAV formation; Step 12: Obtain real-time environmental parameters and determine the initial planning path of each UAV in combination with the first mission requirements of each UAV; Step 13: Obtain the equipment parameters of each UAV and determine whether each UAV can complete the flight of the corresponding initial planned path; If the UAV can complete the flight of the corresponding initial planned path, the initial planned path is used as the first planned path of the corresponding UAV; If the UAV cannot complete the flight of the corresponding initially planned path, it is necessary to re-analyze the real-time mission requirements of the target UAV formation.

3. The method for controlling the coordinated flight of a UAV formation according to claim 2, characterized in that: An initial path model is established based on the real-time mission requirements of the target UAV formation, and each first planned path of the target UAV formation is input into the initial path model, so as to determine the feasibility of each first planned path, including: Step 21: dividing the requirements based on the real-time mission requirements of the target UAV formation, and establishing an initial path model of the target UAV formation based on the requirements division result and the formation information of the target UAV formation; Step 22: judging whether each first planned path can satisfy the model constraint of the initial path model based on the model constraint of the initial path model, and if so, taking the first planned path of each UAV as the second planned path; Step 23: input each second planned path into the initial path model, thereby obtaining the first planned task result, thereby obtaining the first task set of each UAV in the target UAV formation; Step 24: Integrate each first planning task result in the first task set to obtain a comprehensive task planning result of the target UAV formation, and compare the comprehensive task planning result with the real-time task requirements to determine the planning feasibility of the comprehensive task planning result; If the comprehensive planning task result is not feasible, the real-time task requirements are classified according to the corresponding UAVs to obtain the first task requirement of each UAV; Step 25: Compare each first task requirement with the corresponding first planning task result to obtain a first comparison result, and determine a planning feasibility index S in combination with time feasibility; ; Where S is the planning feasibility index of the current UAV, is the planned relative position of the jth parameter at the i-th moment in the result of the first planning task, is the relative position of the jth parameter requirement at the i-th moment in the corresponding first task requirement, is the obstacle avoidance result at the i-th moment in the first planning task result, is the flight impact factor, is the flight impact of the remaining k-th UAV on the current UAV, is the relative position of the remaining k-th UAV to the current UAV, is the time feasibility impact weight, is the flight feasibility impact weight, where the obstacle avoidance result value is 0 or 1, 0 indicates successful obstacle avoidance, 1 indicates failed obstacle avoidance, m is the number of parameters in the first planning task result, n is the number of data acquisition moments in the first planning task result cycle, h is the number of remaining drones in the target drone formation, and exp is an exponential function with e as the base; Step 26: Determine the feasibility of the first planned path of the current UAV based on the planning feasibility index.

4. The method for controlling the coordinated flight of a UAV formation according to claim 3, characterized in that: The feasibility of the first planned path of the current UAV is determined based on the planning feasibility index, including: When the planning feasibility index is greater than the first preset feasibility index, it is determined that the current first planning path is feasible; When the planning feasibility index is less than the preset first feasibility index and greater than the second feasibility index, the corresponding planning path optimization scheme is extracted from the preset path adjustment database based on the planning feasibility index, so as to perform basic optimization on the first planning path of the current UAV, and perform feasibility judgment on the planning path again; When the planning feasibility index is less than the preset second feasibility index, it is determined that the current first planned path is not feasible.

5. The method for controlling the coordinated flight of a UAV formation according to claim 3, characterized in that: The first planned path of the corresponding UAV is adjusted based on the feasibility of the first planned path, so as to obtain a second planned path set, including: Step 31: extracting the UAVs whose first planned paths are not feasible, and obtaining the corresponding first comparison results; Step 32: Determine the deviation type and deviation value between the first planning task result corresponding to the first planning path in the first comparison result and the real-time task requirement, and obtain a first deviation set; Step 33: based on each deviation type and the corresponding deviation value in the first deviation set, a corresponding planning adjustment strategy is selected from the deviation-strategy database to obtain a first planning adjustment strategy, thereby obtaining a first planning adjustment strategy set of the target UAV formation; Step 34: Adjust the first planned path based on the first planning adjustment strategy set to obtain a second planned adjustment path, thereby obtaining a second planned path set of the target UAV formation.

6. The method for controlling the coordinated flight of a UAV formation according to claim 5, characterized in that: The first planned path is adjusted based on the first planned adjustment strategy set to obtain a second planned adjustment path, thereby obtaining a second planned path set of the target UAV formation, including: Step 341: adjusting the corresponding first planning path based on each first planning adjustment strategy in the first planning adjustment strategy set to obtain an initial planning adjustment path; Step 342: combining each initial planned adjusted path of the target UAV formation with the first planned path that has not been adjusted to obtain an initial planned path set of the target UAV formation; Step 343: Determine whether there is a path conflict in the initial planned path set; If there is a path conflict, the initial planning adjustment path corresponding to the UAV with the path conflict is extracted and the path is adjusted again; If there is no path conflict, each initial planned adjustment path is used as the second planned adjustment path of the corresponding UAV, so as to obtain the second planned path set of the target UAV formation by combining the first planned path that has not been adjusted.

7. The method for controlling the coordinated flight of a UAV formation according to claim 5, characterized in that: The coordinated flight is performed based on the second planned path set, and the second planned path set is dynamically adjusted based on the real-time environmental parameters, including: Step 41: performing coordinated flight of the target UAV formation based on the second planned path set to obtain a real-time flight path of the target UAV formation; Step 42: Compare the real-time flight path with the second planned path set, and determine the real-time path error at each flight detection moment; Step 43: obtaining the real-time environmental parameters of the target UAV formation, and obtaining the historical environmental parameters of the target UAV formation when performing historical coordinated flight to obtain the historical path error of the target UAV formation during historical coordinated flight; Step 44: adjusting the real-time path error based on the historical path error, thereby achieving real-time adjustment of the second planned path set.

8. A UAV formation cooperative flight control system, characterized in that: include: The first planning module is used to determine the first planning path of each UAV in the target UAV formation based on the real-time mission requirements of the target UAV formation; Feasibility judgment module: used to establish an initial path model based on the real-time mission requirements of the target UAV formation, and input each first planned path of the target UAV formation into the initial path model, so as to judge the feasibility of each first planned path; Path optimization module: used for adjusting the first planned path of the corresponding UAV based on the feasibility of the first planned path, so as to obtain a second planned path set; Collaborative processing module: used to perform collaborative flight based on the second planned path set, and dynamically adjust the second planned path set based on real-time environmental parameters.

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