Unmanned aerial vehicle cluster planning decision-making system and decision-making method
By using a planning and decision-making platform and collaborative control devices, combined with situational information, UAV information and mission information, the system enables pre-mission planning, online mission planning and autonomous decision-making for UAV swarms. This solves the problem of rapid response and combat effectiveness of UAV swarms in complex battlefield environments and improves the level of engineering application of UAV swarms.
Patent Information
- Application Number
- CN202511069493.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-31
- Publication Date
- 2025-11-25
AI Technical Summary
The planning and decision-making of drone swarms have shortcomings in engineering applications such as scenario adaptation, work efficiency, and rapid response.
By employing a planning and decision-making platform and collaborative control devices, and combining situational information, UAV information, and mission information, pre-mission planning, online mission planning, and autonomous decision-making can be achieved, thereby improving the efficiency of the planning and decision-making system and the combat effectiveness of UAV swarms.
It improves the rapid response capability and combat effectiveness of UAV swarms in complex battlefield situations, meets the needs of actual combat, simplifies the system structure, and improves work efficiency.
Smart Images
Figure CN121010137A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application belongs to the field of unmanned aerial vehicles, and particularly relates to an unmanned aerial vehicle cluster planning and decision system and method. BACKGROUND
[0002] With the rapid development of data link, cooperative control, situation awareness and planning and decision technologies, especially the wide application of unmanned aerial vehicles in various tasks in recent years, the rapid advancement of unmanned aerial vehicle cluster architecture, key technologies and integrated verification, unmanned aerial vehicle clusters will become an important mode for various tasks in the future.
[0003] The current planning and decision of unmanned aerial vehicle clusters have deficiencies in scene adaptation, work efficiency and rapid response in the engineering application level, and need to be further optimized. SUMMARY
[0004] The present application aims to provide an unmanned aerial vehicle cluster planning and decision system and method to solve the problems of unmanned aerial vehicle cluster planning and decision in engineering application.
[0005] To achieve the above tasks, the present application adopts the following technical solutions:
[0006] An unmanned aerial vehicle cluster planning and decision system comprises a planning and decision platform and a cooperative control device, wherein:
[0007] The planning and decision platform is deployed on the ground to realize pre-task planning and online task planning; the cooperative control device is deployed on each unmanned aerial vehicle in the unmanned aerial vehicle cluster to realize autonomous decision-making.
[0008] The pre-task planning refers to the planning and decision of the unmanned aerial vehicle in the unlaunched state, including pre-task allocation and pre-route planning; the online task planning refers to the planning and decision of the unmanned aerial vehicle in the online control state and executing non-attack tasks, including online task allocation and online route planning; the autonomous decision-making refers to the planning and decision of the unmanned aerial vehicle in the online control state and executing attack tasks, including attack task allocation and attack route planning.
[0009] An unmanned aerial vehicle cluster planning and decision method comprises:
[0010] The planning and decision platform receives the combat task issued by the superior;
[0011] The planning and decision platform performs pre-task planning based on the combat task to obtain pre-task allocation and pre-route planning results; the unmanned aerial vehicle executes the task according to the pre-task planning results;
[0012] According to the combat mission, the unmanned aerial vehicle state and the task type, corresponding planning decisions are executed: if the combat mission is updated and the unmanned aerial vehicle is not in an online management state, the unmanned aerial vehicle continues to execute the task according to the pre-task planning result; if the combat mission is updated and the unmanned aerial vehicle is in an online management state and the task type is an attack task, the unmanned aerial vehicle executes autonomous decision by using the collaborative management device carried; if the combat mission is updated and the unmanned aerial vehicle is in an online management state and the task type is not an attack task, online task planning is executed according to the updated combat mission.
[0013] Further, the combat mission comprises situation information, unmanned aerial vehicle information and task information.
[0014] Further, the situation information comprises threat sources, no-fly zones and digital elevations of combat areas; the threat sources are enemy air defense systems, the threat sources are simplified and constructed as spheres; the no-fly zones are equivalently modeled as regular shapes, including spheres, cylinders and prisms;
[0015] The unmanned aerial vehicle information comprises unmanned aerial vehicle numbers, unmanned aerial vehicle positions, current waypoints, endurance times, communication distances, flight parameters, reconnaissance parameters and attack parameters;
[0016] The task information comprises reconnaissance task information, interference task information, attack task information, evaluation task information and relay task information.
[0017] Further, the unmanned aerial vehicle position comprises longitude, latitude and height; the current waypoint comprises waypoint longitude, waypoint latitude and waypoint height; the flight parameter comprises maximum climb rate, maximum dive rate, maximum turn rate, minimum turn radius, maximum flight speed, minimum flight speed, cruise speed and inter-aircraft flight safety distance; the reconnaissance parameter comprises classical reconnaissance height, reconnaissance width, reconnaissance overlap rate and reconnaissance lead time; the attack parameter comprises terminal guidance attack distance, terminal guidance key point height and reserved pre-attack forward distance.
[0018] Further, the reconnaissance task information comprises reconnaissance area number, reconnaissance area vertex coordinates, reconnaissance direction, reconnaissance time and importance level; the interference task information comprises interfered object number, interfered object type, interfered object position, interference time and importance level; the attack task information comprises target number, target type, target position, attack direction and importance level; the evaluation task information comprises evaluation area number, evaluation area vertex coordinates, evaluation time and importance level; the relay task information comprises relayed unmanned aerial vehicle number, relay area vertex coordinates, relay time and importance level.
[0019] Further, the pre-task allocation refers to that the planning and decision platform allocates tasks to the UAVs in advance according to the situation information, UAV information and task information in the combat task; and the pre-path planning refers to that the path is planned according to the task allocated to each UAV.
[0020] Further, the UAV state includes unlaunching, online management and control and disconnection; wherein the online management and control means that the UAV has been launched and remotely controlled by the planning and decision platform through the wireless data link; and the disconnection means that the UAV has been launched but cannot be remotely controlled through the wireless data link.
[0021] Further, the task type includes reconnaissance task, interference task, attack task, evaluation task and relay task, which corresponds to the task information in the combat task issued by the superior.
[0022] A terminal device comprises a processor, a memory and a computer program stored in the memory; when the processor executes the computer program, the UAV cluster planning and decision system and the decision method are realized.
[0023] A computer readable storage medium, the medium stores a computer program; when the computer program is executed by the processor, the UAV cluster planning and decision system and the decision method are realized.
[0024] Compared with the prior art, the present application has the following technical features:
[0025] 1. The planning and decision system comprehensively considers the battlefield environment, UAV performance and task type, meets the actual combat demand, and can provide a reasonable and feasible planning and decision scheme for complex and variable battlefield situation.
[0026] 2. The UAV cluster planning and decision is realized through the planning and decision platform and the cooperative management and control device, which greatly simplifies the constitution of the UAV cluster planning and decision system and improves the working efficiency of the planning and decision system.
[0027] 3. According to the combat task, UAV state and task type, the corresponding planning and decision can be automatically executed, the rapid response is realized, and the combat effectiveness of the UAV cluster is improved. BRIEF DESCRIPTION OF DRAWINGS
[0028] Figure 1 is a flowchart of the UAV cluster planning and decision method. DETAILED DESCRIPTION
[0029] The planning and decision as the brain of the UAV cluster can highly reflect the intelligent level of the UAV cluster, coordinates the UAV cluster to efficiently execute tasks through the running flow and optimization algorithm; the planning and decision running flow realizes the reasonable allocation of resources through the optimization flow, improves the working efficiency of the planning and decision and the combat effectiveness of the UAV cluster.
[0030] The application provides a UAV cluster planning decision system and a decision method, which comprehensively considers battlefield environment, UAV performance and task type, and can automatically perform corresponding planning decisions according to combat tasks, UAV states and task types, thereby improving the rapid response capability of the UAV cluster to complex combat tasks.
[0031] The UAV cluster planning decision system comprises a planning decision platform and a collaborative control device.
[0032] The planning decision platform is deployed on the ground and is used for realizing pre-task planning and online task planning.
[0033] The pre-task planning refers to planning decisions in the state of no launching of the UAV, and comprises pre-task allocation and pre-path planning. The online task planning refers to planning decisions when the UAV is in an online control state and executes non-attack tasks, and comprises online task allocation and online path planning. The autonomous decision refers to planning decisions when the UAV is in an online control state and executes attack tasks, and comprises attack task allocation and attack path planning.
[0034] On the basis of the above technical solution, the application further provides a UAV cluster planning decision method, which comprises the following steps.
[0035] Step 1: The planning decision platform receives combat tasks issued by a superior.
[0036] The combat tasks comprise situation information, UAV information and task information.
[0037] The situation information comprises digital elevation of threat sources, no-fly zones and combat areas. The threat sources are enemy air defense systems, mainly comprising early warning / fire control / search radars, long / medium / short-range air defense missiles and high-velocity guns. In order to improve the planning decision efficiency, the threat sources are usually simplified and constructed as spheres. The no-fly zone refers to an area where the UAV cannot fly due to bad weather, unknown situation and political risks. In order to reduce the difficulty of modeling, the no-fly zone is usually simplified as a sphere, a cylinder or a prism.
[0038] The UAV information includes UAV number, UAV position, current waypoint, endurance time, communication distance, flight parameter, reconnaissance parameter and attack parameter; wherein the UAV position includes longitude, latitude and height; the current waypoint includes waypoint longitude, waypoint latitude and waypoint height; the flight parameter includes maximum climb rate, maximum dive rate, maximum turn rate, minimum turn radius, maximum flight speed, minimum flight speed, cruise speed and inter-aircraft flight safety distance; the reconnaissance parameter includes classic reconnaissance height, reconnaissance width, reconnaissance overlap rate and reconnaissance lead time; the attack parameter includes terminal guidance attack distance, terminal guidance key point height and reserved pre-attack forward distance.
[0039] The task information includes reconnaissance task information, interference task information, attack task information, evaluation task information and relay task information; the reconnaissance task information includes reconnaissance area number, reconnaissance area vertex coordinate, reconnaissance direction, reconnaissance time and importance level; the interference task information includes interfered object number, interfered object type, interfered object position, interference time and importance level; the attack task information includes target number, target type, target position, attack direction and importance level; the evaluation task information includes evaluation area number, evaluation area vertex coordinate, evaluation time and importance level; the relay task information includes relayed UAV number, relay area vertex coordinate, relay time and importance level.
[0040] Step 2, the planning and decision platform performs pre-task planning based on the combat task, and obtains pre-task allocation and pre-waypoint planning result.
[0041] The pre-task allocation refers to pre-task allocation of the UAV by the planning and decision platform according to the situation information, UAV information and task information in the combat task; the pre-waypoint planning refers to waypoint planning according to the task allocated to each UAV; the step can use existing task planning and waypoint planning algorithm, which is not described herein.
[0042] Step 3, according to the combat task, UAV state and task type, corresponding planning and decision are performed:
[0043] The state of each UAV in the UAV cluster includes un-launched, online control and lost; wherein the online control indicates that the UAV has been launched and remotely controlled by the planning and decision platform through the wireless data link; the lost indicates that the UAV has been launched but cannot be remotely controlled through the wireless data link.
[0044] The task type includes reconnaissance task, interference task, attack task, evaluation task and relay task, which corresponds to the task information in the combat task issued by the superior.
[0045] The specific process of executing the corresponding planning decision is:
[0046] If the combat task is updated and the UAV is not in the online management state, the UAV continues to execute the task according to the result of the pre-task planning; if the combat task is updated and the UAV is in the online management state and the task type is an attack task, the UAV executes autonomous decision by using the cooperative management device; if the combat task is updated and the UAV is in the online management state and the task type is not an attack task, the online task planning is executed according to the updated combat task; and if the combat task is not updated, the UAV continues to execute the task according to the result of the pre-task planning.
[0047] Among them, the attack task allocation and the attack route planning in the autonomous decision refer to that the UAV allocates the attack task to different UAVs according to the situation information in the combat task, the UAV information and the task information; each UAV autonomously plans the attack route after accepting the task; the online task allocation and the online route planning in the online task planning refer to that the combat task of the current UAV is updated, for example, it is a non-attack task, and the task needs to be re-planned; the planning decision platform re-plans the task allocation and the route planning of the UAV based on the current UAV information, the situation information and the task information; the task allocation and the route planning in the present scheme can adopt the existing task allocation and route planning algorithm.
[0048] The above examples are only used to illustrate the technical solutions of the present application, but not limit it; although the present application is described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.
Claims
1. An unmanned aerial vehicle swarm planning and decision system, characterized in that, The system comprises a planning decision platform and a cooperative control device, wherein: The planning decision platform is deployed on the ground to realize pre-task planning and online task planning; the cooperative control device is deployed on each unmanned aerial vehicle in the unmanned aerial vehicle cluster to realize autonomous decision-making; The pre-task planning refers to planning decision-making in the state of no launching of the unmanned aerial vehicle, including pre-task allocation and pre-path planning; the online task planning refers to planning decision-making when the unmanned aerial vehicle is in an online control state and executes a non-attack task, including online task allocation and online path planning; the autonomous decision-making refers to planning decision-making when the unmanned aerial vehicle is in an online control state and executes an attack task, including attack task allocation and attack path planning.
2. A method for unmanned aerial vehicle swarm planning and decision making, characterized in that, The system comprises: The planning decision platform receives a combat task issued by a superior; The planning decision platform performs pre-task planning based on the combat task to obtain pre-task allocation and pre-path planning results; the unmanned aerial vehicle executes the task according to the pre-task planning results; According to the combat task, the state of the unmanned aerial vehicle and the type of the task, corresponding planning decision-making is performed: if the combat task is updated and the unmanned aerial vehicle is not in an online control state, the unmanned aerial vehicle continues to execute the task according to the pre-task planning results; if the combat task is updated and the unmanned aerial vehicle is in an online control state and the type of the task is an attack task, the unmanned aerial vehicle performs autonomous decision-making by using the cooperative control device; if the combat task is updated and the unmanned aerial vehicle is in an online control state and the type of the task is not an attack task, online task planning is performed according to the updated combat task.
3. The method of claim 2, wherein, The combat task comprises situation information, unmanned aerial vehicle information and task information.
4. The method of claim 3, wherein, The situation information comprises threat sources, no-fly zones and digital elevations of combat areas; the threat sources are enemy air defense systems, and the threat sources are simplified and constructed into spherical shapes; The no-fly zones are equivalently modeled into regular shapes, including spherical, cylindrical and prismatic shapes; The unmanned aerial vehicle information comprises unmanned aerial vehicle numbers, unmanned aerial vehicle positions, current waypoints, endurance times, communication distances, flight parameters, reconnaissance parameters and attack parameters; The task information comprises reconnaissance task information, interference task information, attack task information, evaluation task information and relay task information.
5. The method of claim 4, wherein, The unmanned aerial vehicle position comprises longitude, latitude and altitude; the current waypoint comprises waypoint longitude, waypoint latitude and waypoint altitude; the flight parameter comprises maximum climb rate, maximum dive rate, maximum turn rate, minimum turn radius, maximum flight speed, minimum flight speed, cruising speed and inter-vehicle flight safety distance; the reconnaissance parameter comprises classical reconnaissance altitude, reconnaissance width, reconnaissance overlap rate and reconnaissance lead time; the attack parameter comprises terminal guidance attack distance, terminal guidance key point altitude and reserved pre-attack forward distance.
6. The method of claim 4, wherein, The reconnaissance task information includes reconnaissance area number, reconnaissance area vertex coordinates, reconnaissance direction, reconnaissance time and importance level; the interference task information includes interfered object number, interfered object type, interfered object position, interference time and importance level; the attack task information includes target number, target type, target position, attack direction and importance level; the evaluation task information includes evaluation area number, evaluation area vertex coordinates, evaluation time and importance level; and the relay task information includes relayed UAV number, relay area vertex coordinates, relay time and importance level.
7. The method of claim 2, wherein, The pre-task allocation refers to pre-allocation of tasks to the UAVs by the planning and decision platform according to the situation information, UAV information and task information in the combat task; The pre-route planning refers to route planning according to the task allocated to each UAV. 8.The method of claim 2, wherein, The UAV state includes unlaunched, online management and control and lost. The online management and control means that the UAV has been launched and remotely controlled by the planning and decision platform through a wireless data link; and the lost means that the UAV has been launched but cannot be remotely controlled through the wireless data link.
9. The method of claim 2, wherein, The task type includes reconnaissance task, interference task, attack task, evaluation task and relay task, which correspond to the task information in the combat task issued by the superior.
10. A computer readable storage medium having stored therein a computer program; characterized in that, The computer program is executed by the processor to realize the UAV cluster planning and decision system and the decision method according to any one of claims 2-9. The computer program is executed by the processor to realize the UAV cluster planning and decision system and the decision method according to any one of claims 2-9.