Unmanned aerial vehicle autonomous flight control method and system based on multi-vehicle cooperation and dynamic airspace distribution
By constructing an airspace unit model and distributed decision-making, autonomous flight control of UAVs is achieved, solving the single point of failure problem of centralized control and the conflict problem of traditional path planning, thus improving the reliability and real-time performance of the system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-13
- Publication Date
- 2026-04-14
AI Technical Summary
Existing multi-UAV systems have the risk of single point of failure due to centralized control architecture, and traditional path planning methods are insufficient in resolving conflicts in time and space, making it difficult to cope with dynamic obstacles and mission changes.
A control method based on multi-aircraft collaboration and dynamic airspace allocation is adopted. By constructing an airspace unit model, the UAV can make autonomous decisions and dynamically allocate resources. Distributed communication networks are used to handle conflicts, and flight control strategies with preset rules are adopted to dynamically adjust flight plans.
It improves the system's reliability and survivability, enabling it to maintain stable operation during node failures, effectively preventing flight conflicts, and enhancing the system's real-time performance and robustness.
Smart Images
Figure CN121857783A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of autonomous flight control technology for unmanned aerial vehicles (UAVs), specifically to an autonomous flight control method and system for UAVs based on multi-aircraft collaboration and dynamic airspace allocation. Background Technology
[0002] With the rapid development of drone technology, multi-drone systems are increasingly being used in logistics, agricultural plant protection, and emergency rescue. However, as drone swarms continue to expand, airspace resource competition and flight conflicts are becoming increasingly prominent, posing serious challenges to existing technological solutions in addressing these issues.
[0003] Currently, multi-UAV cooperative flight mainly adopts a centralized control architecture. This type of architecture relies on ground control stations or cloud servers for centralized path planning and scheduling, which has obvious single point of failure risk. Once the central node fails due to communication interruption or hardware failure, the operation of the entire UAV swarm will be severely affected. Furthermore, existing technologies mostly use path planning methods based on continuous space. These methods solve the coordination problem by planning the optimal path for each UAV in a continuous coordinate system. However, they are essentially the allocation of "path lines" resources, which is difficult to guarantee without conflicts in the time and space dimensions. When encountering dynamic obstacles or sudden mission changes, they are prone to chain conflicts, requiring complex global replanning. The real-time performance and robustness of the system are difficult to guarantee.
[0004] Based on the above reasons, this invention proposes an autonomous flight control method and system for unmanned aerial vehicles (UAVs) based on multi-aircraft collaboration and dynamic airspace allocation. Summary of the Invention
[0005] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides an autonomous flight control method and system for unmanned aerial vehicles (UAVs) based on multi-aircraft collaboration and dynamic airspace allocation, in order to solve the problems of single-point failure risk, poor scalability, and insufficient time and space conflict resolution capabilities of traditional path planning methods in existing centralized control systems.
[0006] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: an autonomous flight control method for unmanned aerial vehicles (UAVs) based on multi-aircraft cooperation and dynamic airspace allocation, comprising the following steps: S1: Collect the spatial features of the task airspace, construct a spatial unit model of the task airspace based on the spatial features, and match the spatial unit model with time based on each independent spatial unit. S2: The UAV inputs the flight path into the airspace unit model, and the individual UAV autonomously generates an initial flight plan containing a series of target airspace units based on the airspace unit model; S3: Send the initial flight plan as an airspace reservation request to the collaborative decision-making system to request the occupation of the series of target airspace units, and perform time matching on the target airspace units; S4: When a conflict is detected in an airspace unit, a flight control processing strategy based on preset rules is triggered and executed to dynamically allocate the conflicting airspace units. S5: Based on the results of conflict resolution, confirm the sequence of airspace units that each UAV is permitted to occupy, and form the final flight permit; S6: Each UAV shall perform autonomous flight within the corresponding airspace unit in accordance with the final flight permission.
[0007] Preferably, the size of the airspace unit in the airspace unit model in step S1 can be adaptively adjusted according to the preset UAV swarm density, environmental complexity, or mission accuracy requirements.
[0008] Preferably, the conflict determination in step S4 is one or more of the following: at least two UAVs are in the same airspace unit at the same time, one UAV covers the airspace unit of an adjacent UAV at the same time, and the UAV changes its flight path to cause a conflict with the airspace unit.
[0009] Preferably, the execution of the flight control processing strategy based on preset rules specifically includes: the conflicting UAVs exchanging their respective priority information through a distributed communication network, the UAV with lower priority actively relinquishing its occupation of the conflict airspace unit, and regenerating alternative flight plans based on the current state and the airspace unit model.
[0010] Preferably, the flight control processing strategy based on preset rules further includes: recording the number of times each UAV actively abandons the use of conflict units, and setting a threshold for the number of times in the system; when UAVs have the same priority, the one that actively abandons the use of conflict units more times flies first; when the number of times a UAV actively abandons the use of conflict units reaches the threshold, it has the highest flight level for occupying airspace units.
[0011] Preferably, the priority is generated dynamically from at least one or more of the following: the UAV's mission type, onboard energy status, flight speed, and flight efficiency index.
[0012] An autonomous flight control system for unmanned aerial vehicles (UAVs) based on multi-aircraft collaboration and dynamic airspace allocation, used to implement the method, includes: The airspace modeling module is used to build and maintain the airspace unit model and record the real-time status of each airspace unit. A flight control processing strategy and communication unit, deployed on each UAV, is used to generate the initial flight plan, send the airspace reservation request, and handle conflicts with other UAVs. The airspace scheduling module is communicatively connected to the airspace modeling module and all the flight control processing strategies and communication units. It is used to receive the airspace reservation request, perform conflict detection, and process flight control processing strategies and communication unit data.
[0013] Preferably, the airspace scheduling module is integrated into each UAV and does not participate in decision-making under normal circumstances, but is activated only when airspace unit conflicts occur.
[0014] An unmanned aerial vehicle (UAV) equipped with the aforementioned distributed decision-making and communication unit, enabling the UAV to act as a cluster node and execute the method described above.
[0015] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method.
[0016] (III) Beneficial Effects Compared with existing technologies, this invention provides an autonomous flight control method and system for unmanned aerial vehicles (UAVs) based on multi-machine collaboration and dynamic airspace allocation, which has the following beneficial effects: This invention adopts a flight control processing strategy with preset rules, and each UAV has autonomous decision-making capabilities, effectively overcoming the single point of failure problem of traditional centralized architecture. Even if some nodes fail, the system can still maintain stable operation, and the reliability and survivability are significantly improved. This invention introduces an airspace unit model mechanism, transforming the basic unit of resource allocation from path lines to airspace units, realizing a fundamental change in flight trajectory in both spatial and temporal dimensions. This mechanism can fundamentally prevent the occurrence of conflicts and has significant advantages over traditional methods. Attached Figure Description
[0017] Figure 1 This is a flowchart of the control method of the present invention. Detailed Implementation
[0018] To better understand the purpose, structure, and function of this invention, and to achieve safer and more efficient autonomous collaborative flight of multiple UAVs, this invention provides a more detailed description of the autonomous flight control method and system for UAVs based on multi-UAV collaboration and dynamic airspace allocation.
[0019] refer to Figure 1 This invention relates to an autonomous flight control method for unmanned aerial vehicles (UAVs) based on multi-aircraft collaboration and dynamic airspace allocation, comprising the following steps: S1: Collect the spatial features of the mission airspace, construct the airspace unit model of the mission airspace based on the spatial features, and adaptively adjust the airspace unit model based on the matching time of each independent airspace unit and the airspace unit size of the airspace unit model according to the preset UAV cluster density, environmental complexity or mission accuracy requirements. By adopting a flight control and processing strategy based on preset rules, each UAV has autonomous decision-making capabilities, effectively overcoming the single point of failure problem of traditional centralized architecture. Even if some nodes fail, the system can still maintain stable operation, and its reliability and survivability are significantly improved.
[0020] S2: The UAV inputs the flight path into the airspace unit model, and the individual UAV autonomously generates an initial flight plan containing a series of target airspace units based on the airspace unit model; S3: Send the initial flight plan as an airspace reservation request to the collaborative decision-making system to request the occupation of the series of target airspace units, and perform time matching on the target airspace units; S4: When a conflict is detected in an airspace unit, a flight control processing strategy based on preset rules is triggered and executed to dynamically allocate the conflicting airspace units. The determination of a conflict is one or more of the following: at least two drones are in the same airspace unit at the same time; one drone covers the airspace unit of an adjacent drone at the same time; and the conflict occurs when the drone changes its flight path. Specifically, the execution of the flight control processing strategy based on preset rules includes: the conflicting UAVs exchange their respective priority information through a distributed communication network, the UAV with lower priority actively relinquishes its occupation of the conflict airspace unit, and regenerates alternative flight plans based on the current state and the airspace unit model, providing a flexible and efficient solution for airspace resource allocation through a dynamic priority mechanism; Furthermore, the flight control processing strategy based on preset rules also includes: recording the number of times each UAV actively abandons the use of conflict units and setting a threshold for the number of times; when UAVs have the same priority, the one that actively abandons the use of conflict units more times takes priority in flight; when the number of times a UAV actively abandons the use of conflict units reaches the threshold, it has the highest flight level for occupying airspace units, thus ensuring the efficient flight of the UAV.
[0021] The priority is generated dynamically from at least one or more of the following: the UAV's mission type, onboard energy status, flight speed, and flight efficiency indicators.
[0022] S5: Based on the results of conflict resolution, confirm the sequence of airspace units that each UAV is permitted to occupy, and form the final flight permit; S6: Each UAV shall perform autonomous flight within the corresponding airspace unit in accordance with the final flight permission.
[0023] An autonomous flight control system for unmanned aerial vehicles (UAVs) based on multi-aircraft collaboration and dynamic airspace allocation, used to implement the method, includes: The airspace modeling module is used to build and maintain the airspace unit model and record the real-time status of each airspace unit. A flight control processing strategy and communication unit, deployed on each UAV, is used to generate the initial flight plan, send the airspace reservation request, and handle conflicts with other UAVs. The airspace scheduling module is communicatively connected to the airspace modeling module and all the flight control processing strategies and communication units. It is used to receive the airspace reservation request, perform conflict detection, and process flight control processing strategies and communication unit data. The airspace scheduling module is integrated into each UAV and does not participate in decision-making under normal circumstances. It is only activated when airspace unit conflicts occur. Each UAV has autonomous decision-making capabilities, effectively overcoming the single point of failure problem of traditional centralized architecture.
[0024] An unmanned aerial vehicle (UAV) equipped with the aforementioned distributed decision-making and communication unit, enabling the UAV to act as a cluster node and execute the method described above.
[0025] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method.
[0026] It is understood that the present invention has been described through some embodiments, and those skilled in the art will recognize that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the invention. Furthermore, under the teachings of the present invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the protection scope of the present invention.
Claims
1. An autonomous flight control method for unmanned aerial vehicles (UAVs) based on multi-aircraft collaboration and dynamic airspace allocation, characterized in that, Includes the following steps: S1: Collect the spatial features of the task airspace, construct a spatial unit model of the task airspace based on the spatial features, and match the spatial unit model with time based on each independent spatial unit. S2: The UAV inputs the flight path into the airspace unit model, and the individual UAV autonomously generates an initial flight plan containing a series of target airspace units based on the airspace unit model; S3: Send the initial flight plan as an airspace reservation request to the collaborative decision-making system to request the occupation of the series of target airspace units, and perform time matching on the target airspace units; S4: When a conflict is detected in an airspace unit, a flight control processing strategy based on preset rules is triggered and executed to dynamically allocate the conflicting airspace units. S5: Based on the results of conflict resolution, confirm the sequence of airspace units that each UAV is permitted to occupy, and form the final flight permit; S6: Each UAV shall perform autonomous flight within the corresponding airspace unit in accordance with the final flight permission.
2. The autonomous flight control method for unmanned aerial vehicles (UAVs) based on multi-aircraft collaboration and dynamic airspace allocation according to claim 1, characterized in that, The size of the airspace unit in the airspace unit model in step S1 can be adaptively adjusted according to the preset UAV swarm density, environmental complexity, or mission accuracy requirements.
3. The autonomous flight control method for unmanned aerial vehicles (UAVs) based on multi-aircraft collaboration and dynamic airspace allocation according to claim 1, characterized in that, The conflict determination in step S4 is one or more of the following: at least two UAVs are in the same airspace unit at the same time, one UAV covers the airspace unit of an adjacent UAV at the same time, and the UAV changes its flight path to cause a conflict with the airspace unit.
4. The UAV autonomous flight control method based on multi-aircraft collaboration and dynamic airspace allocation according to claim 3, characterized in that, The execution of the flight control processing strategy based on preset rules specifically includes: the conflicting UAVs exchange their respective priority information through a distributed communication network, the UAV with lower priority actively relinquishes its occupation of the conflict airspace unit, and regenerates alternative flight plans based on the current state and the airspace unit model.
5. The UAV autonomous flight control method based on multi-aircraft cooperation and dynamic airspace allocation according to claim 4, characterized in that: The flight control processing strategy based on preset rules also includes: recording the number of times each UAV actively abandons the use of conflict units and setting a threshold for the number of times in the system; when UAVs have the same priority, the one that actively abandons the use of conflict units more times will fly first; when the number of times a UAV actively abandons the use of conflict units reaches the threshold, it will have the highest flight level for occupying airspace units.
6. The UAV autonomous flight control method and system based on multi-aircraft collaboration and dynamic airspace allocation according to claim 5, characterized in that, The priority is generated dynamically from at least one or more of the following: the UAV's mission type, onboard energy status, flight speed, and flight efficiency indicators.
7. An autonomous flight control system for unmanned aerial vehicles (UAVs) based on multi-aircraft collaboration and dynamic airspace allocation, used to implement the method described in any one of claims 1-6, characterized in that, include: The airspace modeling module is used to build and maintain the airspace unit model and record the real-time status of each airspace unit. A flight control processing strategy and communication unit, deployed on each UAV, is used to generate the initial flight plan, send the airspace reservation request, and handle conflicts with other UAVs. The airspace scheduling module is communicatively connected to the airspace modeling module and all the flight control processing strategies and communication units. It is used to receive the airspace reservation request, perform conflict detection, and process flight control processing strategies and communication unit data.
8. The UAV autonomous flight control system based on multi-aircraft collaboration and dynamic airspace allocation according to claim 7, characterized in that, The airspace scheduling module is integrated into each UAV and does not participate in decision-making under normal circumstances, but is activated only when airspace unit conflicts occur.
9. A drone, characterized in that, The drone is equipped with a distributed decision-making and communication unit as described in claim 7 or 8, enabling the drone to act as a cluster node and execute the method as described in any one of claims 1-6.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the steps of the method as described in any one of claims 1-6.