A UAV formation control method and system

Abstract

The present invention relates to the field of drone control technology, and proposes a drone formation control method and system. The method comprises: generating multiple drone flight corridors according to the global paths of multiple drones of a drone cluster; determining whether a drone is in a trap according to the inclusion relationship between the drone flight corridor and the global path of the main drone of the drone cluster; when there is a trapped drone in the trap, constructing a connection path between the trapped drone and the main drone, generating a trapped drone flight corridor according to the connection path, and determining a temporary navigation point so that the drone cluster flies to the temporary navigation point; and constructing and solving an optimization problem to constrain the drone cluster in the main drone flight corridor so that the drone cluster bypasses the trap. The present invention effectively solves the dilemma of drones in continuous obstacles, is suitable for drone applications in various complex environments, and has a wide range of practical application value.

Description

Technical Field

[0001] The present invention generally relates to the field of drone control technology. Specifically, the present invention relates to a drone formation control method and system. Background Art

[0002] With the continuous development of drone technology, drone fleets are being used in industrial logistics and transportation, agricultural monitoring and management, disaster emergency response, environmental monitoring and resource management, smart city infrastructure management and other fields.

[0003] However, the existing UAV formation control methods still have the following problems:

[0004] (1) Existing technologies lack the ability to adapt to different environments and are difficult to operate effectively in changing natural and urban environments. This may make it impossible to adjust the flight path and obstacle avoidance strategy of the drone in a timely manner, resulting in the drone colliding in complex and continuous terrain or obstacles or being unable to find a feasible path.

[0005] (2) Some existing technologies rely on manual intervention or external instructions to achieve obstacle avoidance and lack autonomous decision-making capabilities. This not only increases operational complexity, but also limits the autonomy and flexibility of drone clusters, making it impossible to fully utilize the advantages of autonomous obstacle avoidance.

[0006] (3) Although existing technologies provide a certain degree of obstacle avoidance capabilities, in actual applications, especially in some specific environments, there are still safety risks in the flight of drone swarms. Drones may collide due to obstacle avoidance failure, resulting in equipment damage or mission failure, and the safe operation of drone swarms cannot be guaranteed.

[0007] (4) Existing technologies do not fully consider optimality in obstacle avoidance strategies, which results in the drone consuming a lot of energy and time during the obstacle avoidance process. This not only reduces mission efficiency, but also increases the operating cost of the drone, making it impossible to achieve efficient and economical obstacle avoidance operations. Summary of the invention

[0008] In order to at least partially solve the above problems in the prior art, the present invention proposes a UAV formation control method, comprising the following steps:

[0009] Generate multiple drone flight corridors based on the global paths of multiple drones in the drone swarm;

[0010] Determining whether the drone is in a trap based on an inclusion relationship between the drone flight corridor and the global path of the master drone of the drone cluster;

[0011] When there is a trapped drone in the trap, construct a connection path between the trapped drone and the main drone, generate a flight corridor for the trapped drone according to the connection path, and determine a temporary navigation point so that the drone cluster flies to the temporary navigation point; and

[0012] An optimization problem is constructed and solved to constrain the drone swarm within the main drone flight corridor so that the drone swarm bypasses the trap.

[0013] In one embodiment of the present invention, it is provided that generating multiple drone flight corridors according to the global paths of multiple drones of the drone cluster includes:

[0014] Based on the control points of the global path of each drone in the drone cluster and combined with the map information of the drone, the drone is expanded in the x-axis, y-axis and z-axis directions to generate the drone flight corridor.

[0015] In one embodiment of the present invention, it is provided that the optimization problem is expressed as follows:

[0016]

[0017] ψ c (p(t), corridor) = max{dist err (p(t)), 0} 2

[0018] Among them, J c represents the optimization function, T represents the time period, t represents the time, ω(t) represents the weight function, ψ c represents the error function between the UAV and the main UAV flight corridor, p(t) represents the position of the UAV, corridor represents the main UAV flight corridor, and dist err (p(t)) represents the distance between the UAV’s position point and the main UAV flight corridor.

[0019] In one embodiment of the present invention, it is provided that when the position point of the UAV is outside the main UAV flight corridor, c is positive, otherwise it is zero.

[0020] In one embodiment of the present invention, it is provided that the method is used for post-disaster rescue, urban environment monitoring process, and / or logistics distribution.

[0021] The present invention also proposes a UAV formation control system, comprising:

[0022] A flight corridor module, configured to generate a plurality of UAV flight corridors according to global paths of a plurality of UAVs of the UAV cluster;

[0023] a trap determination module, configured to determine whether a drone is in a trap based on an inclusion relationship between the drone flight corridor and a global path of a master drone of the drone cluster;

[0024] an escape navigation module, which is configured to construct a connection path between the trapped drone and the main drone when there is a trapped drone in the trap, generate a flight corridor for the trapped drone according to the connection path, and determine a temporary navigation point so that the drone cluster flies to the temporary navigation point; and

[0025] An optimization constraint module is configured to construct and solve an optimization problem to constrain the drone swarm in a main drone flight corridor so that the drone swarm bypasses the trap.

[0026] The present invention aims to solve the problem of a drone swarm being able to escape and avoid being trapped again after being partially trapped in an obstacle that cannot be avoided. The present invention firstly simulates a scenario in which the drone swarm cannot all pass safely, and proposes a feasible solution for how to enable the drone swarm to pass the scenario safely and efficiently.

[0027] The present invention has at least the following beneficial effects:

[0028] (1) By adopting advanced UAV swarm escape methods, the problem of UAVs being trapped in continuous obstacles can be effectively solved. This method is suitable for UAV applications in various complex environments, including industry, agriculture, emergency response, urban management and other fields, and has broad practical application value.

[0029] (2) It can ensure that the UAV cluster can maintain the consistency and stability of the formation during the process of obstacle avoidance and escape, avoid the formation chaos caused by the obstacle avoidance of a single machine, and improve the cluster's collaborative combat capability and mission execution efficiency.

[0030] (3) By optimizing the escape strategy and obstacle avoidance path planning, the present invention can quickly and effectively solve the obstacle problems of UAV clusters in complex environments, improve the task completion efficiency and the overall performance of the UAV cluster, and ensure efficient operation in various complex environments.

[0031] (4) By optimizing the obstacle avoidance and escape strategies of drone swarms, the safety of drones in complex environments can be significantly improved, the risk of collision and equipment damage can be reduced, and the smooth progress of the mission and the safe operation of drone swarms can be ensured.

[0032] (5) It provides a simple and easy-to-implement algorithm that can reduce the consumption of computing resources and time while ensuring the obstacle avoidance effect, and can be easily integrated into the existing UAV control system, thereby reducing the implementation cost and technical threshold. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] To further illustrate the advantages and features of various embodiments of the present invention, a more detailed description of various embodiments of the present invention will be presented with reference to the accompanying drawings. It will be understood that these drawings only depict typical embodiments of the present invention and are therefore not to be considered as limiting the scope thereof. In the accompanying drawings, for clarity, identical or corresponding parts will be represented by identical or similar reference numerals.

[0034] Figure 1 A schematic flow chart of a method for controlling a formation of unmanned aerial vehicles in one embodiment of the present invention is shown. DETAILED DESCRIPTION

[0035] It should be noted that the components in the figures may be shown exaggeratedly for the sake of illustration and are not necessarily true to scale. In the figures, identical or functionally identical components are provided with the same reference numerals.

[0036] In the present invention, unless otherwise specified, "arranged on...", "arranged above..." and "arranged above..." do not exclude the existence of an intermediate between the two. In addition, "arranged on or above..." merely indicates the relative positional relationship between two components, and in certain cases, such as after reversing the product direction, it can also be converted into "arranged below or below...", and vice versa.

[0037] In the present invention, each embodiment is only intended to illustrate the aspects of the present invention and should not be construed as limiting.

[0038] In the present invention, unless otherwise specified, the quantifiers "a", "an" and "an" do not exclude the presence of a plurality of elements.

[0039] It should also be noted that in the embodiments of the present invention, for the sake of clarity and simplicity, only a portion of the parts or components may be shown, but those of ordinary skill in the art will understand that under the teachings of the present invention, the required parts or components may be added according to the needs of the specific scenario. In addition, unless otherwise specified, the features in different embodiments of the present invention may be combined with each other. For example, a feature in the second embodiment may be used to replace a corresponding or functionally identical or similar feature in the first embodiment, and the resulting embodiment also falls within the disclosure scope or recorded scope of the present application.

[0040] It should also be noted that within the scope of the present invention, the terms "same", "equal", "equal to" and the like do not mean that the values ​​of the two are absolutely equal, but allow a certain reasonable error, that is, the terms also cover "substantially the same", "substantially equal", "substantially equal to". By analogy, in the present invention, the terms "perpendicular to", "parallel to" and the like indicating directions also cover the meanings of "substantially perpendicular to" and "substantially parallel to".

[0041] In addition, the numbering of the steps of the methods of the present invention does not limit the execution order of the method steps. Unless otherwise specified, the method steps can be executed in different orders.

[0042] The present invention will be further described below in conjunction with specific embodiments with reference to the accompanying drawings.

[0043] Figure 1 FIG. 2 is a flow chart of a method for controlling a drone formation in accordance with an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0044] Step 101: Generate multiple drone flight corridors based on the global paths of multiple drones in the drone cluster.

[0045] Step 102: Determine whether the drone is in a trap based on the inclusion relationship between the drone flight corridor and the global path of the master drone of the drone cluster.

[0046] Step 103: When there is a trapped drone, a connection path between the trapped drone and the main drone is constructed, a flight corridor for the trapped drone is generated according to the connection path, and a temporary navigation point is determined so that the drone cluster flies to the temporary navigation point.

[0047] Step 104: construct and solve an optimization problem to constrain the drone cluster in the main drone flight corridor so that the drone cluster bypasses the trap.

[0048] The following describes in detail each step of the method.

[0049] In step 101, based on the control points of the global path of each drone in the drone cluster and combined with the map information updated in real time by the drone, the x-axis, y-axis and z-axis directions of the drone are expanded to generate a flight corridor for judgment and optimization in subsequent steps.

[0050] In step 102, the inclusion relationship between the flight corridors of each drone and the global path of the master drone is used to determine whether the drone cluster is partially trapped in an unavoidable obstacle.

[0051] In step 103, when there is a trapped drone, a connection path between the trapped drone and the main drone is constructed, a flight corridor for the trapped drone is generated according to the same logic as step 101, and a safe temporary navigation point is found through screening, so that the drone cluster flies to the temporary navigation point.

[0052] In step 104, an optimization problem is first constructed, in which a constraint is imposed on the drone to control the drone in the corridor. The optimization problem is expressed as follows:

[0053]

[0054] ψ c (p(t), corridor) = max{dist err (p(t)), 0} 2

[0055] Among them, J c represents the optimization function, T represents the time period, t represents the time, ω(t) represents the weight function, ψ c represents the error function between the UAV and the main UAV flight corridor, p(t) represents the position of the UAV, corridor represents the main UAV flight corridor, and dist err (p(t)) represents the distance between the position point of the UAV and the main UAV flight corridor. If the position point of the UAV is outside the main UAV flight corridor, the error is positive, otherwise it is zero.

[0056] By solving the above optimization problem, the entire drone swarm can be constrained in the trapped drone flight corridor, allowing the drone swarm to safely bypass the trap.

[0057] In the post-disaster rescue process, drone swarms need to perform search and rescue missions in complex ruins and obstacles. Through this method, drone swarms can identify and avoid unavoidable continuous obstacles, safely fly away from obstacles that will trap some drones, and maintain the original formation during the flight. By optimizing path planning, drone swarms can efficiently search and rescue trapped people, improving rescue efficiency and success rate.

[0058] In the process of urban environmental monitoring, drone swarms need to cruise and monitor in high-rise buildings and complex urban environments. Through this method, drone swarms can identify continuous obstacles in the city, such as high-rise buildings and bridges, and safely adjust the flight path to avoid these obstacles while maintaining the original cruising formation. By optimizing transmission and flight strategies, drone swarms can efficiently complete urban environmental monitoring tasks and improve the level of intelligence and refinement of urban management.

[0059] In the process of logistics and delivery, drone swarms need to deliver packages in different environments such as cities and rural areas. At this time, the best use of this solution is that the drone swarm can identify and avoid continuous obstacles in the delivery route, such as power lines and buildings, and safely adjust the flight path to ensure that the package can be delivered to the destination on time and safely. By solving the optimization problem, drone swarms can efficiently plan delivery routes, bypass potential obstacles, and improve the efficiency and reliability of logistics and delivery.

[0060] The present invention also proposes a UAV formation control system, comprising:

[0061] A flight corridor module, configured to generate a plurality of UAV flight corridors according to global paths of a plurality of UAVs of the UAV cluster;

[0062] a trap determination module, configured to determine whether a drone is in a trap based on an inclusion relationship between the drone flight corridor and a global path of a master drone of the drone cluster;

[0063] an escape navigation module, which is configured to construct a connection path between the trapped drone and the main drone when there is a trapped drone in the trap, generate a flight corridor for the trapped drone according to the connection path, and determine a temporary navigation point so that the drone cluster flies to the temporary navigation point; and

[0064] An optimization constraint module is configured to construct and solve an optimization problem to constrain the drone swarm in a main drone flight corridor so that the drone swarm bypasses the trap.

[0065] The present invention firstly uses an advanced obstacle recognition algorithm to effectively identify the unavoidable continuous obstacles in the cluster. This improvement ensures that these unavoidable obstacles can be discovered and judged in time during the flight process by real-time analysis of the flight corridor and surrounding environment of the drone, thereby providing accurate information support for subsequent escape actions.

[0066] After identifying the continuous obstacles, the present invention can formulate a safe escape path to ensure that the drone cluster can safely fly away from the obstacles that trap some drones, and maintain the original formation of the cluster during the escape process. Through precise path planning and formation control algorithms, each drone in the cluster can complete the escape mission without destroying the formation, ensuring that the overall collaborative combat capability is not affected.

[0067] After flying out of the obstacle, the present invention finds the best path to bypass the trap by establishing and solving the optimization problem. The setting of the optimization problem not only takes into account the flight safety of the drone, but also takes into account the overall performance and coordination ability of the cluster. Through the optimization algorithm, the drone cluster can effectively avoid the trap and continue to perform the established tasks, thereby improving the performance of the entire system.

[0068] These improvements are based on a deep understanding of the flight characteristics and obstacle environment of drone swarms, and are designed to improve the swarm's escape capability and flight safety, ensuring that missions can be successfully completed even in complex environments.

[0069] The present invention divides the overall functions of each drone into multiple modules such as sensing, detection, and escape, which makes the overall code easy to read, maintain and expand, and also reduces the possibility of mutual influence between various functions. A distributed design is adopted for the control of each drone. Each drone has its own tracking, obstacle avoidance and other functions, and a mutual communication method is used to control the entire cluster, which enables the drone cluster to cope with more complex environments. Each drone can make the best strategy for its own special situation while ensuring the cluster formation. This design also allows developers to arbitrarily change the number and formation of drones in the drone cluster to adapt to different mission objectives.

[0070] When the drone detects and flies out of the trap, it ensures real-time detection of the surrounding environment and avoids obstacles according to the surrounding environment and formation. When bypassing the trap, the entire cluster is optimized in a safe flight corridor, and there is no possibility of collision with obstacles. The present invention ensures the safety of drone cluster flight to the greatest extent possible. By dividing the flight corridor, the entire drone cluster finds the shortest and most efficient path for the mission goal of bypassing the trap, and will speed up appropriately to quickly pass the trap while ensuring safety. The present invention can enable the drone cluster to have good error correction and avoidance for different continuous obstacles, and can be applied to similar obstacles in different environments.

[0071] The present invention can be widely used in the fields of industrial logistics and transportation, agricultural monitoring and management, disaster emergency response, environmental monitoring and resource management, smart city infrastructure management, etc. The drone cluster escape technology provided by the method can effectively deal with the problem of continuous obstacles of drones in complex environments, and ensure the stable operation and task execution of drone clusters. In the field of industrial logistics and transportation, the method can ensure that drones can pass through obstacles smoothly during warehousing and distribution, thereby improving logistics efficiency; in agricultural monitoring and management, it can help drones to perform precision agricultural operations in complex terrains; in disaster emergency response, the method can be used for drones to quickly cross obstacles at the disaster site, conduct search and rescue, and deliver materials; in the field of environmental monitoring and resource management, the method can be used for drones to monitor environmental parameters such as the atmosphere, water bodies, and soil in complex natural environments, thereby achieving environmental protection and sustainable resource utilization; in smart city infrastructure management, the method can ensure that drones can efficiently complete inspection and maintenance tasks in urban environments, thereby improving the level of intelligent urban management.

[0072] Although various embodiments of the present invention are described above, it should be understood that they are presented as examples only and not as limitations. It is obvious to those skilled in the relevant art that various combinations, modifications and changes can be made thereto without departing from the spirit and scope of the present invention. Therefore, the breadth and scope of the present invention disclosed herein should not be limited by the exemplary embodiments disclosed above, but should only be defined according to the attached claims and their equivalents.

Claims

1. A method for controlling a drone formation, characterized in that: The following steps are involved: Generate multiple drone flight corridors based on the global paths of multiple drones in the drone swarm; Determining whether the drone is in a trap based on an inclusion relationship between the drone flight corridor and the global path of the master drone of the drone cluster; When there is a trapped drone in the trap, a connection path between the trapped drone and the main drone is constructed, a flight corridor for the trapped drone is generated according to the connection path, and a temporary navigation point is determined so that the drone cluster flies to the temporary navigation point; as well as Construct and solve an optimization problem to constrain the drone swarm to the main drone flight corridor so that the drone swarm bypasses the trap; The optimization problem is expressed as follows: ψ c (p(t),corridor)=max{dist err (p(t)),0} 2 Among them, J c represents the optimization function, T represents the time period, t represents the time, ω(t) represents the weight function, ψ c represents the error function between the UAV and the main UAV flight corridor, p(t) represents the position of the UAV, corridor represents the main UAV flight corridor, and dist err (p(t)) represents the distance between the UAV’s position point and the main UAV flight corridor.

2. The UAV formation control method according to claim 1, characterized in that: Generating multiple drone flight corridors based on the global paths of multiple drones in a drone cluster includes: Based on the control points of the global path of each drone in the drone cluster and combined with the map information of the drone, the drone is expanded in the x-axis, y-axis and z-axis directions to generate the drone flight corridor.

3. The UAV formation control method according to claim 1, characterized in that: When the position of the UAV is outside the main UAV flight corridor ψ c is positive, otherwise it is zero.

4. The UAV formation control method according to claim 1, characterized in that: The method is used in post-disaster rescue, urban environment monitoring process, and\or logistics distribution.

5. A UAV formation control system, characterized in that: include: A flight corridor module, configured to generate a plurality of UAV flight corridors according to global paths of a plurality of UAVs of the UAV cluster; a trap determination module, configured to determine whether a drone is in a trap based on an inclusion relationship between the drone flight corridor and a global path of a master drone of the drone cluster; An escape navigation module is configured to construct a connection path between the trapped drone and the main drone when there is a trapped drone in the trap, generate a flight corridor for the trapped drone according to the connection path, and determine a temporary navigation point so that the drone cluster flies to the temporary navigation point; as well as an optimization constraint module configured to construct and solve an optimization problem to constrain the drone swarm within a primary drone flight corridor so that the drone swarm bypasses the trap; The optimization problem is expressed as follows: ψ c (p(t),corridor)=max{dist err (p(t)),0} 2 Among them, J c represents the optimization function, T represents the time period, t represents the time, ω(t) represents the weight function, ψ c represents the error function between the UAV and the main UAV flight corridor, p(t) represents the position of the UAV, corridor represents the main UAV flight corridor, and dist err (p(t)) represents the distance between the UAV’s position point and the main UAV flight corridor.

Images