Civil unmanned cluster control method and system based on path planning algorithm

Abstract

The invention discloses a civil unmanned cluster control method and system based on a path planning algorithm, and relates to the related technical field of unmanned aerial vehicle control, and the method comprises the steps: taking unmanned aerial vehicle equipment as a node to construct an unmanned aerial vehicle topology network; identifying each node in the unmanned aerial vehicle topology network, and determining a communication configuration parameter of each node; dividing nodes in the unmanned aerial vehicle topology network, and determining a first type of identification nodes and a second type of identification nodes; a global path planning module and a local path planning module are arranged in the control base station; and respectively inputting the first type of identification nodes and the second type of identification nodes into a global path planning module and a local path planning module for path planning. The technical problem that the unmanned cluster cannot stably operate and efficiently work due to the fact that the civil unmanned cluster cannot be accurately controlled because the unmanned aerial vehicle state information is difficult to acquire and the target position calculation and the path planning are inaccurate in the existing unmanned cluster control is solved.

Description

Technical Field

[0001] The present application relates to the field related to drone control technology, and specifically to a civilian unmanned aerial vehicle cluster control method and system based on a path planning algorithm. Background Art

[0002] In today's era of highly developed science and technology, with the rapid development of drone technology and the continuous improvement of digital intelligence, unmanned swarm control has begun to be widely used in various civil fields including industrial and agricultural production, logistics distribution, detection and exploration, disaster relief, etc. The core idea of ​​unmanned swarm control is to combine multiple drone devices into a cluster and complete complex tasks through mutual communication and collaboration between drones. When facing uncertain environments and more complex tasks, existing civil unmanned swarms need to perceive the surrounding environmental information in real time and determine the location and status information of each drone device. However, it is difficult to obtain the position status information of the unmanned cluster in complex scenarios, and the calculated target position and path planning are not accurate enough, resulting in the inability to achieve precise control of the unmanned cluster.

[0003] Therefore, in the current unmanned swarm control related technologies, there are technical problems such as difficulty in obtaining drone status information, inaccurate target position calculation and path planning, which in turn lead to the inability to accurately control civil unmanned swarms, making it impossible for unmanned swarms to operate stably and efficiently. Summary of the invention

[0004] The present application provides a civil unmanned cluster control method and system based on a path planning algorithm, and adopts technical means such as building a topological network, node identification and configuration parameters, node division, and path planning to solve the technical problems existing in the existing civil unmanned cluster control, such as difficulty in obtaining drone status information, inaccurate target position calculation and path planning, which in turn lead to the inability to accurately control the civil unmanned cluster and the inability of the unmanned cluster to operate stably and efficiently. The application achieves the technical effect of realizing precise control of the unmanned cluster and ensuring the stable operation and efficient operation of the unmanned cluster.

[0005] The present application provides a civilian unmanned cluster control method based on a path planning algorithm, the method comprising: constructing a drone topology network with drone devices as nodes, the drone topology network being cluster controlled by a control base station; identifying each node in the drone topology network, and determining the communication configuration parameters of each node, including a communication restricted distance and a communication transmission mode; dividing the nodes in the drone topology network according to the communication restricted distance and the communication transmission mode, and determining a first type of identification node and a second type of identification node, wherein the first type of identification node is a drone node with global path planning as a priority, and the second type of identification node is a drone node with local path planning as a priority; setting a global path planning module and a local path planning module in the control base station; inputting the first type of identification node into the global path planning module for path planning, and inputting the second type of identification node into the local path planning module for path planning.

[0006] In a possible implementation, the nodes in the drone topology network are divided according to the communication restricted distance and the communication transmission mode, and the first type of identification nodes and the second type of identification nodes are determined, and the following processing is performed: global / local entropy operators are assigned to the nodes in the drone topology network according to the communication restricted distance, and a first assignment result is output; global / local entropy operators are assigned to the nodes in the drone topology network according to the communication transmission mode, and a second assignment result is output; the first assignment result and the second assignment result are integrated to determine the first type of identification nodes and the second type of identification nodes.

[0007] In a possible implementation, global / local entropy operators are assigned to nodes in the drone topology network according to the communication restricted distance, and the following processing is performed: the size of the communication restricted distance is identified; if the current communication restricted distance of the node is greater than the preset communication restricted distance, a global entropy operator is assigned to the corresponding node according to the absolute value of the distance difference between the current communication restricted distance of the node and the preset communication restricted distance; if the current communication restricted distance of the node is less than the preset communication restricted distance, a local entropy operator is assigned to the corresponding node according to the absolute value of the distance difference between the current communication restricted distance of the node and the preset communication restricted distance.

[0008] In a possible implementation, global / local entropy operators are assigned to nodes in the UAV topological network according to the communication transmission mode, wherein the communication transmission mode includes a relay communication transmission mode and a non-relay communication transmission mode, the relay communication transmission mode is a mode in which relay jump communication transmission can be performed between UAVs, and the non-relay communication transmission mode is a mode in which relay jump communication transmission cannot be performed between UAVs.

[0009] In a possible implementation, global / local entropy operators are assigned to nodes in the UAV topology network according to the communication transmission mode, and the following processing is also performed: if the communication transmission mode of the node is a relay communication transmission mode, a global entropy operator is assigned to the corresponding node; if the communication transmission mode of the node is a non-relay communication transmission mode, a local entropy operator is assigned to the corresponding node.

[0010] In a possible implementation, the first type of identification node is input into the global path planning module for path planning, and the following processing is also performed: any node belonging to the first type of identification node is obtained, and the initialization path of the current node is collected; the remaining nodes belonging to the first type of identification node are used to generate a path relay node optimization library, and the relay fitness analysis of the initialization path in the path relay node optimization library is performed, and the optimization result is output, wherein the optimization result includes multiple relay nodes; the initialization path is optimized based on the multiple relay nodes, and a global optimized path is output.

[0011] In a possible implementation, the second-category identification node is input into the local path planning module for path planning, and the following processing is also performed: any node belonging to the second-category identification node is obtained, and the initialization path of the current node is collected; the planned path of each node is obtained through the control base station, and the collision risk is identified based on the planned path and the initialization path to obtain the collision risk probability, and a heuristic function is generated based on the collision risk probability; the initialization path is heuristically searched using the heuristic function to output a local optimized path.

[0012] This application also provides a civilian unmanned cluster control system based on a path planning algorithm, including:

[0013] A drone topology network construction module, wherein the drone topology network construction module is used to construct a drone topology network with drone devices as nodes, and the drone topology network is cluster-controlled by a control base station;

[0014] A node communication configuration parameter determination module, the node communication configuration parameter determination module is used to identify each node in the drone topology network and determine the communication configuration parameters of each node, including the communication restricted distance and the communication transmission mode;

[0015] A node division module, the node division module is used to divide the nodes in the drone topology network according to the communication restricted distance and the communication transmission mode, and determine the first type of identification nodes and the second type of identification nodes, wherein the first type of identification nodes are drone nodes with global path planning as priority, and the second type of identification nodes are drone nodes with local path planning as priority;

[0016] A path planning module setting module, the path planning module setting module is used to set a global path planning module and a local path planning module in the control base station;

[0017] A path planning module, wherein the path planning module is used to input the first type of identification nodes into the global path planning module for path planning, and input the second type of identification nodes into the local path planning module for path planning.

[0018] It is intended to use the civil unmanned cluster control method and system based on the path planning algorithm proposed in this application to build a UAV topology network with UAV equipment as nodes and perform cluster control through a control base station; identify each node in the UAV topology network and determine the communication configuration parameters of each node, including the communication restricted distance and the communication transmission mode; divide the nodes in the UAV topology network according to the communication restricted distance and the communication transmission mode, and determine the first type of identification nodes and the second type of identification nodes, wherein the first type of identification nodes are UAV nodes with global path planning as priority, and the second type of identification nodes are UAV nodes with local path planning as priority; set a global path planning module and a local path planning module in the control base station; input the first type of identification nodes and the second type of identification nodes into the global path planning module and the local path planning module respectively for path planning, thereby solving the technical problems existing in the existing unmanned cluster control, such as the difficulty in obtaining UAV status information, inaccurate target position calculation and path planning, which in turn lead to the inability of civil unmanned clusters to be accurately controlled, making it impossible for unmanned clusters to operate stably and efficiently, and achieving the technical effect of realizing accurate control of unmanned clusters and ensuring stable operation and efficient operation of unmanned clusters. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0020] Figure 1 A schematic diagram of a civilian unmanned swarm control method based on a path planning algorithm provided in an embodiment of the present application;

[0021] Figure 2 A schematic diagram of the process of dividing the topological network nodes of drones in the civilian unmanned cluster control method based on the path planning algorithm provided in the embodiment of the present application;

[0022] Figure 3A schematic diagram of the structure of a civilian unmanned cluster control system based on a path planning algorithm provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0024] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0025] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0026] The present application embodiment provides a civilian unmanned cluster control method based on a path planning algorithm, such as Figure 1 As shown, the method includes:

[0027] Establish a topological structure model of the unmanned cluster, determine the basic information such as the position, speed and direction of each drone in the unmanned cluster, design a suitable path planning algorithm according to the task requirements, including global path planning and local path planning, and apply the path planning algorithm to the unmanned cluster control. By obtaining the status information of the unmanned cluster in real time, calculate the target position and path of the drone, generate control instructions, and achieve precise control of the unmanned cluster, ensuring the stable operation and efficient operation of the unmanned cluster.

[0028] Step S100, constructing a drone topology network with drone devices as nodes, and the drone topology network is cluster controlled by a control base station. With multiple drone devices as nodes, they are interconnected through wireless communication to build a drone topology network. The drone topology network is clustered through a control base station. The control base station is a central node responsible for managing and controlling the drone network. Through mutual communication with drone nodes, it can send control instructions to drones, receive data feedback, and coordinate the collaborative behavior between drones. After receiving the feedback data of each drone, it analyzes, calculates the target position and route planning of each drone, and sends control instructions to the drone device. The constructed drone topology network can complete complex tasks through unmanned cluster control. For example, in disaster relief missions, the control base station can guide each drone device to search the area and find trapped people; in the field of agricultural production, unmanned clusters can collaborate in farmland patrols and fertilization operations; in logistics and distribution, unmanned clusters can realize collaborative transportation and cargo sorting. Through unmanned cluster control, the efficiency and stability of task execution can also be improved. The interconnection and cluster control between various drone devices make collaborative work and information communication more convenient and reliable. In general, by using multiple drone devices as nodes to build a drone topology network for wireless communication and cluster control through a control base station, collaborative complex task execution can be achieved, and the efficiency of task completion can be improved.

[0029] After the drone topology network is constructed, step S200 is executed to identify each node in the drone topology network and determine the communication configuration parameters of each node, including the communication restricted distance and the communication transmission mode. Each drone node in the constructed drone topology network is identified and the communication configuration parameters of each node are determined, wherein the communication configuration parameters include the communication restricted distance and the communication transmission mode. Specifically, in order to identify each drone node in the drone topology network, each drone device can be assigned a unique identifier, such as a unique ID, IP address or MAC address. This ensures that the identity and position of each drone device in the topology network are determined, which facilitates the management of cluster control. The communication restricted distance refers to the maximum communication range or distance of each drone device, so as to avoid affecting the reliability and efficiency of communication due to the drone's excessively long communication distance. Usually, the communication restricted distance is determined based on the communication performance, environmental conditions and mission requirements of the drone device; the communication transmission mode refers to the communication requirements and Conditions, determine the communication transmission mode of each UAV node, for example, the UAV node can only communicate and transmit with the control base station or in addition to information transmission with the control base station, it can also transmit information with other UAV equipment. Specifically, the control base station serves as the cluster control center, which is used to coordinate and manage the entire UAV cluster. The UAV node can communicate and transmit with the control base station through wireless communication technology, send the UAV node status to the control base station, receive the control instructions and configuration parameters of the task, etc.; in addition to communicating and transmitting with the control base station, the UAV nodes can also communicate and transmit and exchange data with each other, and can share location data, environmental information, task completion status, etc., to achieve collaborative work and mutual support. Through cluster control and collaborative work, the UAV nodes can complete various tasks more flexibly and efficiently, and adapt to complex and changing environments and task requirements.

[0030] After determining the communication configuration parameters of each node, such as Figure 2As shown, execute step S300, divide the nodes in the UAV topology network according to the communication restricted distance and the communication transmission mode, and determine the first type of identification nodes and the second type of identification nodes, wherein the first type of identification nodes are UAV nodes with global path planning as priority, and the second type of identification nodes are UAV nodes with local path planning as priority. Specifically, according to the communication restricted distance and communication transmission mode determined above, the nodes in the constructed UAV topological network are divided, and the UAV nodes are divided into first-class identification nodes and second-class identification nodes, wherein the first-class identification nodes are UAV nodes with global path planning as priority. These UAV nodes have a larger communication restricted distance and can communicate with other UAV nodes at a long distance in the constructed UAV topological network. Because the first-class identification nodes have higher communication capabilities, they are usually used for global path planning and global decision-making, and can coordinate and manage the entire UAV cluster, for example, responsible for key tasks such as task allocation or route planning; the second-class identification nodes are UAV nodes with local path planning as priority. The communication restricted distance of these UAV nodes is relatively small, and they can usually only communicate with nearby UAV nodes at close range in the constructed UAV topological network. Because they are limited by communication capabilities and communication distances, the second-class identification nodes are mainly used for local path planning and execution, and perform coordinated actions and information exchange in local areas, and quickly respond to control instructions and signals from neighboring nodes to achieve efficient local task execution.

[0031] In a possible implementation, step S300 divides the nodes in the drone topology network according to the communication restricted distance and the communication transmission mode, determines the first type of identification nodes and the second type of identification nodes, and further includes step S310, assigning global / local entropy operators to the nodes in the drone topology network according to the communication restricted distance, and outputting the first assignment result. According to the communication restricted distance of the drone node, the nodes in the drone topology network are assigned global entropy operators and local entropy operators, and the first assignment result is output. Specifically, the global entropy operator and the local entropy operator are respectively used to measure the influence of the drone node in the global path planning and the local path planning. The global entropy operator reflects the importance of the drone node in the entire drone topology network. The drone node with a higher global entropy operator value indicates that it has a greater influence in the global path planning; the local entropy operator reflects the importance of the drone node in the local range of the drone topology network. The drone node with a higher local entropy operator value indicates that it has a greater influence in the local path planning. The result after the global / local entropy operator assignment to the node is used as the first assignment result and output. It also includes step S320, assigning global / local entropy operators to the nodes in the drone topology network according to the communication transmission mode, and outputting the second assignment result. According to the communication transmission mode of the drone node, the nodes in the drone topology network are assigned global entropy operators and local entropy operators, and the second assignment result is output. Similarly, the global entropy operator and the local entropy operator are respectively used to measure the influence of the drone node in the global path planning and the local path planning. The global entropy operator reflects the importance of the drone node in the entire drone topology network. The drone node with a higher global entropy operator value indicates that it has a greater influence in the global path planning; the local entropy operator reflects the importance of the drone node in the local range of the drone topology network. The drone node with a higher local entropy operator value indicates that it has a greater influence in the local path planning. The result of assigning the global / local entropy operator to the node again is used as the second assignment result and output. It also includes step S330, fusing the first assignment result and the second assignment result to determine the first type of identification node and the second type of identification node. Specifically, the obtained first assignment result and the second assignment result are fused, and the first type of identification node and the second type of identification node are determined according to the fusion result. Through steps S310 to S330, the nodes in the drone topology network are divided according to the node communication restricted distance and the node communication mode.

[0032] In a possible implementation, step S310 performs global / local entropy operator assignment on the nodes in the drone topology network according to the communication restricted distance, outputs a first assignment result, and further includes step S311, identifying the size of the communication restricted distance, wherein a large communication restricted distance refers to a drone node with a relatively large communication range, which needs to be assigned with a global entropy operator; conversely, a small communication restricted distance refers to a drone node with a relatively small communication range, which needs to be assigned with a local entropy operator. It also further includes step S312, if the current communication restricted distance of the node is greater than the preset communication restricted distance, the global entropy operator is assigned to the corresponding node according to the absolute value of the distance difference between the current communication restricted distance of the node and the preset communication restricted distance. Specifically, when the communication distance of the current drone node is greater than the preset node communication distance, the absolute value of the difference between the current communication restricted distance of the node and the preset node communication distance is used to assign a global entropy operator to the current drone node. It further includes step S313, if the current communication restricted distance of the node is less than the preset communication restricted distance, the local entropy operator is assigned to the corresponding node according to the absolute value of the distance difference between the current communication restricted distance of the node and the preset communication restricted distance. Specifically, when the communication distance of the current drone node is less than the preset node communication distance, the absolute value of the difference between the current communication restricted distance of the node and the preset node communication distance is used to assign a local entropy operator to the current drone node. Through steps S311 to S313, the global / local entropy operator assignment of nodes in the drone topology network is completed according to the node communication restricted distance, ensuring that the nodes are accurately divided subsequently.

[0033] In a possible implementation, step S320 performs global / local entropy operator assignment on the nodes in the drone topology network according to the communication transmission mode, wherein the communication transmission mode includes a relay communication transmission mode and a non-relay communication transmission mode, wherein the relay communication transmission mode is a mode in which relay jump communication transmission can be performed between drones, and the non-relay communication transmission mode is a mode in which relay jump communication transmission cannot be performed between drones. Specifically, the relay communication mode refers to a mode in which relay jump communication transmission can be performed between drones. In this communication transmission mode, some drones act as relay nodes to transmit data from the source node to the target node. After receiving the data, the relay node forwards it to other nodes to realize data transmission; the non-relay communication transmission mode refers to a communication transmission mode in which relay jump cannot be performed between drones. In this mode, communication between drones needs to be performed directly, and each drone node directly communicates with other nodes to complete data transmission and exchange.

[0034] In a possible implementation, step S320 performs global / local entropy operator assignment on the nodes in the drone topology network according to the communication transmission mode, and further includes step S321, if the communication transmission mode of the node is a relay communication transmission mode, the global entropy operator is assigned to the corresponding node. Specifically, when the communication transmission mode of the node is a relay communication transmission mode, the global entropy operator is assigned to the corresponding drone node. It further includes step S322, if the communication transmission mode of the node is a non-relay communication transmission mode, the local entropy operator is assigned to the corresponding node. Specifically, when the communication transmission mode of the node is a non-relay communication transmission mode, the local entropy operator is assigned to the corresponding drone node.

[0035] After the nodes in the drone network are divided, step S400 is executed to set a global path planning module and a local path planning module in the control base station. Specifically, a global path planning module and a local path planning module are set in the control base station, wherein the global path planning module is responsible for calculating the optimal path in the entire drone topology network to meet the global mission requirements, and may use the global map and environmental information to determine the target position and optimal path of the drone; the local path planning module is responsible for calculating the local path of the drone in real time for dynamic obstacles and sensors in the current environment to adapt to complex environmental changes and avoid obstacle collisions. Sensor data and real-time signals are usually used to generate a safe path that can avoid obstacles, calculate the trajectory of the drone, and ensure safety.

[0036] After setting the global path planning module and the local path planning module, execute step S500, input the first type of identification node into the global path planning module for path planning, and input the second type of identification node into the local path planning module for path planning. The global path planning module set in the control base station receives the first type of identification node as input, and uses the importance of the node to calculate and plan the optimal path for the movement of the drone in the entire drone topology network. The global path planning module can give priority to the path passing through important nodes, including the location and communication capabilities of the nodes; the local path planning module takes the second type of identification node as input, and uses the communication capabilities of the nodes close to other drone nodes, the environmental conditions of the drone nodes, etc. to make path planning, achieve more accurate unmanned cluster control, and ensure the stability of drone operation.

[0037] In a possible implementation, step S500 inputs the first type of identification node into the global path planning module for path planning, and further includes step S510, obtaining any node belonging to the first type of identification node, and collecting the initialization path of the current node. Specifically, first obtain any drone node in the first type of identification node, and collect the initialization path of the current node according to the identification, location or environmental conditions of the node. It also includes step S520, generating a path relay node optimization library for the remaining nodes belonging to the first type of identification node, performing relay fitness analysis on the initialization path in the path relay node optimization library, and outputting the optimization result. After obtaining a node, the remaining drone nodes in the first category of identification nodes are used to generate a path relay node optimization library, and the relay fitness analysis of the initialization path is performed in the path relay node optimization library, and the optimization result is output. Specifically, the remaining drone nodes are used as potential path relay nodes, and a path relay node optimization library is established. Then, the fitness analysis of the path relay node is performed based on the minimum path delay, optimal communication quality, minimum path length, etc. The fitness value of each relay node is determined by simulating and evaluating the impact of the path relay node on the path, and the optimization result of the relay node is output, wherein the optimization result includes multiple relay nodes. It also includes step S530, optimizing the initialization path based on the multiple relay nodes, and outputting the global optimization path. Through steps S510 to S530, multiple relay nodes and the initialization path are optimized, and the global optimization path is output.

[0038] In a possible implementation, step S500 inputs the first type of identification node into the global path planning module for path planning, and further includes step S540, obtaining any node belonging to the second type of identification node, and collecting the initialization path of the current node. Specifically, first obtain any drone node among the second type of identification nodes, and collect the initialization path of the current node based on the identification, location or environmental conditions of the nearby range of the node. It also includes step S550, obtaining the planned path of each node through the control base station, performing collision risk identification based on the planned path and the initialization path, obtaining the collision risk probability, and generating a heuristic function with the collision risk probability. The planned path of each node is obtained by using the control base station, and the collision risk is identified according to the planned path and the initialization path. Specifically, the initialization path is compared with the planned path, and the planned route and speed trajectory of each node are compared one by one. The identification of the collision risk may include calculating the node distance between the initialization path and the planned path, detecting whether the initialization path and the planned path intersect in the same time period, considering the obstacle avoidance ability of the drone, etc., identifying the size of the collision risk, obtaining the probability of the collision risk, and then generating a heuristic function with the obtained collision risk probability, wherein the heuristic function generated according to the quantified collision risk probability is used to evaluate the risk level of collision of the current path. It also includes step S560, using the heuristic function to perform a heuristic search on the initialization path and output a local optimized path. Specifically, the generated heuristic function is used to perform a heuristic search on the initialization path, that is, to evaluate the collision risk of the initialization path, and output a local optimized path based on the evaluation result, so as to achieve precise control of the unmanned cluster and ensure the stable operation and efficient operation of the unmanned cluster.

[0039] In the above, refer to Figure 1 The civilian unmanned cluster control method based on the path planning algorithm according to the embodiment of the present invention is described in detail. Figure 3 A civilian unmanned cluster control system based on a path planning algorithm according to an embodiment of the present invention is described.

[0040] The civil unmanned cluster control system based on the path planning algorithm according to the embodiment of the present invention is used to solve the technical problems existing in the existing civil unmanned cluster control, such as the difficulty in obtaining drone status information, inaccurate target position calculation and path planning, which leads to the inability to accurately control the unmanned cluster and the inability to stably operate and efficiently operate the unmanned cluster, thereby achieving the technical effect of realizing accurate control of the unmanned cluster and ensuring the stable operation and efficient operation of the unmanned cluster. The civil unmanned cluster control system based on the path planning algorithm includes: a drone topology network construction module 10, a node communication configuration parameter determination module 20, a node division module 30, a path planning module setting module 40, and a path planning module 50.

[0041] A drone topology network construction module 10, wherein the drone topology network construction module 10 is used to construct a drone topology network with drone devices as nodes, and the drone topology network is cluster-controlled by a control base station;

[0042] A node communication configuration parameter determination module 20, the node communication configuration parameter determination module 20 is used to identify each node in the drone topology network and determine the communication configuration parameters of each node, including the communication restricted distance and the communication transmission mode;

[0043] A node division module 30, the node division module 30 is used to divide the nodes in the drone topology network according to the communication restricted distance and the communication transmission mode, and determine the first type of identification nodes and the second type of identification nodes, wherein the first type of identification nodes are drone nodes with global path planning as priority, and the second type of identification nodes are drone nodes with local path planning as priority;

[0044] A path planning module setting module 40, wherein the path planning module setting module 40 is used to set a global path planning module and a local path planning module in the control base station;

[0045] The path planning module 50 is used to input the first type of identification nodes into the global path planning module for path planning, and input the second type of identification nodes into the local path planning module for path planning.

[0046] The specific configuration of the node division module 30 will be described in detail below. As described above, the nodes in the drone topology network are divided according to the communication restricted distance and the communication transmission mode to determine the first type of identification nodes and the second type of identification nodes. The node division module 30 may further include: assigning a global / local entropy operator to the nodes in the drone topology network according to the communication restricted distance, and outputting a first assignment result; assigning a global / local entropy operator to the nodes in the drone topology network according to the communication transmission mode, and outputting a second assignment result; fusing the first assignment result and the second assignment result to determine the first type of identification nodes and the second type of identification nodes.

[0047] The specific configuration of the node division module 30 will be described in detail below. As described above, the nodes in the drone topology network are assigned global / local entropy operators according to the communication restricted distance, and the node division module 30 further includes: identifying the size of the communication restricted distance; if the current communication restricted distance of the node is greater than the preset communication restricted distance, assigning a global entropy operator to the corresponding node according to the absolute value of the distance difference between the current communication restricted distance of the node and the preset communication restricted distance; if the current communication restricted distance of the node is less than the preset communication restricted distance, assigning a local entropy operator to the corresponding node according to the absolute value of the distance difference between the current communication restricted distance of the node and the preset communication restricted distance.

[0048] The specific configuration of the node division module 30 will be described in detail below. The node division module 30 may further include: assigning global / local entropy operators to the nodes in the drone topology network according to the communication transmission mode, wherein the communication transmission mode includes a relay communication transmission mode and a non-relay communication transmission mode, wherein the relay communication transmission mode is a mode in which relay jump communication transmission can be performed between drones, and the non-relay communication transmission mode is a mode in which relay jump communication transmission cannot be performed between drones.

[0049] The specific configuration of the node division module 30 will be described in detail below. As described above, the nodes in the drone topology network are assigned global / local entropy operators according to the communication transmission mode, and the node division module 30 further includes: if the communication transmission mode of the node is a relay communication transmission mode, the corresponding node is assigned a global entropy operator; if the communication transmission mode of the node is a non-relay communication transmission mode, the corresponding node is assigned a local entropy operator.

[0050] The specific configuration of the path planning module 50 will be described in detail below. As described above, the first type of identification node is input into the global path planning module for path planning, and the path planning module 50 may further include: obtaining any node belonging to the first type of identification node, and collecting the initialization path of the current node; generating a path relay node optimization library for the remaining nodes belonging to the first type of identification node, performing relay fitness analysis on the initialization path in the path relay node optimization library, and outputting the optimization result, wherein the optimization result includes multiple relay nodes; optimizing the initialization path based on the multiple relay nodes, and outputting the global optimization path.

[0051] The specific configuration of the path planning module 50 will be described in detail below. As described above, the second type of identification node is input into the local path planning module for path planning, and the path planning module 50 may further include: obtaining any node belonging to the second type of identification node, and collecting the initialization path of the current node; obtaining the planned path of each node through the control base station, performing collision risk identification based on the planned path and the initialization path, obtaining the collision risk probability, and generating a heuristic function based on the collision risk probability; performing a heuristic search on the initialization path with the heuristic function, and outputting a local optimized path.

[0052] The civilian unmanned cluster control system based on the path planning algorithm provided in the embodiment of the present invention can execute the civilian unmanned cluster control method based on the path planning algorithm provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0053] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0054] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A civilian unmanned swarm control method based on a path planning algorithm, characterized in that: The method comprises: A drone topology network is constructed with drone devices as nodes, and the drone topology network is cluster controlled by a control base station; Identify each node in the drone topology network and determine the communication configuration parameters of each node, including the communication restricted distance and the communication transmission mode; The nodes in the drone topology network are divided according to the communication restricted distance and the communication transmission mode to determine a first type of identification node and a second type of identification node, wherein the first type of identification node is a drone node with global path planning as a priority, and the second type of identification node is a drone node with local path planning as a priority; A global path planning module and a local path planning module are set in the control base station; The first type of identification nodes are input into the global path planning module for path planning, and the second type of identification nodes are input into the local path planning module for path planning.

2. The civilian unmanned cluster control method based on the path planning algorithm according to claim 1, characterized in that: The nodes in the drone topology network are divided according to the communication restricted distance and the communication transmission mode to determine the first type identification nodes and the second type identification nodes, including: Performing global / local entropy operator assignment on nodes in the drone topology network according to the communication restricted distance, and outputting a first assignment result; Performing global / local entropy operator assignment on nodes in the drone topology network according to the communication transmission mode, and outputting a second assignment result; The first assignment result and the second assignment result are integrated to determine a first-category identification node and a second-category identification node.

3. The civilian unmanned cluster control method based on the path planning algorithm as claimed in claim 2, characterized in that: Assigning global / local entropy operators to nodes in the drone topology network according to the communication restricted distance, the method comprising: Identifying the size of the communication limited distance; If the current communication restricted distance of the node is greater than the preset communication restricted distance, a global entropy operator is assigned to the corresponding node according to the absolute value of the distance difference between the current communication restricted distance of the node and the preset communication restricted distance; If the current communication restricted distance of the node is less than the preset communication restricted distance, a local entropy operator is assigned to the corresponding node according to the absolute value of the distance difference between the current communication restricted distance of the node and the preset communication restricted distance.

4. The civilian unmanned cluster control method based on the path planning algorithm as claimed in claim 2, characterized in that: The nodes in the UAV topological network are assigned global / local entropy operators according to the communication transmission mode, wherein the communication transmission mode includes a relay communication transmission mode and a non-relay communication transmission mode, the relay communication transmission mode is a mode in which relay jump communication transmission can be performed between UAVs, and the non-relay communication transmission mode is a mode in which relay jump communication transmission cannot be performed between UAVs.

5. The civilian unmanned cluster control method based on the path planning algorithm as claimed in claim 4, characterized in that: According to the communication transmission mode, global / local entropy operator assignment is performed on the nodes in the drone topology network, including: If the communication transmission mode of the node is the relay communication transmission mode, the global entropy operator is assigned to the corresponding node; If the communication transmission mode of the node is a non-relay communication transmission mode, a local entropy operator is assigned to the corresponding node.

6. The civilian unmanned cluster control method based on the path planning algorithm according to claim 1, characterized in that: The global path planning module includes: Obtain any node belonging to the first type of identification nodes, and collect the initialization path of the current node; Generate a path relay node optimization library from the remaining nodes in the first category of identification nodes, perform relay fitness analysis on the initialized path in the path relay node optimization library, and output an optimization result, wherein the optimization result includes multiple relay nodes; The initialization path is optimized based on the multiple relay nodes, and a global optimized path is output.

7. The civilian unmanned cluster control method based on the path planning algorithm as claimed in claim 1, characterized in that: The local path planning module includes: Obtain any node belonging to the second type of identification nodes, and collect the initialization path of the current node; Acquire the planned path of each node through the control base station, perform collision risk identification according to the planned path and the initialization path, obtain the collision risk probability, and generate a heuristic function based on the collision risk probability; Perform a heuristic search on the initialization path using the heuristic function, and output a local optimization path.

8. A civilian unmanned swarm control system based on a path planning algorithm, characterized in that: The system is used to implement the civilian unmanned cluster control method based on the path planning algorithm according to any one of claims 1 to 7, and the system comprises: A drone topology network construction module, wherein the drone topology network construction module is used to construct a drone topology network with drone devices as nodes, and the drone topology network is cluster-controlled by a control base station; A node communication configuration parameter determination module, the node communication configuration parameter determination module is used to identify each node in the drone topology network and determine the communication configuration parameters of each node, including the communication restricted distance and the communication transmission mode; A node division module, the node division module is used to divide the nodes in the drone topology network according to the communication restricted distance and the communication transmission mode, and determine the first type of identification nodes and the second type of identification nodes, wherein the first type of identification nodes are drone nodes with global path planning as priority, and the second type of identification nodes are drone nodes with local path planning as priority; A path planning module setting module, the path planning module setting module is used to set a global path planning module and a local path planning module in the control base station; A path planning module, wherein the path planning module is used to input the first type of identification nodes into the global path planning module for path planning, and input the second type of identification nodes into the local path planning module for path planning.

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