A UAV path planning method based on LoRa self-organizing network

Abstract

The invention proposes a path planning method for an unmanned aerial vehicle (UAV) based on a LoRa self-organizing network, and relates to the technical field of wireless communication networks. The method comprises: collecting environmental data around the emergency site through sensor nodes at the site, and using the LoRa self-organizing network to set a relay node for transmitting environmental data including position and time, and calculating the data transmission rate, time data and network transmission capacity data in the process of environmental data transmission according to the configuration parameters of the self-organizing network to establish a path planning mathematical model for the UAV, and solving to obtain an optimal path planning result; the invention establishes a path planning mathematical model for the UAV by establishing a LoRa self-organizing network and accurately calculating relevant data in the process of environmental data transmission, so as to quickly and accurately plan the optimal path, solve the problem of wireless communication for data transmission and path planning of relay UAVs, and realize the real-time collection, transmission, storage and management of environmental emergency information.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication network technology, and in particular to a method for unmanned aerial vehicle (UAV) path planning based on a LoRa self-organizing network. Background Art

[0002] In recent years, there have been frequent incidents of people being trapped due to natural disasters or human factors. Due to the harsh rescue environment, there is great uncertainty in communication methods and facilities. In order to carry out rescue work smoothly, the first thing is to ensure the smooth communication work in the emergency environment such as floods, earthquakes, fires and explosions, and to ensure the reasonable allocation of limited available communication resources on site. Driven by the current demand for large-scale monitoring, the trend of using drone-assisted wireless sensor network systems to monitor, explore and monitor large areas of interest is becoming stronger and stronger, and has received widespread attention from researchers. The drone carries a mobile receiver and flies in the air along a static or dynamically calculated trajectory according to the navigation system. The mobile receiver exchanges data with the ground sensor nodes covered by its flight trajectory and then transmits the data back to the base station. The mobility of the drone can reduce the response time in an emergency. Let the UAV act as a relay or data collector, which can extend the service life of the sensor node, balance the energy consumption of the sensor node, alleviate the problems caused by WAN node failure in the emergency environment, and expand the monitoring range.

[0003] However, search and rescue work in emergency environments is a prominent task, and its related communication functions must be prioritized and deployed to ensure the safety of people's lives and property. However, due to the limitations of communication resources in disaster areas, the emergency deployment plans currently adopted are relatively simple and fail to provide resource signal processing capabilities equivalent to emergency communication needs. From the existing literature, the resource deployment methods described in most literatures are still difficult to meet actual needs, and the resource supply services for communication terminals in disaster areas are not perfect enough.

[0004] Therefore, the present invention proposes a UAV path planning method based on LoRa self-organizing network to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a UAV path planning method based on LoRa self-organizing network, which can solve the path planning problems of wireless communication and relay UAV for data transmission, find the optimal transmission path between communication nodes in an emergency environment, and provide a more reliable reference basis for the scientific planning of node transmission for communication tasks with different requirements.

[0006] The technical solution of the present invention is:

[0007] The present application provides a UAV path planning method based on LoRa self-organizing network, which includes the following steps:

[0008] S1, collect environmental data around the scene through the sensor nodes at the emergency scene;

[0009] S2, using LoRa ad hoc network to set up relay nodes for transmitting environmental data including location and time;

[0010] S3, calculating the data transmission rate, time data and network transmission capacity data during the environmental data transmission process according to the configuration parameters of the LoRa self-organizing network;

[0011] S4, determining the node subset visited by the UAV, the node visit sequence and the location of the collection points corresponding to the nodes according to the location and time of the nodes, the data transmission rate, the time data and the network transmission capacity, and establishing a mathematical model for the path planning of the UAV;

[0012] S5. Solve the mathematical model of the drone's path planning using the optimal solution algorithm to obtain the best path planning result.

[0013] Furthermore, in step S3, the configuration parameters of the LoRa ad hoc network include: carrier frequency, spreading factor, bandwidth and coding rate.

[0014] Furthermore, in step S3, the calculation formula of the data transmission rate includes:

[0015] ,

[0016] In the formula, DR Indicates the LoRa data transmission rate, SF represents the spreading factor, BW represents bandwidth, CR Indicates the coding rate.

[0017] Further, in step S3, the time data includes air transmission time and data sampling time; wherein the calculation formula of the air transmission time includes:

[0018] ,

[0019] ,

[0020] The calculation formula of the data sampling time includes:

[0021] ,

[0022] In the formula, Rs. represents the symbol rate, SF represents the spreading factor, BW represents bandwidth, ToA Indicates the air transmission time of data packets in LoRa self-organizing network. N symbolIndicates the number of symbols, S SN Indicates the time slot when the node samples data in the current minute. ID SN represents the ID number of the node, mod is the remainder function, T slot Indicates the time slot duration.

[0023] Further, in step S3, the above network transmission capacity includes the maximum network capacity, the upper limit of the end-to-end delay between nodes and the network throughput of the LoRa self-organizing network; wherein, the above network maximum capacity calculation formula includes:

[0024] ,

[0025] The calculation formula of the upper limit of the end-to-end delay between the nodes includes:

[0026] ,

[0027] The calculation formula of the network throughput of the LoRa self-organizing network includes:

[0028] ,

[0029] ,

[0030] ,

[0031] ,

[0032] In the formula, N Indicates the maximum capacity of the network. T SI represents the sampling interval, T slot Indicates the time slot duration, UPPER delay Indicates the upper limit of delay, HOP min represents the minimum number of hops required for data to travel from the sensor node to the gateway, G represents the average number of data frames generated during each time slot, n represents the number of sensor nodes, P ( k,t )express t Generated during the time slot k The probability of a data frame, k represents the number of different nodes in the node subset visited by the drone, k ! represents factorial, P 0 means the probability of a frame being successfully received. S LoRaWAN Indicates the network throughput of each time slot of the LoRa self-organizing network.

[0033] Furthermore, in step S4, the calculation formula of the mathematical model of the path planning of the above-mentioned UAV includes:

[0034] ,

[0035] In the formula, K Indicates the maximum number of nodes collected. k represents the number of different nodes in the node subset visited by the drone, ψ k Indicates the order of visiting nodes. P k Represents the collection point position sequence corresponding to the node, L ( P ) represents the total travel time, i.e. the total time for data transmission and total drone flight time the sum of v Indicates the flight speed of the drone. Representation Node The location of the collection point, T max Indicates the time threshold, s i Indicates the path i nodes, s 1 indicates the starting node of the path, d Indicates the communication range of the node, C Indicates the remote control distance of the drone. n Indicates the total number of nodes, s 1 indicates the end node of the path.

[0036] Furthermore, the above-mentioned optimal solution algorithms include ant colony algorithm, particle swarm algorithm and genetic algorithm.

[0037] Compared with the prior art, the present invention has at least the following advantages or beneficial effects:

[0038] (1) The present invention provides a method for UAV path planning based on LoRa self-organizing network. By adopting LoRa self-organizing network and cooperating with relay nodes, ultra-long distance communication can be achieved, the sensitivity of UAV communication is significantly improved, power consumption is reduced, and a star or network topology structure can be formed by connecting multiple nodes to achieve free networking, saving costs and improving reliability. The method can monitor the environment in real time and accurately locate in emergency situations;

[0039] (2) The present invention calculates the data transmission rate, time data and network transmission capacity data during the data transmission process based on the configuration parameters of the LoRa self-organizing network. It can reduce control overhead, increase network throughput, reduce end-to-end transmission delay, and improve network reliability and robustness by real-time monitoring and controlling the transmission rate.

[0040] (3) The present invention can quickly and accurately plan the optimal path by establishing a mathematical model for the path planning of the UAV, solve the path planning problems of wireless communication and relay UAVs for data transmission, find the optimal transmission path between communication nodes in an emergency environment, and provide a more reliable reference basis for the scientific planning of node transmission for communication tasks with different requirements, thus realizing the real-time collection, transmission, storage and management of environmental emergency information. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 A step diagram of a UAV path planning method based on LoRa self-organizing network of the present invention;

[0043] Figure 2 It is a schematic block diagram of the internal structure of a node of the present invention. DETAILED DESCRIPTION

[0044] Terminology explanation:

[0045] UTC: (Coordinated Universal Time), Coordinated Universal Time.

[0046] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0047] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0048] It should be noted that, in this article, the term "comprises" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprises..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0049] In conjunction with the accompanying drawings, some implementation methods of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Example

[0050] See also Figure 1 , Figure 1 The figure shows a step diagram of a UAV path planning method based on LoRa self-organizing network provided in an embodiment of the present application.

[0051] The present application provides a UAV path planning method based on LoRa self-organizing network, which includes the following steps:

[0052] S1, collect environmental data around the scene through the sensor nodes at the emergency scene;

[0053] S2, using LoRa ad hoc network to set up relay nodes for transmitting environmental data including location and time;

[0054] S3, calculating the data transmission rate, time data and network transmission capacity data during the environmental data transmission process according to the configuration parameters of the LoRa self-organizing network;

[0055] S4, determining the node subset visited by the UAV, the node visit sequence and the location of the collection points corresponding to the nodes according to the location and time of the nodes, the data transmission rate, the time data and the network transmission capacity, and establishing a mathematical model for the path planning of the UAV;

[0056] S5. Solve the mathematical model of the drone's path planning using the optimal solution algorithm to obtain the best path planning result.

[0057] It should be noted that the LoRa self-organizing network in step S2 realizes ultra-long-distance communication, significantly improves the sensitivity of drone communication, and can form a star or network topology by connecting multiple nodes, thus realizing free networking;

[0058] Among them, network nodes include sensor nodes, relay nodes and gateway nodes. Sensor nodes are randomly distributed in the sensing area and can be fixed or mobile. They generate sensor data at fixed time intervals and reliably transmit them to gateway nodes within a certain time limit to realize real-time collection of environmental parameter data. Multiple drones form an aerial relay network. By setting up relay nodes containing location and time for transmitting environmental data, environmental data can be first transmitted to relay nodes, and then sent to gateway nodes in the data receiving center through relay nodes. The gateway then uploads the data to the server, and the server can present the sensing data of the disaster site through a visualization system. The geographical location and time of the node can be obtained by setting up a positioning system to obtain geographical latitude and longitude coordinates, elevation information and UTC time in real time. See Figure 2 ,The node mainly includes a pluggable sensor interface, positioning ,module, a microcontroller, a power source and a wireless communication module.

[0059] As a preferred implementation, in step S3, the configuration parameters of the LoRa self-organizing network include: carrier frequency, spreading factor, bandwidth and coding rate.

[0060] As a preferred implementation, in step S3, the calculation formula for the data transmission rate includes:

[0061] .

[0062] In the formula, DR Indicates the LoRa data transmission rate, SF represents the spreading factor, BW represents bandwidth, CR Indicates the coding rate.

[0063] As a preferred implementation, in step S3, the time data includes air transmission time and data sampling time; wherein the calculation formula of air transmission time includes:

[0064] ,

[0065] ,

[0066] The calculation formula of the data sampling time includes:

[0067] ,

[0068] In the formula, Rs. represents the symbol rate, SF represents the spreading factor, BW represents bandwidth, ToA Indicates the air transmission time of data packets in LoRa self-organizing network. N symbol Indicates the number of symbols, SSN Indicates the time slot when the node samples data in the current minute. ID SN represents the ID number of the node, mod is the remainder function, T slot Indicates the time slot duration;

[0069] It should be noted that the symbol rate represents the transmission speed of the effective load information, and the number of symbols is calculated by the symbol rate.

[0070] As a preferred implementation, in step S3, the network transmission capacity includes the maximum network capacity, the upper limit of the end-to-end delay between nodes, and the network throughput of the LoRa self-organizing network;

[0071] The formula for calculating the maximum network capacity includes:

[0072] ,

[0073] The calculation formula of the upper limit of the end-to-end delay between the nodes includes:

[0074] ,

[0075] The calculation formula of the network throughput of the LoRa self-organizing network includes:

[0076] ,

[0077] ,

[0078] ,

[0079] ,

[0080] In the formula, N Indicates the maximum capacity of the network. T SI represents the sampling interval, T slot Indicates the time slot duration, UPPER delay Indicates the upper limit of delay, HOP min represents the minimum number of hops required for data to travel from the sensor node to the gateway, G represents the average number of data frames generated during each time slot, n represents the number of sensor nodes, P ( k,t )express t Generated during the time slot k The probability of a data frame, k represents the number of different nodes in the node subset visited by the drone, k ! represents factorial, P0 means the probability of a frame being successfully received. S LoRaWAN Indicates the network throughput of each time slot of the LoRa self-organizing network;

[0081] It should be noted that when calculating the upper limit of the end-to-end delay between nodes, the number of nodes must not exceed the maximum capacity of the network.

[0082] As a preferred implementation, in step S4, the calculation formula of the path planning mathematical model of the drone includes:

[0083] ,

[0084] In the formula, K Indicates the maximum number of nodes collected. k represents the number of different nodes in the node subset visited by the drone, ψ k Indicates the order of visiting nodes. P k Represents the collection point position sequence corresponding to the node, L ( P ) represents the total travel time, i.e. the total time for data transmission and total drone flight time the sum of v Indicates the flight speed of the drone. Representation Node The location of the collection point, T max Indicates the time threshold, s i Indicates the path i nodes, s 1 indicates the starting node of the path, d Indicates the communication range of the node, C Indicates the remote control distance of the drone. n Indicates the total number of nodes, s 1 indicates the end node of the path.

[0085] It should be noted that by using the mathematical model of the drone's path planning to plan the drone's path, the number of sensors collected can be maximized when the drone visits the collection point locations of the sensors at the emergency site to collect environmental data in sequence, while controlling the distance between the location of the farthest collection point and the drone's starting point not to exceed the drone's remote control distance, and controlling the total travel time of the drone's flight path not to exceed a preset time threshold.

[0086] As a preferred implementation, the optimal solution algorithm includes ant colony algorithm, particle swarm algorithm and genetic algorithm.

[0087] It should be noted that the ant colony algorithm used in this embodiment is just suitable for the mathematical model of the path planning of the UAV under the multi-objective constraints of the present invention. It can find a balance point between multiple objectives and can effectively handle the optimal solution that requires a trade-off between multiple conflicting objectives. The ant colony algorithm is a distributed computing method that can search the solution space in parallel to improve the search efficiency. It shows high efficiency when dealing with large-scale problems. It can adaptively adjust parameters according to the characteristics of the problem to improve the performance of the algorithm. It adopts a positive feedback mechanism to make the search process converge continuously and eventually approach the optimal solution, so that effective collaboration and information sharing can be achieved even without central control.

[0088] It is understood that the structure shown in the figure is only for illustration, and a method for drone path planning based on LoRa self-organizing network may also include more or fewer components than those shown in the figure, or have a different configuration than those shown in the figure. Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0089] In the embodiments provided in the present application, it should be understood that the disclosed method can also be implemented in other ways. The embodiments described above are merely schematic, for example, the flowchart or block diagram in the accompanying drawings shows the possible implementation architecture, functions and operations of the method and computer program product according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or the flowchart, and the combination of the boxes in the block diagram and / or the flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0090] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0091] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0092] In summary, the embodiment of the present application provides a UAV path planning method based on the LoRa self-organizing network. By establishing a LoRa self-organizing network and accurately calculating the data transmission rate, time data and network transmission capacity data in the process of environmental data transmission, the node subset visited by the UAV, the node access order and the collection point location corresponding to the node are determined, thereby establishing a mathematical model for the path planning of the UAV. The optimal path can be planned quickly and accurately, which solves the wireless communication and relay UAV path planning problems of data transmission, and realizes the real-time collection, transmission, storage and management of environmental emergency information.

[0093] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0094] It will be apparent to those skilled in the art that the present application is not limited to the details of the exemplary embodiments described above, and that the present application can be implemented in other specific forms without departing from the spirit or essential features of the present application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present application. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A UAV path planning method based on LoRa self-organizing network, characterized in that: The following steps are involved: S1, collect environmental data around the scene through the sensor nodes at the emergency scene; S2, using LoRa ad hoc network to set up relay nodes for transmitting environmental data including location and time; S3, calculating the data transmission rate, time data and network transmission capacity data during the environmental data transmission process according to the configuration parameters of the LoRa self-organizing network; S4. Determine the node subset visited by the drone, the node visit sequence and the location of the collection points corresponding to the nodes according to the node location and time, data transmission rate, time data and network transmission capacity, and establish a mathematical model for the path planning of the drone; wherein the calculation formula of the mathematical model for the path planning of the drone includes: In the formula, K Indicates the maximum number of nodes collected. k represents the number of different nodes in the node subset visited by the drone, ψ k Indicates the order of visiting nodes. P k Represents the collection point position sequence corresponding to the node, L ( P ) represents the total travel time, i.e. the total time for data transmission and total drone flight time the sum of v Indicates the flight speed of the drone. Representation Node The location of the collection point, T max represents the time threshold, σ i Indicates the path i nodes, σ 1 indicates the starting node of the path, δ Indicates the communication range of the node, C Indicates the remote control distance of the drone. n Indicates the total number of nodes, s 1 indicates the end node of the path; S5. Solve the mathematical model of the drone's path planning using the optimal solution algorithm to obtain the best path planning result.

2. A method for unmanned aerial vehicle path planning based on LoRa self-organizing network as claimed in claim 1, characterized in that: In step S3, the configuration parameters of the LoRa self-organizing network include: carrier frequency, spreading factor, bandwidth and coding rate.

3. A method for unmanned aerial vehicle path planning based on LoRa self-organizing network as claimed in claim 2, characterized in that: In step S3, the data transmission rate is calculated by the formula include: In the formula, DR Indicates the LoRa data transmission rate, SF represents the spreading factor, BW represents bandwidth, CR Indicates the coding rate.

4. A method for unmanned aerial vehicle path planning based on LoRa self-organizing network as claimed in claim 2, characterized in that: In step S3, the time data includes air transmission time and data sampling time; wherein the calculation formula of the air transmission time includes: The calculation formula of the data sampling time includes: In the formula, Rs represents the symbol rate, SF represents the spreading factor, BW represents bandwidth, ToA Indicates the air transmission time of data packets in LoRa self-organizing network. N symbol Indicates the number of symbols, S SN Indicates the time slot when the node samples data in the current minute. ID SN represents the ID number of the node, mod is the remainder function, T slot Indicates the time slot duration.

5. A method for unmanned aerial vehicle path planning based on LoRa self-organizing network as claimed in claim 1, characterized in that: In step S3, the network transmission capacity includes the maximum network capacity, the upper limit of the end-to-end delay between nodes and the network throughput of the LoRa self-organizing network; wherein the maximum network capacity calculation formula includes: The calculation formula of the upper limit of the end-to-end delay between the nodes includes: The calculation formula of the network throughput of the LoRa self-organizing network includes: In the formula, N Indicates the maximum capacity of the network. T SI represents the sampling interval, T slot Indicates the time slot duration, UPPER delay Indicates the upper limit of delay, HOP min represents the minimum number of hops required for data to travel from the sensor node to the gateway, G represents the average number of data frames generated during each time slot, n represents the number of sensor nodes, P ( k,t )express t Generated during the time slot k The probability of a data frame, k represents the number of different nodes in the node subset visited by the drone, k！ represents factorial, P 0 means the probability of a frame being successfully received. S LoRaWAN Indicates the network throughput of each time slot of the LoRa self-organizing network.

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