Unmanned aerial vehicle intelligent tracking and automatic homeward flight nest system and method thereof

Abstract

The invention relates to the technical field of power grid intelligent inspection, and discloses an unmanned aerial vehicle intelligent tracking and automatic homeward voyage nest system and method, and the system comprises an unmanned aerial vehicle, a nest, an intelligent tracking module and an automatic control module. The unmanned aerial vehicle is provided with a multi-mode sensor and a first wireless communication module; a second wireless communication module is integrated in the machine nest; the intelligent tracking module is used for receiving and processing the operation data and the sensing data of the unmanned aerial vehicle, performing real-time position tracking of the unmanned aerial vehicle in combination with the operation data of the nest, determining the task execution state of the unmanned aerial vehicle, and adjusting the flight route of the unmanned aerial vehicle according to the real-time position and the task execution state of the unmanned aerial vehicle; and the automatic control module is used for analyzing the task execution state of the unmanned aerial vehicle in real time, and controlling the unmanned aerial vehicle to be switched to a return flight program when the task execution state triggers a return flight condition. The method has the capabilities of automatically judging the homeward voyage opportunity and intelligently adjusting the homeward voyage process route, and is high in control intelligence degree and flexibility.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent power grid inspection, and in particular to a drone intelligent tracking and automatic return-to-nest system and method. Background Art

[0002] In recent years, drone technology has developed rapidly and has been widely used in various fields. However, traditional drone operations still rely on manual intervention, especially in the return and charging stages after the mission is completed, as well as the sudden return stages during the mission, which require manual control of the drone to start the return and control the return route. This manual operation is not only inefficient, but also prone to errors, affecting the drone's continuous operation capability and mission success rate.

[0003] In response to the above situation, the patent "Method and Device for Controlling the Return of UAV" with publication number CN107783550A disclosed an automated return control method in 2018. The solution controls the UAV to return according to the return point indicated by the return instruction by receiving the UAV return instruction sent by the ground control terminal. However, this solution cannot handle the problem that in some cases, the UAV in the return mode may not be able to return to the return point according to the preset instructions due to limitations such as power, navigation accuracy, and flight control accuracy. Furthermore, the patent "Method, Device and UAV for Controlling the Return of UAV" with publication number CN110383196B disclosed a new control scheme in 2023. In this scheme, the UAV in the return mode can determine whether to return to the return point or to the alternate landing area based on the return evaluation information. In this way, the UAV can flexibly choose the return method, ensuring the flight safety and flexibility of the UAV. Although this solution can be regarded as an advancement of the former solution, the drone in the above solution still does not have the ability to automatically determine the timing of return and intelligently adjust the route of the return process, and the control intelligence and flexibility are relatively low. Summary of the invention

[0004] The present invention aims to provide a UAV intelligent tracking and automatic return to the nest system and method thereof, which has the ability to automatically determine the return timing and intelligently adjust the return process route, and has a high degree of control intelligence and flexibility.

[0005] To achieve the above objectives, the present invention provides the following basic solutions.

[0006] Solution 1

[0007] A drone intelligent tracking and automatic return to the nest system, comprising a drone and a nest; the drone is equipped with a multimodal sensor and a first wireless communication module; a second wireless communication module is integrated inside the nest; communication interaction is established between the first wireless communication module and the second wireless communication module;

[0008] It also includes an intelligent tracking module and an automatic control module; the intelligent tracking module is used to receive and process the operation data and perception data of the drone, perform real-time position tracking of the drone, and determine the mission execution status of the drone, and adjust the flight route of the drone according to the real-time position and mission execution status of the drone; the automatic control module is used to analyze the mission execution status of the drone in real time, and when the mission execution status triggers the return condition, control the drone to switch to the return program; and during the return process of the drone, adjust the return program parameters in combination with the perception data of the drone; the return program parameters include the return route and the return speed.

[0009] Solution 2

[0010] A method for intelligent tracking and automatic return control of a drone, using a drone intelligent tracking and automatic return machine nest system as described in Scheme 1 to perform intelligent tracking and automatic return control of the drone; comprising the following steps:

[0011] Step 1: Import the inspection task to the drone; control the machine nest to open and send the drone to perform the inspection task;

[0012] Step 2, the intelligent tracking module receives and processes the UAV's operating data and perception data, tracks the UAV's real-time position, determines the UAV's mission execution status, and adjusts the UAV's flight route based on the UAV's real-time position and mission execution status; the automation control module analyzes the UAV's mission execution status in real time, and when the mission execution status triggers the return condition, controls the UAV to switch to the return program; and during the UAV's return process, adjusts the return program parameters in combination with the UAV's perception data until the UAV arrives at the machine nest.

[0013] The working principle and advantages of the present invention are:

[0014] The present invention discloses an intelligent tracking and automatic return-to-home system for unmanned aerial vehicles and a method thereof, which are particularly provided with an intelligent tracking module and an automatic control module. The system can analyze the status of the unmanned aerial vehicle in real time and comprehensively based on the intelligent tracking of the flight status, perception status, etc. of the unmanned aerial vehicle, and summarize the status as the task execution status. The system can automatically trigger the return program and optimize the parameters of the return program according to the conditions, and has the ability to automatically determine the return timing and intelligently adjust the return process route, and has a high degree of control intelligence and flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic diagram of the system structure of an embodiment of a UAV intelligent tracking and automatic return to the nest system and method thereof of the present invention;

[0016] Figure 2The present invention is a schematic diagram of the operating principle of a target tracking module of an embodiment of a UAV intelligent tracking and automatic return to the home nest system and method thereof. DETAILED DESCRIPTION

[0017] The following is a further detailed description through specific implementation methods:

[0018] The embodiment is basically as shown in the attached Figure 1 Shown: A UAV intelligent tracking and automatic return to the nest system, including a UAV and a nest. It also includes an intelligent tracking module and an automatic control module.

[0019] The drone is equipped with a multimodal sensor and a first wireless communication module; a second wireless communication module is integrated inside the nest; and communication interaction is established between the first wireless communication module and the second wireless communication module. In this embodiment, the intelligent tracking module and the automation control module are integrated in the nest, and the drone-related data is obtained through the second wireless communication module. In actual applications, the intelligent tracking module and the automation control module can adjust the integration position as needed.

[0020] Specifically, the multimodal sensor includes a GPS module, a visual sensor, a laser radar module and an inertial measurement unit. Preferably, the nest is also equipped with an existing automatic charging device and an existing visual recognition module, which can guide the drone to land accurately and start charging.

[0021] The drone is also provided with a target tracking module; the target tracking module is provided with a tracking program for controlling the drone to perform a target tracking task; Figure 2 As shown, when tracking a target, the target tracking module first controls the drone to take off to an initial height position and hover, and searches for the target at 360° with an initial rotation angular velocity, and triggers a tracking program after detecting the target; the tracking program includes: estimating the relative position of the target (i.e., the position relationship of the target relative to the drone) based on the perception data, and if the target is not in the perception center, controlling the drone to climb or descend or swing until the target is in the perception center;

[0022] If the target is in the perception center, it is determined whether the relative position of the target is greater than the safety distance (in actual applications, the value of the safety distance can be set as needed); if not, the drone is controlled to retreat until the relative position of the target is greater than the safety distance; if so, the tracking mode is entered, and the drone is controlled to track the target through relative posture; in this embodiment, when the drone is controlled to track the target through relative posture, the relative posture of the drone and the target is controlled to remain within a preset fluctuation range (such as 1% to 10%) to achieve target tracking.

[0023] Through the target tracking module, drones can complete some special power grid inspection tasks, such as monitoring foreign objects on power grid lines and animal activities. Through dynamic target tracking, these moving foreign objects or animals can be monitored to prevent them from causing damage to the power grid, so as to promptly notify operation and maintenance personnel to take measures.

[0024] The intelligent tracking module is used to receive and process the operation data and perception data of the drone, perform real-time position tracking of the drone, determine the mission execution status of the drone, and adjust the flight route of the drone according to the real-time position and mission execution status of the drone; the automatic control module is used to analyze the mission execution status of the drone in real time, and when the mission execution status triggers the return condition, control the drone to switch to the return program; and during the return process of the drone, adjust the return program parameters in combination with the perception data of the drone; the return program parameters include the return route and the return speed.

[0025] The operating data of the drone includes flight parameters and system status data; the flight parameters include flight altitude, latitude and longitude coordinates, flight speed, acceleration, angular velocity and flight direction; the system status data includes power, motor speed, obstacle avoidance system status, satellite lock status and communication signal strength; the perception data includes GPS location data, image data and lidar data.

[0026] When determining the mission execution status of the UAV, the UAV trajectory is analyzed based on the GPS position data of the UAV, and the mission stage of the UAV is confirmed based on the UAV trajectory; the flight status of the UAV is confirmed based on the system status data of the UAV; at the same time, the preset analysis model is used to perform trend analysis on the operation data and perception data of the UAV, respectively, and the fault state and perception state of the UAV are correspondingly confirmed; and an array of the form (a, b, c, d) is formed as the mission execution status of the UAV; among them, a, b, c, and d refer to the mission stage, flight status, perception status, and fault status, respectively.

[0027] The values ​​of a, b, c, and d are 0 or 1. When confirming the mission stage of the drone, when the drone trajectory matches the drone mission route, a takes the value of 1, otherwise it takes the value of 0.

[0028] When confirming the flight status of the drone, if there is data in the drone system status data that is lower than the preset threshold, b takes the value of 0, otherwise it takes the value of 1. Specifically, for the power in the drone system status data, the corresponding preset threshold is set to 15% of the power value; for the motor speed, the corresponding preset threshold is set according to the motor model of the drone, and when the motor speed exceeds the rated speed and the excess is greater than 20%, b also takes the value of 0. For the obstacle avoidance system status, when the obstacle avoidance system status is invalid, b takes the value of 0; for the satellite locking status, when the locking status is invalid, b takes the value of 0. For the communication signal strength, the communication signal strength at the machine nest is used as the benchmark value, and the corresponding preset threshold is set to 50% of the benchmark value.

[0029] When confirming the perception state of the drone, if the matching degree between the data change trend output by the preset analysis model and the corresponding perception analysis benchmark is less than the threshold, c takes the value of 0, otherwise it takes the value of 1; when confirming the fault state of the drone, if the matching degree between the data change trend output by the preset analysis model and the corresponding fault analysis benchmark is less than the threshold, d takes the value of 0, otherwise it takes the value of 1. Here, the threshold is set to 60%.

[0030] The preset analysis model is a BP neural network model. When determining the fault analysis benchmark, the BP neural network model is trained using the historical flight data of the UAV, and multiple sets of data change trends are output, and the average is taken as the fault analysis benchmark. The historical flight data is the historical flight data of the UAV performing similar tasks and the task execution results are achievements. When determining the perception analysis benchmark, the BP neural network model is trained using the historical perception data of the UAV, and multiple sets of data change trends are output, and the average is taken as the perception analysis benchmark. The historical perception data is the historical perception data generated during the UAV performing similar tasks and the task execution results are achievements.

[0031] Through trend analysis, this solution can quickly make a rough assessment of the perception status and fault status of the drone. When the perception data and flight data show a change trend that is significantly different from the analysis benchmark, it can be inferred that the drone has hidden faults or no longer has effective perception capabilities and cannot effectively complete the inspection task, which can provide objective data reference for subsequent return decisions.

[0032] The return condition includes: any value of 0 appears in the mission execution status of the drone. In practical applications, the return condition can also be set more carefully, such as increasing the dimension of the (a, b, c, d) array to (a, b1, b2, b3, c, d); where b1, b2, b3 refer to the power, obstacle avoidance system status, and communication signal strength in the drone system status data, respectively.

[0033] When the values ​​in the drone's task execution status are all 1, the intelligent tracking module will adjust the drone's flight route according to the inspection task to ensure that the inspection task is completed.

[0034] When adjusting the return program parameters, the default return route is divided into N route segments, N = S / L; S is the total length of the default return route; L is the real-time maximum perception diameter of the drone; and the parameters are adjusted segment by segment according to the route segment; based on the drone's perception data, when there is a target object in a single route segment, the drone's obstacle avoidance program is called to re-plan the route segment, generate a new route segment, and use the end point of the new route segment as the starting point and the machine nest position as the end point, and make associated adjustments to other route segments based on the shortest path principle.

[0035] When there is no target in a single route segment, increase the return speed; in the last route segment, reduce the return speed and send a landing signal to the aircraft nest.

[0036] This embodiment also provides a method for intelligent tracking and automatic return control of a drone, which uses the above-mentioned drone intelligent tracking and automatic return machine nest system to perform intelligent tracking and automatic return control of the drone; the method includes the following steps:

[0037] Step 1: Import the inspection task to the drone; control the machine nest to open and send the drone to perform the inspection task;

[0038] Step 2, the intelligent tracking module receives and processes the UAV's operating data and perception data, tracks the UAV's real-time position, determines the UAV's mission execution status, and adjusts the UAV's flight route based on the UAV's real-time position and mission execution status; the automation control module analyzes the UAV's mission execution status in real time, and when the mission execution status triggers the return condition, controls the UAV to switch to the return program; and during the UAV's return process, adjusts the return program parameters in combination with the UAV's perception data until the UAV arrives at the machine nest.

[0039] The present embodiment provides a drone intelligent tracking and automatic return nest system and method, which have the ability to automatically determine the return timing and intelligently adjust the return process route. Based on the intelligent tracking of the drone's flight status, perception status, etc., it can analyze the drone status in real time and comprehensively, and summarize it into a task execution status array, and intelligently trigger the return program and return path optimization. It can effectively handle the problem that in certain situations, the drone is prone to miss the return opportunity and is lost or damaged due to failure to timely perceive the drone status, and the drone in the return mode may be unable to return to the return point according to preset instructions due to limitations such as power, navigation accuracy, and flight control accuracy. It can effectively reduce the loss rate of the drone, improve the flight safety and flexibility of the drone, and has a high degree of control intelligence and flexibility.

[0040] The above is only an embodiment of the present invention. The common sense such as the known specific structure and characteristics in the scheme is not described in detail here. The ordinary technicians in the relevant field are aware of all the common technical knowledge in the technical field of the invention before the application date or priority date, can obtain all the existing technologies in the field, and have the ability to apply the conventional experimental means before that date. The ordinary technicians in the relevant field can improve and implement the scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for the ordinary technicians in the relevant field to implement this application. It should be pointed out that for the technicians in this field, without departing from the structure of the present invention, several deformations and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent.

Claims

1. A drone intelligent tracking and automatic return to the nest system, including a drone and a nest; It is characterized in that The drone is equipped with a multimodal sensor and a first wireless communication module; a second wireless communication module is integrated inside the nest; and communication interaction is established between the first wireless communication module and the second wireless communication module; It also includes an intelligent tracking module and an automatic control module; the intelligent tracking module is used to receive and process the operation data and perception data of the drone, perform real-time position tracking of the drone, and determine the mission execution status of the drone, and adjust the flight route of the drone according to the real-time position and mission execution status of the drone; the automatic control module is used to analyze the mission execution status of the drone in real time, and when the mission execution status triggers the return condition, control the drone to switch to the return program; and during the return process of the drone, adjust the return program parameters in combination with the perception data of the drone; the return program parameters include the return route and the return speed.

2. According to claim 1, a drone intelligent tracking and automatic return to the nest system, It is characterized in that The multimodal sensor includes a GPS module, a visual sensor, a lidar module and an inertial measurement unit.

3. The UAV intelligent tracking and automatic return to the nest system according to claim 1, It is characterized in that The drone is also provided with a target tracking module; the target tracking module is provided with a tracking program for controlling the drone to perform a target tracking task; when tracking a target, the target tracking module first controls the drone to take off to an initial height position and hover, and searches for the target at 360° at an initial rotation angular velocity, and triggers the tracking program after detecting the target; The tracking procedure includes: estimating the relative position of the target based on the perception data, and if the target is not in the perception center, controlling the drone to climb or descend or swing until the target is in the perception center; If the target is in the perception center, determine whether the target's relative position is greater than the safety distance; if not, control the drone to retreat until the target's relative position is greater than the safety distance; if so, enter the tracking mode and control the drone to track the target through relative posture.

4. The UAV intelligent tracking and automatic return to the nest system according to claim 1, It is characterized in that When adjusting the return program parameters, the default return route is divided into N route segments, N = S / L; S is the total length of the default return route; L is the real-time maximum perception diameter of the drone; and the parameters are adjusted segment by segment according to the route segment; based on the drone's perception data, when there is a target object in a single route segment, the drone's obstacle avoidance program is called to re-plan the route segment, generate a new route segment, and use the end point of the new route segment as the starting point and the machine nest position as the end point, and make associated adjustments to other route segments based on the shortest path principle.

5. The UAV intelligent tracking and automatic return to the nest system according to claim 4, It is characterized in that When there is no target in a single route segment, increase the return speed; in the last route segment, reduce the return speed and send a landing signal to the aircraft nest.

6. The UAV intelligent tracking and automatic return to the nest system according to claim 2, It is characterized in that The operating data of the drone includes flight parameters and system status data; the flight parameters include flight altitude, latitude and longitude coordinates, flight speed, acceleration, angular velocity and flight direction; the system status data includes power, motor speed, obstacle avoidance system status, satellite lock status and communication signal strength; the perception data includes GPS location data, image data and lidar data.

7. The UAV intelligent tracking and automatic return to the nest system according to claim 6, It is characterized in that When determining the mission execution status of the UAV, the UAV trajectory is analyzed based on the GPS position data of the UAV, and the mission stage of the UAV is confirmed based on the UAV trajectory; the flight status of the UAV is confirmed based on the system status data of the UAV; at the same time, the preset analysis model is used to perform trend analysis on the operation data and perception data of the UAV, respectively, and the fault state and perception state of the UAV are correspondingly confirmed; and an array of the form (a, b, c, d) is formed as the mission execution status of the UAV; among them, a, b, c, and d refer to the mission stage, flight status, perception status, and fault status, respectively.

8. The UAV intelligent tracking and automatic return to the nest system according to claim 7, It is characterized in that The values ​​of a, b, c, and d are 0 or 1. When confirming the mission stage of the drone, when the matching degree of the drone trajectory is consistent with the drone mission route, a takes the value of 1, otherwise it takes the value of 0. When confirming the flight status of the drone, if there is data in the drone system status data that is lower than the preset threshold, b takes the value of 0, otherwise it takes the value of 1. When confirming the perception state of the drone, if the matching degree of the data change trend output by the preset analysis model and the corresponding perception analysis benchmark is less than the threshold, c takes the value of 0, otherwise it takes the value of 1. When confirming the fault state of the drone, if the matching degree of the data change trend output by the preset analysis model and the corresponding fault analysis benchmark is less than the threshold, d takes the value of 0, otherwise it takes the value of 1.

9. The UAV intelligent tracking and automatic return to the nest system according to claim 8, It is characterized in that The return condition includes: any value of 0 appears in the mission execution status of the drone.

10. A method for intelligent tracking and automatic return control of unmanned aerial vehicles. It is characterized in that The method of using a drone intelligent tracking and automatic return-to-home system as claimed in any one of claims 1 to 9 to perform intelligent tracking and automatic return control of a drone comprises the following steps: Step 1: Import the inspection task to the drone; control the machine nest to open and send the drone to perform the inspection task; Step 2, the intelligent tracking module receives and processes the UAV's operating data and perception data, tracks the UAV's real-time position, determines the UAV's mission execution status, and adjusts the UAV's flight route based on the UAV's real-time position and mission execution status; the automation control module analyzes the UAV's mission execution status in real time, and when the mission execution status triggers the return condition, controls the UAV to switch to the return program; and during the UAV's return process, adjusts the return program parameters in combination with the UAV's perception data until the UAV arrives at the machine nest.

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