# Research on Multi Machine Cooperative Autonomous Inspection Strategy for UHV Dense Transmission Channel Based on 5G Technology

Authors: Long Li; Liangliang Cheng; Xiang Li; Lingping Yue; Hongbin Zhang
Journal: Journal of Sensors
 (2022)

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## Abstract

With the continuous progress of UAV technology and 5G technology, the safety and reliability of UAV are gradually improved. In addition, UAV has many advantages, such as light weight, small size, low cost, and fast response in view of the low economy, high labor intensity, low efficiency, and other problems of the traditional inspection of UHV intensive transmission channels. This paper proposes a multi machine collaborative autonomous inspection scheme for UHV dense transmission channels based on 5G technology. This method is to realize the intelligent detection service for UHV dense transmission channels by building a self-service inspection model under the joint cooperation of 5G technology and UAV collaborative operation technology. The research of this paper shows that the multi machine coordinated autonomous inspection scheme of UHV dense transmission channel based on 5G technology constructed in this paper is not only more efficient but also safer than the traditional inspection scheme and single UAV inspection scheme. The related technologies can not only improve the maintenance efficiency of power grid dense transmission channel but also provide theoretical and technical reference for the improvement of multi machine coordinated technology.


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## Body

## 1. Introduction

In order to comprehensively promote the implementation of the national plan, establish a full coverage, three-dimensional and high-precision comprehensive management and control system of the State Grid, and fully realize the goal of “three-dimensional monitoring, rapid response, standardized law enforcement, and informatization of management,” it is necessary to speed up the construction of the supervision capacity of the power grid system, strengthen the layout of major facilities and equipment configuration of lines, and improve the monitoring efficiency and emergency response capacity of power grid lines [1].At present, the scale of power grid construction in China has surpassed that of the United States, ranking first in the world. Six inter provincial power grids have been built in the South, Northwest, East, Central, North, and Northeast China, and more than 500000 kilometers of transmission lines have exceeded 110 kV. Transmission lines of more than 500 kV have become the main power transmission lines in various regions. The annual overall investment in the power industry is about 100 billion yuan, of which 73% are hardware facilities. The proportion of transmission equipment in the construction of the national power grid is becoming larger and larger [2–4]. The expansion of ultra-high voltage and large capacity power lines will cross a variety of complex terrain. China has a vast territory, relatively complex terrain, more hills, and fewer plains. In addition, the complex and changeable meteorological conditions have brought great difficulties to the construction of cross-regional power grid and ultra-high-voltage transmission line projects. In addition, the maintenance and repair after completion. Only relying on the existing inspection means and routine testing cannot meet the requirements of efficient and rapid nor can it achieve good results. How to solve the accuracy and efficiency of power line detection is a major problem perplexing the power industry [5–8].With the increase of power grid users in China year by year, high-voltage transmission lines are spread all over the country. Power enterprises spend a lot of manpower and material resources to maintain and operate every year. In the early years, the power grid maintenance was mainly completed by manual line inspection, which was labor intensive and inefficient. Some lines were subject to terrain factors, resulting in abnormal difficulties and dangers in line inspection. With the development of automation equipment and technology, power supply units have realized that it is more economical and reliable to use automation technology to maintain the power grid and began to study the comprehensive monitoring system of transmission lines [9, 10] more than a decade ago, trying to use various sensors installed on towers and power equipment to monitor the operation of the power grid instead of manual inspection. However, due to the high-voltage transmission lines crossing mountains, the harsh natural environment at the wiring place, the drastic change of temperature, animal feces, electromagnetic interference, and the power supply problems of the monitoring system itself, the system is difficult to operate and maintain effectively, and finally 70% of the current comprehensive monitoring systems of transmission lines are out of operation. At present, the transmission lines of the domestic power grid are still mainly inspected manually [11, 12].From the development experience of foreign advanced countries, power grid monitoring presents three trends: equipment modernization, three-dimensional monitoring, and information integration. The requirement of three-dimensional monitoring will inevitably involve aircraft. Because UAV has many incomparable advantages, it has developed rapidly and its application field has been expanding. Foreign developed countries have successfully applied UAV to power line patrol monitoring. UAV integrating high technology has the advantages of mobility, high efficiency, low cost, multiload tasks, strong adaptability, fast monitoring speed, and high intelligence. The unique advantages of safety and reliability provide a powerful means for the power grid system to better complete the tasks of “planning transmission lines, surveying topographic maps, routine line patrol, and line erection,” which plays an irreplaceable supporting role. Through the pilot, the gradual promotion and application will bring great economic and social benefits [13–19].In view of various problems of manual inspection, power units have successively adopted advanced technologies such as manned helicopters [20, 21], robots [22, 23], and UAVs [17, 18] to inspect high-voltage transmission lines. Although manned helicopter line patrol and robot line patrol are safer and more efficient than manual line patrol, they are difficult to be applied on a large scale due to high cost, difficult technology, and high operating environment requirements; however, unmanned aerial vehicles (UAVs) have attracted extensive attention in this field because of their advantages such as small size, convenience, easy operation, low cost, fast operation speed, and strong adaptability. Relevant data show that the cost of UAV patrol inspection is only 30% of that of manual patrol inspection. Some transmission systems in China have also carried out experimental research on infrared aerial survey lines and explored many experiences and achieved certain results [24–26]. Ground detection research based on national conditions is also being effectively promoted.The application of UAV has not been effectively promoted in China. The concepts and problems in the application process should be solved by reasonable means and methods. The most effective means is to strengthen the technical research and investment in relevant aspects, promote technological innovation, and use high-tech means to overcome the problems of UAV application in the construction of power system. Only by vigorously cultivating technical talents and increasing investment in technological innovation can we provide more effective guarantee for the construction and maintenance of China’s power system [18].The traditional manual line inspection method not only has heavy workload, difficult conditions, but also is inefficient. Especially, it takes a long time, high labor cost, great difficulties, and high risks to inspect transmission lines in mountainous areas and across rivers, as well as during ice disasters, floods, earthquakes, landslides, and nights. At present, it is difficult to complete the manual line inspection method in some line areas and some inspection projects. Some patrol inspection items cannot be completed by conventional methods [19, 27].However, with the development and application of UAV and 5G technology, multi UAV collaboration has been applied in various fields [27–29], such as collaborative investigation, operations, geological exploration, and map drawing, significantly improving the operation efficiency. In the actual UHV transmission channel multi machine coordinated independent inspection project, it has become the consensus of power grid operation and maintenance units at home and abroad to use 5G technology UAV aircraft to carry out power line inspection. UAV has realized electronic, information, and intelligent inspection and improved the work efficiency, emergency rescue level, and power supply reliability of power line inspection. This technology will play a more powerful advantage in the future power engineering construction [30, 31].


## 2. Basic Technology and Theory

### 2.1. 5G Technology

5G network supports various application scenarios of mobile Internet and Internet of things by providing various ways of communication between people, man-machine, and machines. At the same time, 5G network adapts to the flexibility and diversified business requirements of different application scenarios by providing the ability of diversified business requirements and business characteristics, such as ultra wideband, ultra-low delay, massive connection, and ultra-high reliability. Taking business as the center and providing the best user experience efficiently and flexibly are the guiding goal of 5G network system design.


### 2.2. Multi UAV Cooperation Technology

UAV formation is a collective composed of multiple UAVs, but it is not a linear superposition of the functions of a few simple UAVs, but a more complex nonlinear cooperative system. By sharing all or part of the information, the formation members make decisions to make “behaviors” in line with the overall interests, so as to obtain the overall efficiency of “1 + 1 > 2.” With the increase of the number of formation UAVs, the hidden energy will increase exponentially, but the requirements for the corresponding cooperative control technology are also higher. At present, some researchers have begun to study the self-organizing UAV cluster, hoping to skip the collaborative control problem within the UAV cluster and directly obtain the overall efficiency. However, UAV formation cooperative control technology is always an important foundation. Figure1 shows the overall solution of power transmission channel of 5G network connected UAV network.Figure 1
5G networked UAV overall solution.The basic schematic diagram of multi aircraft collaboration technology is shown in Figure2, which mainly includes collaborative task allocation technology, collaborative target positioning technology, collaborative search technology, collaborative track planning technology, and multi aircraft anti-collision technology. These contents intersect with each other, but the emphasis is different. The collaborative task allocation technology is to reasonably allocate the sub-tasks or sub-objectives to be completed to the system members according to the overall task requirements of power grid patrol inspection, so that the tasks can be completed efficiently [18]. When multiple UAVs perform multiple tasks or a task contains multiple targets, reasonable task allocation according to UAV type, load, and other attributes can effectively carry out redundant configuration, so as to improve the probability of task completion. In the process of power grid patrol inspection, the cooperative target location technology uses multiple UAVs to accurately locate, monitor, and detect targets based on economic, security, and other factors. Collaborative search technology is also the basic technology of multi machine collaborative inspection. Under the premise of unknown target, multi UAVs are used to form an inspection team, and appropriate search strategies or algorithms are used to search the fault of UHV Circuit. In addition, it is the multi aircraft anti-collision technology which aims to protect the UAV formation and UHV power transmission lines. It is also a basic technology for multi aircraft cooperation. Reasonable planning and layout should be carried out from the initial planning stage to the final track tracking stage.Figure 2
Schematic diagram of multi machine coordinated patrol inspection.


## 2.1. 5G Technology

5G network supports various application scenarios of mobile Internet and Internet of things by providing various ways of communication between people, man-machine, and machines. At the same time, 5G network adapts to the flexibility and diversified business requirements of different application scenarios by providing the ability of diversified business requirements and business characteristics, such as ultra wideband, ultra-low delay, massive connection, and ultra-high reliability. Taking business as the center and providing the best user experience efficiently and flexibly are the guiding goal of 5G network system design.


## 2.2. Multi UAV Cooperation Technology

UAV formation is a collective composed of multiple UAVs, but it is not a linear superposition of the functions of a few simple UAVs, but a more complex nonlinear cooperative system. By sharing all or part of the information, the formation members make decisions to make “behaviors” in line with the overall interests, so as to obtain the overall efficiency of “1 + 1 > 2.” With the increase of the number of formation UAVs, the hidden energy will increase exponentially, but the requirements for the corresponding cooperative control technology are also higher. At present, some researchers have begun to study the self-organizing UAV cluster, hoping to skip the collaborative control problem within the UAV cluster and directly obtain the overall efficiency. However, UAV formation cooperative control technology is always an important foundation. Figure1 shows the overall solution of power transmission channel of 5G network connected UAV network.Figure 1
5G networked UAV overall solution.The basic schematic diagram of multi aircraft collaboration technology is shown in Figure2, which mainly includes collaborative task allocation technology, collaborative target positioning technology, collaborative search technology, collaborative track planning technology, and multi aircraft anti-collision technology. These contents intersect with each other, but the emphasis is different. The collaborative task allocation technology is to reasonably allocate the sub-tasks or sub-objectives to be completed to the system members according to the overall task requirements of power grid patrol inspection, so that the tasks can be completed efficiently [18]. When multiple UAVs perform multiple tasks or a task contains multiple targets, reasonable task allocation according to UAV type, load, and other attributes can effectively carry out redundant configuration, so as to improve the probability of task completion. In the process of power grid patrol inspection, the cooperative target location technology uses multiple UAVs to accurately locate, monitor, and detect targets based on economic, security, and other factors. Collaborative search technology is also the basic technology of multi machine collaborative inspection. Under the premise of unknown target, multi UAVs are used to form an inspection team, and appropriate search strategies or algorithms are used to search the fault of UHV Circuit. In addition, it is the multi aircraft anti-collision technology which aims to protect the UAV formation and UHV power transmission lines. It is also a basic technology for multi aircraft cooperation. Reasonable planning and layout should be carried out from the initial planning stage to the final track tracking stage.Figure 2
Schematic diagram of multi machine coordinated patrol inspection.


## 3. Multi Machine Cooperative Autonomous Inspection Strategy for UHV Dense Transmission Channel Based on 5G Technology

The detailed tasks of UAV fine patrol inspection of high-voltage transmission lines are as follows: the UAV flies along the conductor at a short distance and low speed according to the specified path and uses the all-optical spectrum imager mounted on the aircraft to detect and record the conductor defects. Once any suspicious defects are found, it is necessary to hover close to the place for shooting; identify threatening obstacles (such as collapsed trees) and towers along the line; when the UAV approaches the tower, it shall identify each interest point (insulator, hardware, etc.) on the tower and hover one by one to detect whether there is any defect or fault; once the UAV finds the threatening obstacles or defects on the conductors and towers, it shall immediately report to the ground station so that the relevant units can take maintenance measures in time. During flight, the UAV must avoid touching high-voltage lines, towers, and other obstacles and keep a proper distance from the measured object.


### 3.1. Construction of Multi Machine Collaborative Inspection System

The multi UAV system mainly includes the body, power unit, flight control and navigation system, data link system, launch and recovery system, and mission equipment. The specific composition structure is shown in Figure3. The flight control system, also known as the flight management and control system, is equivalent to the “heart” part of the UAV system. It has an important impact on the stability of the UAV and the reliability, accuracy, and real-time of data transmission, and plays a decisive role in its flight performance. The data link system can ensure the accurate transmission of remote control commands, as well as the real-time and reliability of UAV receiving and sending information, so as to ensure the timely effectiveness of information feedback and the smooth and accurate completion of tasks. The launch and recovery system ensures that the UAV can take off smoothly and safely fall back from the sky to the ground after completing the mission. Task equipment is the equipment carried by UAV when it performs corresponding tasks.Figure 3
Composition block diagram of Power Patrol UAV system.


### 3.2. Multi Aircraft Cooperative Patrol Flight Control

The generation process of the preset controller is as follows.(1)
Training samples. Let experienced staff operate the remote control UAV with the same hardware specifications as the autonomous line patrol UAV to perform sampling flight for different towers according to the route shown in Figure4(b) and synchronously record relevant data, including real-time video recorded by the imager and flight parameters such as aircraft real-time position, attitude, speed, and tower model. For asymmetric towers (including the case with odd number of vertices as shown in Figure 4(b)), both sides shall be trained and recorded, respectively. For each type of tower, multiple training is required. The more training times, the more accurate the samples are(2)
Data processing. Select and extract key information from the recorded data, including the motion trajectory of the interest points captured from the flight video, calculate the speed increment in motion, the relative position and trajectory between the UAV and the given tower center, and decompose the flight process into action steps(3)
Information fitting. The regression algorithm is used to fit the extracted information, and the appropriate fuzzy control algorithm is selected to form a preset controller. In addition to forming corresponding controllers for each type of tower, since the main points of interest are insulator strings, a general tower patrol controller can also be formed according to the decomposed insulator string tracking action; it is stipulated that the detection of the tower by UAV starts from the top, so the flight process of UAV finding and hovering near the first point of interest to be inspected from above the conductor also needs to be simulated and integrated into the general controllerFigure 4
Schematic diagram of interest point path for single UAV and multi UAV cooperative inspection.
(a)
Single machine patrol inspection(b)
Multi machine patrol inspectionDue to the complex flight environment, the distance between the fuselage and the tower, cables, obstacles, and other entities must be constrained in real time in the actual flight process. The constraint method is to use the focal length information of the imager to judge the distance in real time. When the distance between the UAV and some entities is less than the minimum safe distance and has not yet reached the position suitable for the observation point of interest, trace back a certain distance according to the original route, list the position of the entity as a reference, and re-plan the approach route until the observation position is reached. If the number of backtracking times on the way close to a certain point of interest reaches a certain number, it will give up approaching the point of interest, hover on the spot to observe the point of interest before the last backtracking, and move to the next point of interest after backtracking.After checking the points of interest, the two UAVs safely evacuated to both sides of the conductor along the opposite direction of the tower to perform the patrol inspection of the conductor.


### 3.3. Multi Machine Collaborative Patrol Scheme Setting

In the inspection process of high-voltage dense transmission channels, there are generally two types of inspection objectives; one is the inspection of high-voltage transmission towers, and the other is the inspection of high-voltage transmission conductors. Taking the dual computer cooperative patrol inspection as an example, the schematic diagram of the above two types of target patrol inspection is shown in Figures4 and 5. During the inspection of high-voltage transmission towers, the points of interest on the towers are generally symmetrically distributed on both sides of the tower. Take the 220 kV double circuit tension angle steel tower as an example. According to industry regulations, UAVs are not allowed to fly over the top of high-voltage transmission towers. When inspecting towers, a single remote control UAV must first detect the points of interest on one side of the tower from top to bottom according to the route shown in Figure 4(a), then fly from below the conductor to the other side of the tower along the path shown by the red arrow, and then detect the remaining points of interest from bottom to top. For the path part marked by the red arrow, the flight environment is very complex. It is difficult to complete the design of autonomous flight control only by relying on the information of camera and altitude sensor, and it is easy to touch the tower or cable by mistake during the flight, causing safety accidents. Figure 4(b) shows the path of two UAVs to inspect the strain tower. The two UAVs only need to detect the points of interest on one side of the tower without crossing the tower and can safely evacuate in the direction away from the tower after the detection.Figure 5
Schematic diagram of multi UAV cooperative inspection of high-voltage conductor.During the inspection of high-voltage transmission lines, two UAVs flew in parallel along both sides of the lines. Keep a certain distance from the conductor during flight and adjust the orientation of the imager as shown in the figure, so that its field of vision can take into account the flight environment of another UAV while observing the conductor. When it is detected that there are threatening obstacles (fallen trees in the figure) on the flight path of the opposite UAV, it is notified to avoid obstacles, so as to cooperate to avoid safety accidents. During line patrol inspection, the two aircrafts must maintain synchronous flight: when a UAV hovers to shoot a suspected line, the opposite UAV will also hover to assist in shooting if it is not in obstacle avoidance mode.


### 3.4. Multi Aircraft Cooperative Patrol Flight Algorithm

In order to effectively support the cooperative positioning among multiple UAVs in the GPS denial environment, this paper proposes a cooperative positioning algorithm framework based on positioning confidence, and its structure is shown in Figure2. The framework consists of sensor data acquisition, relative positioning, cooperative positioning based on positioning confidence, and heterogeneous sensor fusion mechanism based on EKF. Among them, the sensor data acquisition mechanism obtains data from UWB, IMU, GPS, and other sensors as the input of relative positioning mechanism and EKF heterogeneous fusion mechanism, respectively. At the same time, UWB sensors can realize information sharing between UAVs in the process of ranging. The function of the relative positioning mechanism is to enhance the relative distance and relative speed data between UAVs measured in real time by interpolation method and modify the state judge, so as to permanently stimulate the relative positioning convergence; the function of cooperative localization mechanism is to update the localization confidence and carry out adaptive cooperative localization; EKF heterogeneous fusion mechanism fuses IMU, GPS sensor data, and collaborative positioning data through extended Kalman filter and outputs UAV attitude information including velocity, positioning, and acceleration.


#### 3.4.1. Positioning Confidence

The positioning reliability proposed in this paper is different from the traditional concepts of confidence interval and confidence degree in the sense of mathematics but is used to describe the accuracy of autonomous positioning estimation of UAV in the GPS denial environment: affected by environmental changes, when GPS is available, the positioning confidence degree is set to the highest value, indicating that the positioning is reliable; when there is no GPS signal, the positioning confidence is gradually reduced by the error accumulation rate, but it can be corrected by cooperative positioning.In this paper, parameterQi,k is defined to describe the fixed position reliability of UAV i during the k-th positioning estimation, and its value range is (0, 1]. When the GPS signal is received, correct the current positioning estimation and make Qi,k+1=1, which means that the current positioning is reliable; when there is no GPS signal, make Qi,k+1=Qi,k∗0.95; when there are other cooperative UAVs in the neighborhood, update and correct the calculation according to the positioning information of the cooperative UAV and its positioning confidence, as shown in equation (1).
(1)Qi,k=Qi,k+1+∑j∈Ni0.011+eQi,k−Qj,kμP−P^j,k−P^i,k−X^i,kij,where X^i,kij represents the relative positioning between UAV i and UAV j estimated at time k. P^i,k represents the self-positioning estimation of UAV i. uP is the collaboration threshold value; it is used to measure the difference between the observed positioning of other UAVs and their own estimated positioning: when the difference between the two is greater than this value, it is considered that one party’s positioning is not credible; when the difference between the two is less than this value, it is considered that there is little difference between the observed value and the estimated value, and the positioning is reliable. After the above update, the positioning confidence Qi,k will show the following trends:
(1)
When the UAV GPS is available, the positioning confidence is kept at 1, indicating that its own positioning is reliable and can coordinate with other UAVs for positioning(2)
When the UAV flies into the GPS denial environment, the positioning confidence is multiplied by a coefficient of 0.95 every time step until it gets rid of the GPS denial environment, indicating that its estimated positioning accuracy gradually decreases with the increase of continuous time in the GPS denial environment(3)
When there are other UAVs in the neighborhood of the UAV, the position estimation can be corrected through the cooperation of other UAVs, and the position determination reliability can be further evaluated according to formula (2):(a)
When the position determination reliability of cooperative UAV is higher than its own position determination reliability, and the difference between cooperative positioning and autonomous positioning is less thanup, it means that the cooperative positioning is similar to its own positioning, and the current positioning is more reliable, so the position determination reliability of its own is improved(b)
When the position determination reliability of cooperative UAV is higher than its own position determination reliability, and the difference between cooperative positioning and autonomous positioning is greater thanup, it means that the current self-estimated positioning deviation is large and the reliability is low, so the self-position determination reliability is reduced(c)
When the position determination reliability of the cooperative UAV is lower than the self-positioning confidence, and the0.01/1+eQi,k+1−Qj,k+1 term approaches 0, the self-positioning confidence will not change much


#### 3.4.2. Cooperative Positioning Algorithm

The cooperative positioning algorithm proposed in this paper is different from the traditional multi UAV cooperative positioning method based on graphic geometry. This method is not affected by the spatial distribution of UAVs. In the process of relative positioning, the autonomous positioning results and positioning confidence information are exchanged, the cooperative positioning weight is calculated by using the positioning confidence, the autonomous positioning results of all UAVs in the neighborhood are fused to modify their own positioning results, and the positioning confidence is updated. For the synchronous cluster system, the calculation method of the fixed position reliability is shown in formula (2).
(2)P^i,k+1=Qi,kQi,k+∑j∈NirQj,kP^i,k+Tvi,k+∑j∈NirQj,kQi,k+∑j∈NirQj,kP^j,k−X^i,kij.Equation (2) requires the UAV in the cluster to synchronize the measurement cycle, which is difficult to deploy in practical applications. Therefore, we modify equation (2) to equation (3):
(3)P^i,k=Qi,kQi,k+rQj,kP^i,k+rQj,kQi,k+rQj,kP^j,k−X^i,kij.According to formula (3), in a distributed system such as UAV cluster, UAVs do not need to be fully synchronized; in each measurement cycle, as long as there is a relative positioning process, cooperative positioning can be carried out through the transmitted data, and the cooperative positioning results can be used to correct their own positioning, wherein r is the relative factor, which is determined by the relative positioning error. Because the cooperative positioning results not only retain the positioning error of the cooperative UAV but also the relative positioning error. Therefore, the positioning weight of the cooperative UAV needs to be multiplied by the relative factor r in addition to using Qj,k.


### 3.5. Construction of Intelligent Management Platform for Multi Machine Collaborative Inspection

In combination with the actual situation of the transmission line and the existing UAV management and control platform architecture, the architecture design of the network management platform of the 5G UAV for intelligent inspection of power transmission and transformation is shown in Figure6. The inspection UAV equipped with 5G modules is used at the front end of the data acquisition to collect the image information of the tower body and auxiliary facilities for discovering various defects in the tower, ground wire, insulator, hardware, stay wire, foundation, and auxiliary facilities, so as to achieve high-precision visual inspection application requirements; the mode of combining 5G+ Internet regions is adopted to realize the interaction between internal and external networks.Figure 6
Intelligent management platform for multi machine collaborative inspection.The overall platform construction goal is expected to be achieved: support synchronous access to ≥1000 patrol UAVs; test and realize the remote control of UAV over 1000 km; the system can display more than 50 groups of patrol inspection return signals in real time and support free switching; the real-time data return delay is less than 1 ms; multi machine collaborative task planning function (supports multiple mount types and multiple models).


## 3.1. Construction of Multi Machine Collaborative Inspection System

The multi UAV system mainly includes the body, power unit, flight control and navigation system, data link system, launch and recovery system, and mission equipment. The specific composition structure is shown in Figure3. The flight control system, also known as the flight management and control system, is equivalent to the “heart” part of the UAV system. It has an important impact on the stability of the UAV and the reliability, accuracy, and real-time of data transmission, and plays a decisive role in its flight performance. The data link system can ensure the accurate transmission of remote control commands, as well as the real-time and reliability of UAV receiving and sending information, so as to ensure the timely effectiveness of information feedback and the smooth and accurate completion of tasks. The launch and recovery system ensures that the UAV can take off smoothly and safely fall back from the sky to the ground after completing the mission. Task equipment is the equipment carried by UAV when it performs corresponding tasks.Figure 3
Composition block diagram of Power Patrol UAV system.


## 3.2. Multi Aircraft Cooperative Patrol Flight Control

The generation process of the preset controller is as follows.(1)
Training samples. Let experienced staff operate the remote control UAV with the same hardware specifications as the autonomous line patrol UAV to perform sampling flight for different towers according to the route shown in Figure4(b) and synchronously record relevant data, including real-time video recorded by the imager and flight parameters such as aircraft real-time position, attitude, speed, and tower model. For asymmetric towers (including the case with odd number of vertices as shown in Figure 4(b)), both sides shall be trained and recorded, respectively. For each type of tower, multiple training is required. The more training times, the more accurate the samples are(2)
Data processing. Select and extract key information from the recorded data, including the motion trajectory of the interest points captured from the flight video, calculate the speed increment in motion, the relative position and trajectory between the UAV and the given tower center, and decompose the flight process into action steps(3)
Information fitting. The regression algorithm is used to fit the extracted information, and the appropriate fuzzy control algorithm is selected to form a preset controller. In addition to forming corresponding controllers for each type of tower, since the main points of interest are insulator strings, a general tower patrol controller can also be formed according to the decomposed insulator string tracking action; it is stipulated that the detection of the tower by UAV starts from the top, so the flight process of UAV finding and hovering near the first point of interest to be inspected from above the conductor also needs to be simulated and integrated into the general controllerFigure 4
Schematic diagram of interest point path for single UAV and multi UAV cooperative inspection.
(a)
Single machine patrol inspection(b)
Multi machine patrol inspectionDue to the complex flight environment, the distance between the fuselage and the tower, cables, obstacles, and other entities must be constrained in real time in the actual flight process. The constraint method is to use the focal length information of the imager to judge the distance in real time. When the distance between the UAV and some entities is less than the minimum safe distance and has not yet reached the position suitable for the observation point of interest, trace back a certain distance according to the original route, list the position of the entity as a reference, and re-plan the approach route until the observation position is reached. If the number of backtracking times on the way close to a certain point of interest reaches a certain number, it will give up approaching the point of interest, hover on the spot to observe the point of interest before the last backtracking, and move to the next point of interest after backtracking.After checking the points of interest, the two UAVs safely evacuated to both sides of the conductor along the opposite direction of the tower to perform the patrol inspection of the conductor.


## 3.3. Multi Machine Collaborative Patrol Scheme Setting

In the inspection process of high-voltage dense transmission channels, there are generally two types of inspection objectives; one is the inspection of high-voltage transmission towers, and the other is the inspection of high-voltage transmission conductors. Taking the dual computer cooperative patrol inspection as an example, the schematic diagram of the above two types of target patrol inspection is shown in Figures4 and 5. During the inspection of high-voltage transmission towers, the points of interest on the towers are generally symmetrically distributed on both sides of the tower. Take the 220 kV double circuit tension angle steel tower as an example. According to industry regulations, UAVs are not allowed to fly over the top of high-voltage transmission towers. When inspecting towers, a single remote control UAV must first detect the points of interest on one side of the tower from top to bottom according to the route shown in Figure 4(a), then fly from below the conductor to the other side of the tower along the path shown by the red arrow, and then detect the remaining points of interest from bottom to top. For the path part marked by the red arrow, the flight environment is very complex. It is difficult to complete the design of autonomous flight control only by relying on the information of camera and altitude sensor, and it is easy to touch the tower or cable by mistake during the flight, causing safety accidents. Figure 4(b) shows the path of two UAVs to inspect the strain tower. The two UAVs only need to detect the points of interest on one side of the tower without crossing the tower and can safely evacuate in the direction away from the tower after the detection.Figure 5
Schematic diagram of multi UAV cooperative inspection of high-voltage conductor.During the inspection of high-voltage transmission lines, two UAVs flew in parallel along both sides of the lines. Keep a certain distance from the conductor during flight and adjust the orientation of the imager as shown in the figure, so that its field of vision can take into account the flight environment of another UAV while observing the conductor. When it is detected that there are threatening obstacles (fallen trees in the figure) on the flight path of the opposite UAV, it is notified to avoid obstacles, so as to cooperate to avoid safety accidents. During line patrol inspection, the two aircrafts must maintain synchronous flight: when a UAV hovers to shoot a suspected line, the opposite UAV will also hover to assist in shooting if it is not in obstacle avoidance mode.


## 3.4. Multi Aircraft Cooperative Patrol Flight Algorithm

In order to effectively support the cooperative positioning among multiple UAVs in the GPS denial environment, this paper proposes a cooperative positioning algorithm framework based on positioning confidence, and its structure is shown in Figure2. The framework consists of sensor data acquisition, relative positioning, cooperative positioning based on positioning confidence, and heterogeneous sensor fusion mechanism based on EKF. Among them, the sensor data acquisition mechanism obtains data from UWB, IMU, GPS, and other sensors as the input of relative positioning mechanism and EKF heterogeneous fusion mechanism, respectively. At the same time, UWB sensors can realize information sharing between UAVs in the process of ranging. The function of the relative positioning mechanism is to enhance the relative distance and relative speed data between UAVs measured in real time by interpolation method and modify the state judge, so as to permanently stimulate the relative positioning convergence; the function of cooperative localization mechanism is to update the localization confidence and carry out adaptive cooperative localization; EKF heterogeneous fusion mechanism fuses IMU, GPS sensor data, and collaborative positioning data through extended Kalman filter and outputs UAV attitude information including velocity, positioning, and acceleration.


### 3.4.1. Positioning Confidence

The positioning reliability proposed in this paper is different from the traditional concepts of confidence interval and confidence degree in the sense of mathematics but is used to describe the accuracy of autonomous positioning estimation of UAV in the GPS denial environment: affected by environmental changes, when GPS is available, the positioning confidence degree is set to the highest value, indicating that the positioning is reliable; when there is no GPS signal, the positioning confidence is gradually reduced by the error accumulation rate, but it can be corrected by cooperative positioning.In this paper, parameterQi,k is defined to describe the fixed position reliability of UAV i during the k-th positioning estimation, and its value range is (0, 1]. When the GPS signal is received, correct the current positioning estimation and make Qi,k+1=1, which means that the current positioning is reliable; when there is no GPS signal, make Qi,k+1=Qi,k∗0.95; when there are other cooperative UAVs in the neighborhood, update and correct the calculation according to the positioning information of the cooperative UAV and its positioning confidence, as shown in equation (1).
(1)Qi,k=Qi,k+1+∑j∈Ni0.011+eQi,k−Qj,kμP−P^j,k−P^i,k−X^i,kij,where X^i,kij represents the relative positioning between UAV i and UAV j estimated at time k. P^i,k represents the self-positioning estimation of UAV i. uP is the collaboration threshold value; it is used to measure the difference between the observed positioning of other UAVs and their own estimated positioning: when the difference between the two is greater than this value, it is considered that one party’s positioning is not credible; when the difference between the two is less than this value, it is considered that there is little difference between the observed value and the estimated value, and the positioning is reliable. After the above update, the positioning confidence Qi,k will show the following trends:
(1)
When the UAV GPS is available, the positioning confidence is kept at 1, indicating that its own positioning is reliable and can coordinate with other UAVs for positioning(2)
When the UAV flies into the GPS denial environment, the positioning confidence is multiplied by a coefficient of 0.95 every time step until it gets rid of the GPS denial environment, indicating that its estimated positioning accuracy gradually decreases with the increase of continuous time in the GPS denial environment(3)
When there are other UAVs in the neighborhood of the UAV, the position estimation can be corrected through the cooperation of other UAVs, and the position determination reliability can be further evaluated according to formula (2):(a)
When the position determination reliability of cooperative UAV is higher than its own position determination reliability, and the difference between cooperative positioning and autonomous positioning is less thanup, it means that the cooperative positioning is similar to its own positioning, and the current positioning is more reliable, so the position determination reliability of its own is improved(b)
When the position determination reliability of cooperative UAV is higher than its own position determination reliability, and the difference between cooperative positioning and autonomous positioning is greater thanup, it means that the current self-estimated positioning deviation is large and the reliability is low, so the self-position determination reliability is reduced(c)
When the position determination reliability of the cooperative UAV is lower than the self-positioning confidence, and the0.01/1+eQi,k+1−Qj,k+1 term approaches 0, the self-positioning confidence will not change much


### 3.4.2. Cooperative Positioning Algorithm

The cooperative positioning algorithm proposed in this paper is different from the traditional multi UAV cooperative positioning method based on graphic geometry. This method is not affected by the spatial distribution of UAVs. In the process of relative positioning, the autonomous positioning results and positioning confidence information are exchanged, the cooperative positioning weight is calculated by using the positioning confidence, the autonomous positioning results of all UAVs in the neighborhood are fused to modify their own positioning results, and the positioning confidence is updated. For the synchronous cluster system, the calculation method of the fixed position reliability is shown in formula (2).
(2)P^i,k+1=Qi,kQi,k+∑j∈NirQj,kP^i,k+Tvi,k+∑j∈NirQj,kQi,k+∑j∈NirQj,kP^j,k−X^i,kij.Equation (2) requires the UAV in the cluster to synchronize the measurement cycle, which is difficult to deploy in practical applications. Therefore, we modify equation (2) to equation (3):
(3)P^i,k=Qi,kQi,k+rQj,kP^i,k+rQj,kQi,k+rQj,kP^j,k−X^i,kij.According to formula (3), in a distributed system such as UAV cluster, UAVs do not need to be fully synchronized; in each measurement cycle, as long as there is a relative positioning process, cooperative positioning can be carried out through the transmitted data, and the cooperative positioning results can be used to correct their own positioning, wherein r is the relative factor, which is determined by the relative positioning error. Because the cooperative positioning results not only retain the positioning error of the cooperative UAV but also the relative positioning error. Therefore, the positioning weight of the cooperative UAV needs to be multiplied by the relative factor r in addition to using Qj,k.


## 3.4.1. Positioning Confidence

The positioning reliability proposed in this paper is different from the traditional concepts of confidence interval and confidence degree in the sense of mathematics but is used to describe the accuracy of autonomous positioning estimation of UAV in the GPS denial environment: affected by environmental changes, when GPS is available, the positioning confidence degree is set to the highest value, indicating that the positioning is reliable; when there is no GPS signal, the positioning confidence is gradually reduced by the error accumulation rate, but it can be corrected by cooperative positioning.In this paper, parameterQi,k is defined to describe the fixed position reliability of UAV i during the k-th positioning estimation, and its value range is (0, 1]. When the GPS signal is received, correct the current positioning estimation and make Qi,k+1=1, which means that the current positioning is reliable; when there is no GPS signal, make Qi,k+1=Qi,k∗0.95; when there are other cooperative UAVs in the neighborhood, update and correct the calculation according to the positioning information of the cooperative UAV and its positioning confidence, as shown in equation (1).
(1)Qi,k=Qi,k+1+∑j∈Ni0.011+eQi,k−Qj,kμP−P^j,k−P^i,k−X^i,kij,where X^i,kij represents the relative positioning between UAV i and UAV j estimated at time k. P^i,k represents the self-positioning estimation of UAV i. uP is the collaboration threshold value; it is used to measure the difference between the observed positioning of other UAVs and their own estimated positioning: when the difference between the two is greater than this value, it is considered that one party’s positioning is not credible; when the difference between the two is less than this value, it is considered that there is little difference between the observed value and the estimated value, and the positioning is reliable. After the above update, the positioning confidence Qi,k will show the following trends:
(1)
When the UAV GPS is available, the positioning confidence is kept at 1, indicating that its own positioning is reliable and can coordinate with other UAVs for positioning(2)
When the UAV flies into the GPS denial environment, the positioning confidence is multiplied by a coefficient of 0.95 every time step until it gets rid of the GPS denial environment, indicating that its estimated positioning accuracy gradually decreases with the increase of continuous time in the GPS denial environment(3)
When there are other UAVs in the neighborhood of the UAV, the position estimation can be corrected through the cooperation of other UAVs, and the position determination reliability can be further evaluated according to formula (2):(a)
When the position determination reliability of cooperative UAV is higher than its own position determination reliability, and the difference between cooperative positioning and autonomous positioning is less thanup, it means that the cooperative positioning is similar to its own positioning, and the current positioning is more reliable, so the position determination reliability of its own is improved(b)
When the position determination reliability of cooperative UAV is higher than its own position determination reliability, and the difference between cooperative positioning and autonomous positioning is greater thanup, it means that the current self-estimated positioning deviation is large and the reliability is low, so the self-position determination reliability is reduced(c)
When the position determination reliability of the cooperative UAV is lower than the self-positioning confidence, and the0.01/1+eQi,k+1−Qj,k+1 term approaches 0, the self-positioning confidence will not change much


## 3.4.2. Cooperative Positioning Algorithm

The cooperative positioning algorithm proposed in this paper is different from the traditional multi UAV cooperative positioning method based on graphic geometry. This method is not affected by the spatial distribution of UAVs. In the process of relative positioning, the autonomous positioning results and positioning confidence information are exchanged, the cooperative positioning weight is calculated by using the positioning confidence, the autonomous positioning results of all UAVs in the neighborhood are fused to modify their own positioning results, and the positioning confidence is updated. For the synchronous cluster system, the calculation method of the fixed position reliability is shown in formula (2).
(2)P^i,k+1=Qi,kQi,k+∑j∈NirQj,kP^i,k+Tvi,k+∑j∈NirQj,kQi,k+∑j∈NirQj,kP^j,k−X^i,kij.Equation (2) requires the UAV in the cluster to synchronize the measurement cycle, which is difficult to deploy in practical applications. Therefore, we modify equation (2) to equation (3):
(3)P^i,k=Qi,kQi,k+rQj,kP^i,k+rQj,kQi,k+rQj,kP^j,k−X^i,kij.According to formula (3), in a distributed system such as UAV cluster, UAVs do not need to be fully synchronized; in each measurement cycle, as long as there is a relative positioning process, cooperative positioning can be carried out through the transmitted data, and the cooperative positioning results can be used to correct their own positioning, wherein r is the relative factor, which is determined by the relative positioning error. Because the cooperative positioning results not only retain the positioning error of the cooperative UAV but also the relative positioning error. Therefore, the positioning weight of the cooperative UAV needs to be multiplied by the relative factor r in addition to using Qj,k.


## 3.5. Construction of Intelligent Management Platform for Multi Machine Collaborative Inspection

In combination with the actual situation of the transmission line and the existing UAV management and control platform architecture, the architecture design of the network management platform of the 5G UAV for intelligent inspection of power transmission and transformation is shown in Figure6. The inspection UAV equipped with 5G modules is used at the front end of the data acquisition to collect the image information of the tower body and auxiliary facilities for discovering various defects in the tower, ground wire, insulator, hardware, stay wire, foundation, and auxiliary facilities, so as to achieve high-precision visual inspection application requirements; the mode of combining 5G+ Internet regions is adopted to realize the interaction between internal and external networks.Figure 6
Intelligent management platform for multi machine collaborative inspection.The overall platform construction goal is expected to be achieved: support synchronous access to ≥1000 patrol UAVs; test and realize the remote control of UAV over 1000 km; the system can display more than 50 groups of patrol inspection return signals in real time and support free switching; the real-time data return delay is less than 1 ms; multi machine collaborative task planning function (supports multiple mount types and multiple models).


## 4. Design of Simulation Scenario for Multi Machine Patrol Inspection of UHV Dense Transmission Channel Based on 5G Technology

In order to verify the inspection effect of the multi machine inspection system constructed in this paper, this section will build a multi UAV Flight Scene in the gazebo simulation environment and conduct experimental analysis and effect evaluation on the collaborative positioning algorithm proposed in this paper. The selected UAV model is Dajiang M300 four rotor UAV of Dajiang Innovation Technology Co., Ltd. [21], as shown in Figure 7, and the power transmission and transformation line site used for simulation is shown in Figure 8. The/fire/command/trajectory topic provided by the UAV model controls the UAV flight, so that the four UAVs fly along a fixed trajectory or a random trajectory and build different simulation scenarios, respectively.Figure 7
Dajiang M300 - UAV Simulation Scenario.Figure 8
Schematic diagram of power transmission and transformation line simulation site.Set the relevant parameters of relative positioning and collaborative positioning estimation as listed in Table1. Set the UWB ranging range of 10 m and the ranging frequency of 40 Hz. Each ranging process will share its estimated speed and positioning information with other UAVs. The simulation flight time is 300 s.Table 1
Related parameters of multi UAV cooperative positioning experiment.
Relative positioning parametersValueCollaborative positioning parametersValueT0.025 sT0.025 sγ0.1r0.7v¯5.0 m/sup5.0 mεkd,εkḋ0.05 m/0.05 m/sRanging range10.0 mεk0.5 m/sSimulated flight time300 sThe relevant parameters of relative positioning and cooperative positioning estimation are set as listed in Table1. The UWB ranging range is set to 10.0 m, and the ranging frequency is 40 Hz. Each ranging process will share its estimated speed and positioning information with other UAVs, and the simulation flight time is 300 s. In order to fully reflect the cooperative positioning effect, this paper has carried out the comparative experiments of multi UAV cooperative positioning of imu/gps and imu/gps/vo combined positioning systems in the GPS denial environment (blocking and restricted environment). The different configuration settings in the experiment are as follows.Configuration 1: use IMU/GPS integrated positioning, do not add collaborative positioning correction, turn on GPS reject signal in the 10th s of simulation, and all UAV GPS will be invalid.Configuration 2: use IMU/GPS integrated positioning, add collaborative positioning correction, turn on the GPS rejection signal in the 10s of simulation, and all UAV GPS will be invalid.Configuration 3: use IMU/GPS/VO combined positioning, do not add collaborative positioning correction, turn on the GPS rejection signal in the 10 s of simulation, and all UAV GPS will be invalid.Configuration 4: use IMU/GPS/VO combined positioning, add collaborative positioning correction, turn on the GPS reject signal in the 10 s of simulation, and all UAV GPS will be invalid.


## 5. Analysis of Experimental Results

The test results of the 200 km UHV dense transmission channel shown in Figure9 are shown in Table 2, where Table 2, respectively, reflects the average value of the fault detection error of the UAV on the transmission line under configuration 1; figure average value of fault detection error of UAV on transmission line under configuration 2; mean value of fault detection error of UAV on transmission line under configuration 3; mean value of fault detection error of UAV on transmission line under configuration 4.Figure 9
Schematic diagram of fault detection line for 200 km UHV dense transmission channel.Table 2
Localization average error ± standard deviation table in random trajectory flight simulation experiment.
UAVLocalization average error ± standard deviation table in random trajectory flight simulation experimentConfiguration 1 (m)Configuration 2 (m)Configuration 3 (m)Configuration 4 (m)111.2 ± 2.96.4 ± 1.81.9 ± 0.90.6 ± 0.1217.8 ± 1.910.3 ± 2.12.1 ± 1.40.2 ± 0.05319.9 ± 3.78.8 ± 3.21.9 ± 0.80.4 ± 0.149.7 ± 5.46.6 ± 2.01.2 ± 1.00.3 ± 0.1Based on the fault inspection error curve of the UAV configured in Figure4, it can be found that the error of the UAV using IMU/GPS/VO combined positioning in the inspection of high-voltage transmission lines is significantly lower than that using IMU/GPS combined positioning, which proves the superiority of IMU/GPS/VO combined positioning in the inspection process of UAV; comparing configuration 1, configuration 3, configuration 2, and configuration 4, it can be found that the fault detection accuracy of UAV in UHV dense transmission channel can be significantly improved and its error can also be significantly improved after adding collaborative positioning. The minimum error of UAV 2 can be reduced to 0.2 m. Therefore, the multi machine collaborative autonomous inspection strategy based on 5G technology is significantly better than the single machine inspection strategy.


## 6. Conclusion

With the growth of power grid users in China year by year, high-voltage transmission lines are all over the country. Power enterprises inevitably need to spend a lot of human and material resources to maintain and operate every year. Using traditional inspection and maintenance technology is difficult to meet the current huge power grid demand. However, with the development and progress of 5G technology and unmanned aerial vehicle technology, the multi machine coordinated independent inspection strategy of UHV dense transmission channels based on 5G technology is a more complex system. If used improperly, the resulting disasters cannot be ignored. Therefore, this paper summarizes 5G technology, the design of the overall architecture of multi machine cooperative inspection, the flight control and scheme setting of multi machine cooperative inspection, the flight algorithm of multi machine cooperative inspection, and the construction of intelligent management platform of multi machine cooperative inspection. In addition, based on this, a multi machine cooperative autonomous inspection strategy suitable for UHV dense transmission channel is constructed. Finally, based on the simulation experiment, the fault detection of 200 km UHV dense transmission channel is simulated. The experiment shows that the multi machine cooperative autonomous inspection scheme of UHV dense transmission channel based on 5G technology can greatly reduce the fault inspection error compared with the traditional inspection scheme and the single UAV inspection scheme, and it is not only more efficient but also more secure.



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*Source: 8524817-2022-08-25.xml*