A tethered unmanned aerial vehicle based on ultrasonic positioning and a control method thereof

Abstract

The application discloses a tethered unmanned aerial vehicle based on ultrasonic positioning and a control method thereof, and relates to the technical field of unmanned aerial vehicle control. The application discloses a tethered unmanned aerial vehicle based on ultrasonic positioning and a control method thereof. The application integrates ultrasonic positioning technology and tethered unmanned aerial vehicle control into unmanned aerial vehicle control in depth, realizes high intelligence, and significantly improves the operation efficiency of the unmanned aerial vehicle in a complex environment. The method not only ensures that the unmanned aerial vehicle can accurately follow a preplanned route, but also dynamically optimizes a detection process through multi-path ultrasonic detection and real-time echo analysis, and effectively deals with various environmental disturbances. Meanwhile, in combination with image completion and intelligent path planning, the method guarantees the integrity and reliability of detection data, thereby greatly improving the automation level, precision and safety of an inspection task, and being suitable for efficient inspection of bridges, pipelines and various infrastructures.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) control technology, and in particular to a tethered UAV based on ultrasonic positioning and its control method. Background Technology

[0002] In the field of UAV control technology, the core of ultrasonic path planning is a reactive behavior control strategy based on real-time environmental perception. By continuously acquiring local distance information through ultrasonic sensors, the flight trajectory is directly and quickly generated or corrected. Specifically, during flight, the ultrasonic sensor array on the UAV emits high-frequency sound waves forward and in the circumferential sector and receives echoes. By measuring the echo time, the flight control UAV can calculate the precise distance between the UAV and obstacles in various directions in real time, thereby constructing a continuously updated local repulsive field in the algorithm, which drives the UAV to decelerate, hover, or detour. This mechanism enables the UAV to exhibit sensitive obstacle avoidance and basic tracking capabilities in unknown or dynamically changing high-altitude inspection environments, such as when shuttling through steel trusses or flying around large equipment, realizing an online real-time path planning based on local environmental interaction.

[0003] For example, Chinese invention patent CN112912811B discloses a method, apparatus, and drone for unmanned aerial vehicle (UAV) path planning, belonging to the field of robot control technology. The UAV path planning method includes: acquiring a depth map of the environment in front of the UAV; acquiring a grid map centered on the UAV's body based on the depth map; determining candidate flight directions for the UAV based on the grid map; determining the optimal flight direction for the UAV among the candidate flight directions; and controlling the UAV to fly along the optimal flight direction to avoid obstacles in the environment in front of the UAV. Through this method, accurate judgment can be made regarding obstacles that suddenly appear in unknown or dynamic environments, thereby achieving real-time path planning.

[0004] For example, Chinese invention patent CN119336042A discloses a path planning method for a quadcopter drone. This method acquires a voxel map using the quadcopter drone's visual sensor, constructs a Venn diagram based on the voxel map, solves for 3D points in the Venn diagram near obstacles in the voxel map using a quadratic programming solver, obtaining multiple path points. The multiple path points are then updated using an ESDF gradient field to obtain a front-end path point set. A multi-order B-spline curve is constructed based on the front-end path point set. Control points are extracted from the B-spline curve according to preset constraints, obtaining multiple control point groups. These control point groups are then aggregated to obtain an aggregated control point group. Based on A... The algorithm performs path search on the aggregated control point group to obtain the initial path trajectory; the initial path trajectory is then optimized using preset rules to obtain the final path trajectory.

[0005] The aforementioned technologies suffer from at least the following technical problems: Current UAV navigation technologies based on ultrasonic positioning and path planning face significant technical bottlenecks in complex environments due to their inherent physical characteristics and application limitations. In complex indoor environments or environments with numerous hard reflective surfaces, continuous reflections between multiple surfaces create multiple non-line-of-sight propagation paths. This causes the sensor to continuously receive a series of delayed secondary or even multiple reflection signals that return via different paths before or after receiving the first direct echo carrying the true distance information. These false echoes are indiscriminately captured and interpreted by the UAV as actual distances. The presence of objects in the image can generate numerous fictitious obstacles in the perception map or cause a serious overestimation of the distance to real obstacles. This perception distortion induced by the multipath effect directly contaminates the accuracy of the UAV's environmental modeling, causing the path planning algorithm to make decisions based on a map full of ghost images and errors. The direct consequence is that the UAV may perform unnecessary emergency obstacle avoidance maneuvers, resulting in flight trajectory oscillations and energy waste. Or, in more dangerous situations, it may misjudge false echoes as safe passages and ignore actual obstacles, ultimately leading to planning failure or even collisions. This seriously weakens the adaptability and reliability of the entire tethered UAV inspection system. Summary of the Invention

[0006] This invention solves the problems existing in the prior art by providing a tethered unmanned aerial vehicle based on ultrasonic positioning and its control method.

[0007] To achieve the above-mentioned objectives, the present invention provides the following technical solution:

[0008] On the one hand, a tethered unmanned aerial vehicle (UAV) control method based on ultrasonic positioning is provided. This method includes: S1. During the UAV's flight towards its initial inspection position along a pre-planned route, real-time position information is acquired based on ultrasonic positioning and compared with the pre-planned route to determine whether to correct the UAV's flight direction; S2. At the UAV's initial inspection position, multi-path ultrasonic detection signals are sent to the test area of ​​the object to be tested. The echo information of each ultrasonic detection signal is acquired and analyzed. If the echo information of a certain path is normal, then the echo information of that path meets the planning conditions; if the echo information of a certain path is abnormal, then the tethered UAV is... S3. If the echo information of the ultrasonic detection signal along the path is still abnormal after reaching the optimization limit, the abnormal route of the ultrasonic detection signal is blocked, the echo information is supplemented according to the captured image, and it is determined whether the echo information of the supplemented path meets the planning conditions; S4. When the echo information of each path at the starting inspection position of the UAV meets the planning conditions, the three-dimensional point cloud data information of the area to be tested is obtained by analyzing the echo information of each path, and the inspection route of the tethered UAV is planned, and the object to be tested is inspected according to the inspection route.

[0009] On the other hand, a tethered drone based on ultrasonic positioning is provided, which is applied as a control method for a tethered drone based on ultrasonic positioning, including: an ultrasonic module, a tether cable connection component, an integrated control module, and a camera device.

[0010] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0011] 1. This invention provides a tethered drone based on ultrasonic positioning and its control method, deeply integrating ultrasonic positioning technology with tethered drone control to achieve high intelligence and significantly improve the drone's operational efficiency in complex environments. This method not only ensures the drone accurately follows pre-planned routes but also dynamically optimizes the detection process through multi-path ultrasonic detection and real-time echo analysis, effectively addressing various environmental interferences. Simultaneously, combined with image completion and intelligent path planning, it guarantees the integrity and reliability of detection data, thereby greatly improving the automation level, accuracy, and safety of inspection tasks. It is suitable for efficient inspection of various infrastructures such as bridges and pipelines.

[0012] 2. This invention, through continuous comparison of real-time ultrasonic positioning data with pre-planned routes, can automatically identify positional deviations and dynamically adjust the flight direction, ensuring that the UAV always flies along the correct path. This real-time correction mechanism effectively overcomes navigation deviations caused by wind, GPS errors, or environmental obstacles, reduces the need for manual intervention, improves flight stability and reliability, and avoids the risk of the UAV deviating from the inspection area, ensuring the smooth initiation of subsequent detection steps and laying a solid foundation for the overall inspection mission.

[0013] 3. This invention uses multi-path ultrasonic echo analysis to quickly diagnose anomaly types and automatically optimize signal parameters to improve data quality. If the anomaly persists after optimization, it intelligently blocks problematic paths and uses image data to complete the echo information, ensuring no omissions in detection coverage. This multi-level adaptive processing strategy enhances the adaptability of UAVs in harsh environments, ensures the accuracy and availability of data acquired from ultrasonic detection signals, and provides reliable input for subsequent path planning.

[0014] 4. This invention intelligently extracts features of the object under test from 3D point cloud data generated based on echo information and plans an efficient inspection route covering key areas. As the UAV flies along this route, it continuously performs ultrasonic obstacle detection and automatic obstacle avoidance, while simultaneously taking photos of key areas using a camera device, achieving full automation and seamless integration of the inspection process. This method not only optimizes resource utilization and improves inspection efficiency but also ensures operational safety and data integrity through real-time monitoring and dynamic adjustments, making it particularly suitable for the detailed inspection of large or complex structures. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a tethered unmanned aerial vehicle (UAV) based on ultrasonic positioning and its control method provided in an embodiment of the present invention;

[0017] Figure 2 This is a schematic diagram of the adaptive adjustment process of ultrasonic positioning and detection signals of a tethered UAV and its control method based on ultrasonic positioning provided by an embodiment of the present invention.

[0018] Figure 3 This is a schematic diagram of the ultrasonic detection signal optimization and multi-source information fusion decision-making process of a tethered UAV and its control method based on ultrasonic positioning provided by an embodiment of the present invention;

[0019] Figure 4 This is a schematic diagram of the autonomous inspection and dynamic obstacle avoidance process of a tethered unmanned aerial vehicle (UAV) based on ultrasonic positioning and its control method provided by an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0021] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms “first,” “second,” and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an,” “a,” or “the,” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “comprising,” “including,” or “including,” and similar terms mean that the element or object preceding the word encompasses the element or object listed following the word and its equivalents, without excluding other elements or objects. The terms “connected,” “linked,” or “connected,” and similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

[0022] It should be noted that the terms "up", "down", "left", "right", "front", and "back" used in this invention are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0023] like Figure 1The flowchart shown is a process for controlling a tethered drone based on ultrasonic positioning, according to an embodiment of this application. The method includes: S1. During the drone's flight towards its initial inspection position along a pre-planned route, real-time position information is acquired using ultrasonic positioning and compared with the pre-planned route to determine whether to correct the drone's flight direction; S2. At the drone's initial inspection position, multi-path ultrasonic detection signals are sent to the area to be tested, and the echo information of each ultrasonic detection signal is acquired and analyzed. If the echo information of a certain path is normal, then the echo information of that path meets the planning conditions; if the echo information of a certain path is not normal, then the echo information of that path meets the planning conditions. If the echo information is abnormal, the signal parameters of the ultrasonic detection signal emitted by the tethered UAV along that path are optimized until the echo information along that path is normal or reaches the optimization limit; S3. If the echo information along that path is still abnormal after reaching the optimization limit, the abnormal route of the ultrasonic detection signal is blocked, the echo information is supplemented according to the captured image, and it is determined whether the echo information along that path after supplementation meets the planning conditions; S4. When the echo information of each path at the initial inspection position of the UAV meets the planning conditions, the three-dimensional point cloud data information of the area to be tested is obtained by analyzing the echo information of each path, and the inspection route of the tethered UAV is planned, and the object to be tested is inspected according to the inspection route.

[0024] In this embodiment, the UAV first flies towards the starting inspection position according to a pre-planned route. During this time, it uses ultrasonic positioning to acquire its own position information in real time and compares it with the predetermined path to dynamically determine and correct the UAV's flight direction to ensure accurate arrival at the target point. After arriving at the starting inspection position, the UAV emits multi-path ultrasonic detection signals to a specific area of ​​the object under test. The signal quality is evaluated by analyzing the echo information of each path. If the echo of a certain path is normal, it is considered to meet the planning conditions. If an anomaly occurs, the ultrasonic transmission parameters of that path are immediately optimized until the echo returns to normal or reaches the preset optimization limit. If the echo is still abnormal after optimization, the UAV will automatically block the abnormal path and use onboard images to complete the missing echo data. The completed information is then evaluated to see if it meets the planning requirements. Finally, when the echoes of all paths pass verification, the UAV integrates this data to generate high-precision three-dimensional point cloud information of the area under test. Based on this, a detailed inspection route for the tethered UAV is planned, and a comprehensive automated inspection of the object under test is performed according to this route.

[0025] like Figure 2As shown, the process begins with real-time acquisition of the UAV's location information using ultrasonic positioning, which is then compared with a pre-planned route. If correction is needed, the UAV's flight direction is adjusted; otherwise, or after adjustment, the UAV continues flying to the initial inspection position. Upon reaching the designated location, the UAV sends multi-path ultrasonic detection signals to the area to be tested and acquires and analyzes the echo information from each path. The echo status is used for judgment: if the echo is normal, the planned conditions are confirmed; if the echo is abnormal, the ultrasonic detection signal transmission parameters are adaptively optimized, thereby achieving precise positioning and dynamic adjustment of the detection process.

[0026] It needs to be explained that the specific process of correcting the drone's flight direction is as follows: Based on real-time ultrasonic positioning, the drone acquires its position information and calculates the position deviation value from the pre-planned route. The position information refers to the real-time acquired three-dimensional position coordinates of the drone. Based on the calculated position deviation value, the drone's flight direction correction is obtained. Specifically, based on the direction and magnitude of the position deviation value in three-dimensional space, combined with the drone's current attitude data, a navigation algorithm calculates the yaw angle correction and pitch angle correction required to eliminate the deviation. The calculated yaw angle correction and pitch angle correction are converted into low-level control commands for the tethered drone's flight control. By adjusting the speed difference and power output of each motor, the drone is driven to fly towards the pre-planned route to gradually eliminate the position deviation. During the flight direction correction process, the drone continuously acquires its latest position information in real-time based on ultrasonic positioning and repeatedly executes steps S11 to S13 to dynamically iterate and adjust the flight direction until the real-time position information re-enters the position information range allowed by the pre-planned route. At that point, the correction is considered complete, active intervention ceases, and normal flight along the pre-planned route resumes.

[0027] Preferably, the echo information of each ultrasonic detection signal is acquired and analyzed. The specific analysis process is as follows: the echo information of each ultrasonic detection signal is preprocessed to obtain the characteristic curve of each echo information, and the quality evaluation parameters of the characteristic curve of each echo information are obtained.

[0028] The quality assessment parameters of the characteristic curves of the above-mentioned echo information include the cumulative peak amplitude of the characteristic curves of each echo information, the rising time of the leading edge of the characteristic curves of each echo information, and the half-width of the main peak of the characteristic curves of each echo information.

[0029] The cumulative peak amplitude of the characteristic curves of each echo source refers to the amplitude value corresponding to the highest point of the main peak of the characteristic curve of each echo source. It is obtained by scanning the characteristic curves of each echo source and identifying the global maximum value point. The rising time of the leading edge of the characteristic curve of each echo source refers to the time it takes for the main peak of the echo source to rise from the starting position to the peak position. It is obtained by calculating the time interval from the starting position to the peak position. The half-width of the main peak of the characteristic curve of each echo source refers to the full width at half the height of the main peak of the characteristic curve of each echo source. It is obtained by finding half the height of the main peak on the characteristic curve of each echo source, drawing a horizontal line intersecting the two sides of the main peak, and calculating the time interval between the two intersection points.

[0030] By introducing influence weighting coefficients, the influence of the cumulative peak amplitude to the defined cumulative peak amplitude, the rising duration of the leading edge to the defined rising duration, and the half-width of the main peak to the defined half-width of the characteristic curve of each echo information is quantified. These influence levels are then coupled to obtain the anomaly index of the characteristic curve of each echo information. The specific expression is as follows:

[0031] ;

[0032] In the formula, EI is the anomaly index of the characteristic curve of each echo information, fz is the cumulative peak amplitude of the characteristic curve of each echo information, lz is the rising time of the leading edge of the characteristic curve of each echo information, bk is the half-width of the main peak of the characteristic curve of each echo information, fz_min is the pre-defined cumulative peak amplitude in the planning database, which is the critical minimum allowable value of the cumulative peak amplitude, lz_min is the pre-defined rising time of the leading edge in the planning database, which is the critical minimum allowable value of the rising time of the leading edge, bk_min is the pre-defined half-width of the main peak in the planning database, which is the critical minimum allowable value of the half-width of the main peak, kx1 is the influence contribution coefficient corresponding to the pre-defined cumulative peak amplitude in the planning database, kx2 is the influence contribution coefficient corresponding to the pre-defined rising time of the leading edge in the planning database, and kx3 is the influence contribution coefficient corresponding to the pre-defined half-width of the main peak in the planning database.

[0033] It should be explained that the influence contribution coefficient corresponding to the cumulative peak amplitude mentioned above represents the degree of influence of the ratio between the cumulative peak amplitude and the defined cumulative peak amplitude of the characteristic curve of each echo information on the anomaly index of the characteristic curve of each echo information, and the value range is (0, 1]. The influence contribution coefficient corresponding to the rising edge duration mentioned above represents the degree of influence of the ratio between the rising edge duration and the defined rising edge duration of the characteristic curve of each echo information on the anomaly index of the characteristic curve of each echo information, and the value range is (0, 1). The influence contribution coefficient corresponding to the peak half-width mentioned above represents the degree of influence of the ratio between the main peak half-width and the defined main peak half-width of the characteristic curve of each echo information on the anomaly index of the characteristic curve of each echo information, and the value range is (0, 1).

[0034] In this example, when the cumulative peak amplitude fz increases, its ratio to the threshold value fz_min also increases, directly leading to an increase in the anomaly index EI, with the increase controlled by kx1. If the leading edge climb duration lz or the main peak half-width bk also increases, they will further exacerbate the increase in EI through kx2 and kx3, thus strengthening the overall anomaly. Similarly, when the leading edge climb duration lz increases, its ratio to the threshold value lz_min increases, directly pushing up EI, and is also affected by kx2. Simultaneously, the increase in the cumulative peak amplitude fz and the main peak half-width bk will also additionally increase EI through kx1 and kx3, causing the anomaly index to rise significantly when multiple parameters deviate. For the main peak half-width bk, as it increases, the ratio to the threshold value bk_min increases, directly increasing EI, which is regulated by kx3. Meanwhile, increases in the cumulative peak amplitude fz and the leading edge rise time lz also synergistically increase EI through kx1 and kx2, forming a multi-parameter driven anomaly effect. Ultimately, the anomaly index EI is a weighted sum of the deviations of these three parameters, comprehensively reflecting the abnormal state of the echo information characteristic curve through the coupling of their influence contribution coefficients. When any parameter exceeds its threshold value, EI increases, but the magnitude of the increase depends on the influence contribution coefficient of that parameter and the current values ​​of other parameters, thus enabling the anomaly index to sensitively capture abnormal changes under different parameter combinations.

[0035] The abnormality index of the characteristic curve of each echo information is compared with the abnormality threshold. If the abnormality index of the characteristic curve of a certain path echo information is less than the abnormality threshold, the echo information of that path is normal. If the abnormality index of the characteristic curve of a certain path echo information is greater than or equal to the abnormality threshold, the echo information of that path is abnormal. The signal parameters of the ultrasonic detection signal emitted by the tethered UAV for that path are then optimized.

[0036] In this embodiment, the UAV performs preliminary screening and overall assessment of the detection status by comparing the anomaly index of the characteristic curves of each ultrasonic echo with a preset anomaly threshold. This assessment mechanism serves as the core decision-making hub, aiming to efficiently distinguish between normal and abnormal paths and establish clear target guidance for subsequent optimization. Based on this screening result, the UAV initiates a deep diagnostic process for paths identified as abnormal, that is, based on the specific patterns presented by the characteristic curves, such as multiple peaks, main peak distortion, or excessively low peaks, it accurately determines whether the anomaly type belongs to multipath interference or excessive noise. Based on this, the UAV performs precise parameter corrections: for multipath anomalies, the signal emission angle is finely adjusted in a specific direction and step size within the preset beam angle adjustment limit; for noise anomalies, the intensity of noise suppression filtering is dynamically increased within a predetermined margin. After correction, the UAV will re-verify the echo status. If it is still abnormal, the parameters are reset and a more advanced UAV-level global optimization is triggered to ensure that computing resources are accurately allocated. This constitutes a complete autonomous optimization UAV that goes from rapid global survey to local in-depth diagnosis and then to parameter closed-loop optimization, thereby ensuring the real-time performance, robustness and overall efficiency of detection in complex environments.

[0037] like Figure 3 As shown, the process first determines whether the optimized echo information has returned to normal. If it has, the planning conditions are met; if it is still abnormal, the process further checks whether the number of optimization attempts has reached the preset limit. If the limit has not been reached, the UAV will continue to iteratively optimize the signal parameters; if the limit has been reached, the abnormal route is automatically blocked, and the camera is simultaneously activated to capture images of that route to complete the missing echo information. The UAV finally verifies the completed overall information. If the planning conditions are met, the process is complete, thus achieving multi-source information fusion and adaptive decision-making in abnormal detection situations.

[0038] Preferably, the signal parameters of the ultrasonic detection signal emitted by the tethered UAV along the path are optimized. The specific optimization process is as follows: if the abnormality index and abnormality threshold of the characteristic curve of the echo information of a certain path are compared and the path echo information is found to be abnormal, then the abnormal conditions of the path with abnormal echo information can be determined based on the characteristic curve of the path echo information.

[0039] The abnormal conditions existing in the abnormal path of the echo information include multipath abnormal conditions and noise abnormal conditions.

[0040] If the characteristic curve of the path echo information shows multiple peaks or distortion of the main peak waveform, it is determined that the abnormal conditions of the abnormal path of the echo information can be analyzed and the abnormal conditions are multipath abnormal conditions; if the main peak outline of the characteristic curve of the path echo information is blurred, it is determined that the abnormal conditions of the abnormal path of the echo information can be analyzed and the abnormal conditions are noise abnormal conditions; otherwise, it is determined that the abnormal conditions of the abnormal path of the echo information cannot be analyzed, and the signal parameters of the ultrasonic detection signal emitted by the tethered UAV along the path are further optimized.

[0041] It needs to be explained that the waveform distortion of the main peak under multipath anomaly conditions, including the asymmetry of the main peak shape and the twisted shape such as shoulder protrusion, is essentially the waveform superposition caused by the difference in signal arrival time; the main peak outline is blurred under noise anomaly conditions, that is, the rising and falling edges of the main peak are no longer clear and smooth, but become rough and have spikes, the overall outline is less recognizable, the peak area is widened and accompanied by irregular amplitude fluctuations, which is essentially the contamination of the useful signal by random noise.

[0042] If the characteristic curve of the path echo information has multipath anomalies, the beam angle of the ultrasonic detection signal emitted by the path is gradually adjusted towards the center of the beam angle within the beam angle adjustment limit by a preset convergence angle. After each adjustment, the ultrasonic detection signal is re-emitted until the path echo information is normal or the beam angle adjustment limit is reached. If the beam angle adjustment limit is reached, the emission angle of the ultrasonic detection signal of the path is reset to further optimize the emission parameters of the ultrasonic detection signal of the tethered UAV for the path.

[0043] In this embodiment, when the UAV determines that the echo information of a certain path is abnormal based on the anomaly index, and its characteristic curve shows multiple peaks or main peak waveform distortion, it is determined that there is a multipath anomaly condition in the path. At this time, the UAV starts the beam angle orientation optimization mechanism. Within the predefined beam angle adjustment limit, it iteratively focuses the emitted signal beam angle of the ultrasonic detection signal of the path according to a fixed step size of the preset adjustment angle, improves the signal directivity of the ultrasonic detection signal of the path, and suppresses multipath reflection. Specifically, the UAV uses the preset adjustment angle as the minimum adjustment unit, and gradually focuses the emitted signal beam angle within the maximum beam angle change range allowed by the beam angle adjustment limit. After each angle is focused, the ultrasonic detection signal is immediately re-emitted and new echo information is collected for verification. Through this closed-loop control method of adjusting first and then verifying, the echo information is restored to normal or the cumulative adjustment amount reaches the beam angle adjustment limit. If the anomaly is not eliminated when the limit is reached, the UAV will automatically reset the launch angle to the initial state and trigger a deeper signal parameter optimization process. The core of this multipath anomaly optimization mechanism is to avoid multipath interference by precisely controlling the beam pointing. The beam angle adjustment limit ensures the safety boundary of the beam convergence process, while the preset convergence angle ensures the precision and controllability of the optimization process.

[0044] If the characteristic curve of the path echo information has abnormal noise conditions, the cutoff frequency of the ultrasonic detection signal emitted by the path is gradually reduced within the cutoff frequency adjustment limit by a preset adjustment frequency step size. After each reduction, the ultrasonic detection signal is re-emitted until the path echo information is normal or the cutoff frequency adjustment limit is reached. If the noise suppression filter adjustment limit is reached, the adjustment of the cutoff frequency of the ultrasonic detection signal emitted by the path is stopped, and the emission signal parameters of the ultrasonic detection signal emitted by the tethered UAV on the path are further optimized.

[0045] In this embodiment, when the UAV determines that the echo information of a certain path is abnormal and the main peak outline of its characteristic curve is blurred, it is identified that there is an abnormal noise condition in the path. In response, the UAV activates the noise suppression and enhancement mechanism. Within the set cutoff frequency adjustment limit, the cutoff frequency of the ultrasonic detection signal emitted by the path is gradually reduced according to the preset adjustment frequency step size. This effectively filters out the high-frequency random noise superimposed on the echo signal, thereby making the main peak outline, which was originally blurred and rough due to noise interference, clear and smooth, and significantly improving the signal-to-noise ratio and recognizability of the signal. In practice, the UAV uses a preset adjustment frequency step size as the minimum reduction unit. Within the maximum adjustment range allowed by the cutoff frequency adjustment limit, it gradually reduces the cutoff frequency. Utilizing the noise suppression capability in signal processing, the UAV retransmits the detection signal and collects new echoes for verification after each cutoff frequency reduction. This progressive optimization method of first reducing and then verifying continues until the echo information returns to normal or the cutoff frequency reaches the adjustment limit. If the anomaly persists even after reaching the limit, the UAV immediately stops adjusting the cutoff frequency and enters a higher-level signal optimization stage. The key to this noise anomaly optimization mechanism is overcoming noise interference by progressively improving the signal-to-noise ratio. The cutoff frequency adjustment limit prevents signal distortion caused by excessive reduction of the cutoff frequency, while the adjustment frequency step size ensures the stability and gradualness of the optimization process.

[0046] Preferably, the signal parameters of the ultrasonic detection signal emitted by the tethered UAV along the path are further optimized. The specific optimization process is as follows: obtain the anomaly index of the characteristic curve of the echo information of the path; optimize the pulse width and amplitude of the emitted ultrasonic detection signal based on the anomaly index of the characteristic curve of the echo information of the path; re-emit the ultrasonic detection signal of the path and analyze the echo information of the ultrasonic detection signal of the path; if the echo information is normal after the path is re-emitted, the optimization ends and the echo information of the path meets the planning conditions; if the echo information is abnormal after the path is re-emitted, continue to optimize within the optimization limit; if the echo information is normal within the optimization limit, the optimization ends and the echo information of the path meets the planning conditions; if the path echo information is still abnormal after reaching the optimization limit, the path is marked as an abnormal route.

[0047] In this embodiment, when initial angle adjustments or filtering enhancement measures fail to restore the abnormal echo to normal, the UAV will perform deeper signal parameter optimization: First, the specific anomaly index of the path echo information characteristic curve is obtained. Based on the degree of anomaly quantified by this index, the two core parameters of the ultrasonic detection signal for the path, namely the transmission pulse width and transmission amplitude, are simultaneously optimized, where amplitude is the amplitude of the transmitted signal strength. Then, the ultrasonic detection signal for the path is retransmitted with the optimized new parameters, and its echo information is collected and analyzed. If the echo information after retransmission is evaluated as normal, the optimization is considered successful, and the path meets the planning conditions. If the echo information still shows anomalies, the parameter combination of transmission pulse width and transmission amplitude is iteratively adjusted in the above manner within the preset optimization upper limit, forming a closed-loop feedback mechanism, until normal echo information is obtained or the optimization upper limit is reached. Thus, through this dynamic adaptive signal parameter adjustment strategy, the reliability and adaptability of ultrasonic detection in complex detection environments are significantly improved.

[0048] Preferably, the pulse width and amplitude of the ultrasonic detection signal emitted along the path are optimized. The specific optimization process is as follows: Based on the anomaly index of the characteristic curve of the echo information of the path, a pulse width increase coefficient is matched from the planning database. The pulse width increase coefficient is multiplied by the pulse width of the ultrasonic detection signal emitted along the path to obtain the signal increase pulse width. The signal increase pulse width is added to the original pulse width of the ultrasonic detection signal emitted along the path to optimize the pulse width of the ultrasonic detection signal emitted along the path. Based on the anomaly index of the characteristic curve of the echo information of the path, an amplitude increase coefficient is matched from the planning database. The amplitude increase coefficient is multiplied by the amplitude of the ultrasonic detection signal emitted along the path to obtain the signal increase amplitude. The signal increase amplitude is added to the original amplitude of the ultrasonic detection signal emitted along the path to optimize the amplitude of the ultrasonic detection signal emitted along the path.

[0049] In this embodiment, the process of co-optimizing the pulse width and amplitude of the ultrasonic detection signal is as follows: The UAV first acquires the anomaly index of the characteristic curve of the current path echo information, and then, based on the index value, concurrently matches two key adjustment coefficients from the pre-generated planning database: the pulse width increase coefficient and the amplitude increase coefficient. The logic of this matching process is that the planning database, as an empirical knowledge base, pre-stores the optimal parameter adjustment strategies corresponding to different anomaly index ranges. Through the query mechanism, the UAV can accurately match the most suitable adjustment coefficient according to the value of the current anomaly index. The core principle is that the higher the anomaly index, the larger the matched increase coefficient is usually, to ensure that the adjustment intensity is positively correlated with the severity of the problem. After successfully matching the coefficients, the optimization process unfolds simultaneously: in the pulse width optimization branch, the matched pulse width increment coefficient is multiplied by the current transmitted signal pulse width to obtain the signal increment pulse width, and then this increment is added to the original pulse width; in the amplitude optimization branch, the matched amplitude increment coefficient is multiplied by the current transmitted signal amplitude to obtain the signal increment amplitude, and then this increment is added to the original amplitude; through this synchronous optimization mechanism based on database queries, the UAV achieves coordinated adjustment of the signal's time and frequency domain characteristics, making the parameter optimization process more targeted and efficient, and more effectively able to cope with the challenges of complex detection environments.

[0050] Preferably, the echo information is supplemented based on the captured images. The specific supplementation process is as follows: the abnormal route is blocked, and the camera device on the drone is activated; the camera device on the drone cloud is controlled to aim at the direction of the abnormal route and capture images of the abnormal route, and multiple high-definition images of the abnormal route are acquired. The multiple high-definition images are preprocessed, and visual feature points are extracted from the preprocessed multiple high-definition images. Visual pose fusion information is obtained by fusing it with the current precise pose of the drone. The visual pose fusion information is converted into simulated ultrasonic echo information through a preset echo information conversion rule. The missing echo information is supplemented based on the simulated ultrasonic echo information.

[0051] It should be explained that the preprocessing of multiple high-definition images involves several steps. First, digital filtering algorithms are used to reduce noise in the original images, suppressing electrical noise and optical interference that are unavoidable in complex environments. Next, contrast enhancement and histogram equalization techniques are employed to optimize the overall and local visual effects of the images, ensuring sufficient texture detail is preserved even under uneven lighting conditions. Finally, lens distortion correction is performed, eliminating image distortion caused by the inherent characteristics of the optical lens based on the camera's factory-calibrated intrinsic parameter matrix. This series of preprocessing operations effectively eliminates various distortions and interference factors introduced during image acquisition, laying a solid foundation for the stable extraction and accurate matching of subsequent visual feature points.

[0052] The aforementioned visual feature points refer to two-dimensional image key points in digital images that can be reliably detected by algorithms and have uniqueness. They contain location information and feature description vectors, which are used to characterize the visual structure of specific local regions in the image and serve as the basis for subsequent three-dimensional spatial information estimation and data fusion.

[0053] It's important to explain that extracting visual feature points involves several steps. First, a scale-space representation is constructed from the preprocessed input image. Extrema are detected at different resolutions using the difference of Gaussians algorithm. Next, these candidate points are precisely located. Low-contrast unstable points are eliminated by fitting a three-dimensional quadratic function, and edge response points are excluded based on Hessian matrix principal curvature analysis. This ensures that all extracted feature points are located at corners or textured regions with stable geometry. After keypoint localization, the UAV assigns orientation parameters to each feature point. The principal orientation is determined by statistically analyzing the gradient direction histograms of its neighboring pixels, achieving rotation invariance. Finally, a highly discriminative feature descriptor vector is generated. This process involves dividing the feature point's neighborhood into sub-regions, calculating the statistical histogram of gradient directions within each sub-region, and concatenating the histograms of all sub-regions to form a 128-dimensional feature descriptor. This scale-invariant feature transformation-based extraction method ensures that each obtained visual feature point simultaneously contains accurate two-dimensional pixel coordinates and a highly discriminative feature descriptor vector, providing high-quality input data for subsequent multi-view matching and 3D reconstruction.

[0054] It's important to explain that fusing the extracted visual feature points with the UAV's current precise pose to obtain visual pose fusion information is crucial for generating spatial information suitable for echo simulation. This process relies on high-precision, real-time six-degree-of-freedom pose data (i.e., 3D position and 3D attitude) acquired by the UAV through multiple sensors, including GPS, IMU, and possibly visual odometry. During fusion, the UAV extracts 2D feature points from each frame and, based on the UAV's precise pose and camera calibration parameters at the time the frame was captured, uses multi-view geometry principles to calculate the 3D spatial coordinates of these feature points in the world coordinate system. The resulting "visual pose fusion information" is essentially a data set containing a 3D point cloud of the environment reconstructed from visual feature points, with each 3D point associated with its original visual feature descriptor. This information tightly integrates visual appearance with absolute spatial position, providing precise geometric input for subsequent simulation of ultrasonic echoes according to physical laws.

[0055] In this embodiment, when the UAV determines that the ultrasonic echo information of a certain path remains abnormal despite multiple optimizations, the abnormal path is marked as an abnormal route, and the camera device on the UAV is activated. By controlling the gimbal, the camera device is precisely aimed at the direction of the abnormal route, and multiple high-definition image sequences are continuously acquired. After preprocessing these raw images, including denoising, enhancement, and correction, stable visual feature points are extracted, and these visual features are fused with the high-precision pose information of the UAV to generate spatially consistent visual pose fusion information. Subsequently, according to the preset echo information conversion rules, the visual pose fusion information is converted into corresponding simulated ultrasonic echo information. Finally, this simulated echo data is used to complete the reconstruction of the missing echo information of the original abnormal route, thereby maintaining the integrity and continuity of environmental perception even in scenarios where ultrasonic detection is limited.

[0056] Preferably, it is determined whether the echo information of the completed path meets the planning conditions. The specific determination process is as follows:

[0057] After completion, the anomaly index of the characteristic curve of the path echo information is obtained again. If the anomaly index of the characteristic curve of the path echo information is greater than or equal to the anomaly threshold, it is determined that the completed path echo information does not meet the planning conditions. If the anomaly index of the characteristic curve of the path echo information is less than the anomaly threshold, the three-dimensional point cloud data of each path echo information after completion is analyzed. If the three-dimensional point cloud data shows a continuous distribution in three-dimensional space, that is, the distance between any two points in the three-dimensional point cloud is less than the maximum interval distance preset in the planning database, it is determined that the completed path echo information meets the planning conditions. Otherwise, it is determined that the completed path echo information does not meet the planning conditions.

[0058] In this embodiment, the determination of whether the completed path echo information meets the planning conditions is performed according to the following process: After the data completion operation is completed, the UAV will acquire the characteristic curve of the path echo information again and calculate its anomaly index; if the anomaly index is greater than or equal to the preset anomaly threshold, the UAV directly determines that the completion result does not meet the planning conditions; if the anomaly index is confirmed to be lower than the anomaly threshold, the judgment process enters the next stage, and the UAV will analyze the three-dimensional point cloud data corresponding to each path echo information generated after completion, the core of which is to check its distribution continuity in three-dimensional space; when the three-dimensional point cloud data shows continuous spatial distribution characteristics, that is, the distance between any two points in the three-dimensional point cloud is less than the preset maximum interval distance in the planning database, the UAV finally determines that the path echo information meets the planning conditions, otherwise it is determined to be non-compliant.

[0059] like Figure 4As shown, a complete inspection route is first planned, and the drone is controlled to perform the flight inspection task according to the route. During the flight, the drone continuously uses ultrasound for positioning and obstacle detection. Once a temporary obstacle is detected, it immediately enters a hovering waiting state until the obstacle disappears before continuing to perform the inspection task. This forms a closed-loop monitoring and dynamic safety obstacle avoidance mechanism for the entire process.

[0060] Preferably, the inspection route of the tethered UAV is planned, and the specific planning process is as follows: S31. Based on the three-dimensional point cloud data obtained from the analysis of various echo information, the starting area information of the object to be tested is obtained; the three-dimensional point cloud data includes the spatial coordinate information of the surface of the object to be tested, and a continuous three-dimensional mesh model is generated through point cloud registration and fusion technology; S32. Feature extraction and segmentation are performed on the starting area information of the object to be tested to identify the surface contour, edge features and key inspection areas of the object to be tested; the key inspection areas include the concave, convex or abnormal structural areas of the surface of the object to be tested; S33. Based on the identified key inspection areas, a preliminary inspection path covering all key inspection areas is generated; the preliminary inspection path adopts a raster scanning method, and the path point sequence is calculated based on point cloud density and area priority to ensure complete and no omissions in path coverage; S34. Combined with the flight constraints of the tethered UAV, the preliminary inspection path is smoothed, optimized and dynamically adjusted to obtain the final inspection route; the flight constraints include the length limit of the tether cable, the maneuverability of the UAV and the safety distance, and the connection between path points is optimized through path planning algorithm.

[0061] In this embodiment, firstly, based on the analyzed echo information, a three-dimensional point cloud data containing the spatial coordinates of the surface of the object under test is generated. A continuous three-dimensional mesh model is constructed using point cloud registration and fusion techniques, thereby obtaining complete information about the initial region of the object under test. Subsequently, feature extraction and segmentation are performed on this region information to accurately identify the contour features, edge direction, and key inspection areas containing abnormal structures such as depressions and protrusions on the surface of the object under test. A specific application scenario is the inspection of wind turbine blades. Here, feature extraction and segmentation are performed on a specific initial region (such as the leading edge, ...) within the overall three-dimensional model of the wind turbine blade. The inspection is carried out on a section of the trailing edge or pressure surface. Based on this, a preliminary inspection path covering all key inspection areas is generated using a rasterized scanning method. This path calculates the optimal path point sequence by analyzing the point cloud density distribution and regional priority to ensure the completeness and completeness of the inspection coverage. Finally, combined with the actual flight constraints of the tethered UAV, including the length limit of the tether cable, the UAV's own maneuverability parameters, and the safety distance to be maintained, the preliminary inspection path is smoothly optimized and dynamically adjusted by the path planning algorithm to obtain the final inspection route that meets both inspection requirements and flight safety.

[0062] It needs to be explained that a continuous 3D mesh model is generated through point cloud registration and fusion techniques. An iterative nearest-point algorithm is used to unify point cloud data collected from different perspectives into the same coordinate system. By calculating the rigid transformation matrix between point clouds, precise alignment of multiple point cloud segments is achieved. After registration, the UAV performs point cloud fusion processing: using the Poisson surface reconstruction algorithm, the registered dense point cloud is converted into a continuous triangular mesh model. This process first estimates the implicit surface function based on the point cloud data, then generates a triangular mesh with topological structure through an isosurface extraction algorithm, ultimately forming a complete 3D mesh model describing the surface geometry of the object under test. In the model construction phase, a multi-level verification strategy is adopted, including point cloud density uniformity checks, registration error statistical analysis, and mesh closure detection. In the algorithm training phase, a parameter optimization model is established based on a large amount of sample data. The iterative parameters of the registration algorithm and the resolution parameters of the reconstruction algorithm are adjusted through supervised learning, enabling the UAV to adaptively select the optimal parameter combination according to different detection scenarios. Meanwhile, by combining cross-validation and expert evaluation, the entire processing flow is continuously optimized to ensure that the generated 3D mesh model maintains both geometric accuracy and complete topological structure, providing a reliable data foundation for subsequent path planning.

[0063] Preferably, the object to be inspected is inspected according to the inspection route. The specific inspection process is as follows: S41. The UAV autonomously flies along the inspection route. During the flight along the inspection route, the UAV continuously acquires high-precision position information in real time based on ultrasonic positioning and continuously sends ultrasonic detection signals. The echo information of the ultrasonic detection signals is analyzed to detect obstacles ahead in real time. When the analysis of the echo characteristics indicates that there is a temporary obstacle on the flight path of the UAV, it immediately hovers and waits; S42. While hovering and waiting, the UAV uses a waiting timer to keep track of time and continuously acquires the echo signal of the temporary obstacle. When the acquired obstacle echo signal disappears or weakens to below a safety threshold, the UAV automatically resumes the previously interrupted inspection route from the hovering point and continues to perform the inspection task; S43. While flying along the inspection route, the camera device on the UAV is controlled to take pictures of each key inspection area in sequence according to the inspection route.

[0064] In this embodiment, when the tethered UAV performs an autonomous inspection task according to the planned inspection route, it implements a complete safety inspection process. First, the UAV flies autonomously along the predetermined inspection route, continuously acquiring its own high-precision position information through ultrasonic positioning, while simultaneously emitting ultrasonic detection signals forward. By analyzing the characteristic changes of the echo signals in real time, the UAV can effectively detect temporary obstacles on its flight path. When abnormal echo characteristics are detected ahead (such as a sudden increase in echo intensity, a sudden decrease in echo delay, or other signal characteristics indicating the presence of obstacles), the UAV immediately enters a hovering waiting state. During the hovering waiting period, the UAV starts a waiting timer and continuously monitors the echo signal changes in the obstacle area. When the UAV confirms through multiple detections that the obstacle echo signal has completely disappeared, or its intensity has weakened to below a preset safety threshold, the UAV automatically resumes the previously interrupted inspection route from the current hovering position and continues to perform the unfinished inspection task. It should be noted that the "disappearance or weakening of the echo signal to below the safety threshold" here is a comprehensive judgment made after trend analysis of the echo characteristics of multiple consecutive detection cycles, rather than the result of a single detection. This effectively avoids misjudgments caused by momentary interference. Simultaneously, throughout the entire flight inspection process, the camera device on the UAV automatically focuses and acquires images of each key inspection area sequentially according to the progress of the inspection route. This parallel operation mode of ultrasonic detection and visual acquisition ensures both flight safety and the integrity and reliability of the inspection data, forming a complete automated inspection solution.

[0065] The following points need to be explained:

[0066] (1) The accompanying drawings of the embodiments of the present invention only involve the structures involved in the embodiments of the present invention. Other structures can refer to the general design.

[0067] (2) For clarity, the thickness of layers or regions is enlarged or reduced in the drawings used to describe embodiments of the invention, i.e., these drawings are not drawn to scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being “above” or “below” another element, the element may be “directly” located “above” or “below” the other element or there may be intermediate elements.

[0068] (3) Where there is no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other to obtain new embodiments.

[0069] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A control method for a tethered unmanned aerial vehicle based on ultrasonic positioning, characterized in that, include: S1. During the flight of the UAV towards the UAV's starting inspection position along a pre-planned route, the UAV acquires its position information in real time based on ultrasonic positioning and compares it with the pre-planned route to determine whether to correct the UAV's flight direction. S2. At the initial inspection position of the UAV, send multi-path ultrasonic detection signals to the area to be tested of the object, acquire and analyze the echo information of each ultrasonic detection signal. If the echo information of a certain path is normal, then the echo information of that path meets the planning conditions. If the echo information of a certain path is abnormal, then optimize the signal parameters of the ultrasonic detection signal emitted by the tethered UAV for that path until the echo information of that path is normal or reaches the optimization limit. The specific analysis process of acquiring and analyzing the echo information of each ultrasonic detection signal is as follows: Preprocessing the echo information of each ultrasonic detection signal yields the characteristic curves of each echo information, and the quality assessment parameters of the characteristic curves of each echo information are obtained. The quality assessment parameters of the characteristic curves of each echo information include the cumulative peak amplitude of the characteristic curves of each echo information, the rising time of the leading edge of the characteristic curves of each echo information, and the half width of the main peak of the characteristic curves of each echo information. By introducing influence weighting coefficients, the influence of the cumulative peak amplitude to the defined cumulative peak amplitude, the rising time of the leading edge to the defined rising time, and the ratio of the main peak half width to the defined main peak half width of the characteristic curves of each echo information is quantified. The influence of each influence degree is coupled to obtain the anomaly index of the characteristic curves of each echo information. S3. If the path echo information is still abnormal when the optimization limit is reached, the abnormal route of the ultrasonic detection signal is blocked, the echo information is supplemented according to the captured image, and it is determined whether the path echo information after supplementation meets the planning conditions. S4. When all echo information from the initial inspection position of the UAV meets the planning conditions, the three-dimensional point cloud data information of the area to be tested is obtained by analyzing the echo information, and the inspection route of the tethered UAV is planned. The object to be tested is then inspected according to the inspection route.

2. The control method for a tethered unmanned aerial vehicle based on ultrasonic positioning according to claim 1, characterized in that, The anomaly index of the characteristic curve of each echo information is compared with the anomaly threshold. If the anomaly index of the characteristic curve of a certain path echo information is less than the anomaly threshold, then the path echo information is normal. If the anomaly index of the characteristic curve of a certain path echo information is greater than or equal to the anomaly threshold, then the path echo information is abnormal, and the signal parameters of the ultrasonic detection signal emitted by the tethered UAV for that path are optimized.

3. The control method for a tethered unmanned aerial vehicle based on ultrasonic positioning according to claim 2, characterized in that, The optimization of the signal parameters emitted by the ultrasonic detection signal along the path of the tethered UAV is specifically as follows: If the analysis based on the comparison between the abnormal index and the abnormal threshold of the characteristic curve of a certain path echo information indicates that the path echo information is abnormal, then the abnormal conditions of the path echo information can be determined based on the characteristic curve of the path echo information. The abnormal conditions existing in the abnormal path of the echo information include multipath abnormal conditions and noise abnormal conditions; If the characteristic curve of the echo information of the path has multiple peaks or the main peak waveform distortion appears, it is determined that the abnormal conditions of the abnormal path of the echo information can be analyzed and the abnormal conditions are multipath abnormal conditions. If the main peak outline of the characteristic curve of the path echo information is blurred, it is determined that the abnormal conditions of the abnormal path of the echo information can be analyzed and the abnormal conditions are noise abnormal conditions; otherwise, it is determined that the abnormal conditions of the abnormal path of the echo information cannot be analyzed, and the signal parameters of the ultrasonic detection signal emitted by the tethered UAV on this path are further optimized. If the characteristic curve of the path echo information has multipath anomalies, the beam angle of the ultrasonic detection signal emitted by the path is gradually adjusted towards the center of the beam angle within the beam angle adjustment limit by a preset convergence angle. After each adjustment, the ultrasonic detection signal is re-emitted until the path echo information is normal or the beam angle adjustment limit is reached. If the beam angle adjustment limit is reached, the emission angle of the ultrasonic detection signal of the path is reset to further optimize the emission parameters of the ultrasonic detection signal of the tethered UAV. If the characteristic curve of the path echo information has abnormal noise conditions, the cutoff frequency of the ultrasonic detection signal emitted by the path is gradually reduced within the cutoff frequency adjustment limit by a preset adjustment frequency step size. After each reduction, the ultrasonic detection signal is re-emitted until the path echo information is normal or the cutoff frequency adjustment limit is reached. If the noise suppression filter adjustment limit is reached, the adjustment of the cutoff frequency of the ultrasonic detection signal emitted by the path is stopped, and the emission signal parameters of the ultrasonic detection signal emitted by the tethered UAV on the path are further optimized.

4. The control method for a tethered unmanned aerial vehicle based on ultrasonic positioning according to claim 3, characterized in that, The further optimization of the ultrasonic detection signal parameters emitted by the tethered UAV along this path involves the following optimization process: Obtain the anomaly index of the characteristic curve of the echo information of the path, optimize the pulse width and amplitude of the emitted ultrasonic detection signal of the path based on the anomaly index of the characteristic curve of the echo information of the path, re-emit the ultrasonic detection signal of the path and analyze the echo information of the ultrasonic detection signal of the path. If the echo information is normal after the path is retransmitted, the optimization ends and the echo information of the path meets the planning conditions. If the echo information is abnormal after the path is retransmitted, continue to optimize within the optimization limit. If the echo information is normal within the optimization limit, the optimization ends and the echo information of the path meets the planning conditions. If the path echo information is still abnormal after reaching the optimization limit, then the path is marked as an abnormal route.

5. The control method for a tethered unmanned aerial vehicle based on ultrasonic positioning according to claim 4, characterized in that, The optimization process for the pulse width and amplitude of the ultrasonic detection signal transmitted along the path is as follows: Based on the anomaly index of the characteristic curve of the echo information of the path, the pulse width increase coefficient is matched from the planning database. The pulse width increase coefficient is multiplied by the pulse width of the emitted signal of the ultrasonic detection signal of the path to obtain the signal increase pulse width. The signal increase pulse width is added to the original pulse width of the emitted signal of the ultrasonic detection signal of the path to optimize the pulse width of the emitted signal of the ultrasonic detection signal of the path. Based on the anomaly index of the characteristic curve of the echo information of the path, the amplitude increase coefficient is matched from the planning database. The amplitude increase coefficient is multiplied by the amplitude of the emitted ultrasonic detection signal of the path to obtain the signal increase amplitude. The signal increase amplitude is added to the original amplitude of the emitted ultrasonic detection signal of the path to optimize the amplitude of the emitted ultrasonic detection signal of the path.

6. The control method for a tethered unmanned aerial vehicle based on ultrasonic positioning according to claim 1, characterized in that, The process of completing the echo information based on the captured image is as follows: Block out abnormal routes and activate the camera device on the drone; The camera on the drone is aimed at the abnormal route and images of the abnormal route are captured. Multiple high-definition images of the abnormal route are acquired. The high-definition images are preprocessed and visual feature points are extracted from the preprocessed high-definition images. Visual pose fusion information is obtained by fusing the images with the drone's current precise pose. The visual pose fusion information is converted into simulated ultrasonic echo information by a preset echo information conversion rule. The missing echo information is then filled in according to the simulated ultrasonic echo information.

7. The control method for a tethered unmanned aerial vehicle based on ultrasonic positioning according to claim 1, characterized in that, The specific process for determining whether the path echo information after completion meets the planning conditions is as follows: After completion, the anomaly index of the characteristic curve of the path echo information is obtained again. If the anomaly index of the characteristic curve of the path echo information is greater than or equal to the anomaly threshold, it is determined that the path echo information does not meet the planning conditions after completion. If the anomaly index of the characteristic curve of the path echo information is less than the anomaly threshold, then analyze the three-dimensional point cloud data of each path echo information after completion. If the three-dimensional point cloud data shows a continuous distribution in three-dimensional space, that is, the distance between any two three-dimensional point clouds is less than the maximum interval distance preset in the planning database, then it is determined that the completed path echo information meets the planning conditions; otherwise, it is determined that the completed path echo information does not meet the planning conditions.

8. The control method for a tethered unmanned aerial vehicle based on ultrasonic positioning according to claim 1, characterized in that, The planned inspection route for the tethered drone is as follows: S31. Based on the three-dimensional point cloud data obtained from the analysis of various echo information, the starting region information of the object under test is obtained; the three-dimensional point cloud data includes the spatial coordinate information of the surface of the object under test, and a continuous three-dimensional mesh model is generated through point cloud registration and fusion technology. S32. Perform feature extraction and segmentation on the starting region information of the object under test, and identify the surface contour, edge features and key inspection areas of the object under test; the key inspection areas include the depressions, protrusions or abnormal structural areas on the surface of the object under test; S33. Based on the identified key inspection areas, generate a preliminary inspection path covering all key inspection areas; the preliminary inspection path adopts a rasterized scanning method, and calculates the path point sequence based on point cloud density and region priority to ensure complete path coverage without omissions. S34. Combining the flight constraints of the tethered UAV, the preliminary inspection path is smoothly optimized and dynamically adjusted to obtain the final inspection route; The flight constraints include the length limit of the tether cable, the maneuverability of the UAV, and the safe distance. The connection between waypoints is optimized through path planning algorithms.

9. A tethered unmanned aerial vehicle (UAV) control method based on ultrasonic positioning according to claim 1, characterized in that, The inspection process involves checking the object to be tested according to the inspection route. S41. The UAV autonomously flies along the inspection route. During the flight along the inspection route, the UAV continuously acquires high-precision position information in real time based on ultrasonic positioning and continuously sends ultrasonic detection signals. The echo information of the ultrasonic detection signals is analyzed to detect obstacles ahead in real time. When the analysis of the echo characteristics indicates that there are temporary obstacles on the UAV's flight path, the UAV immediately hovers and waits. S42. While hovering, the drone uses a waiting timer to keep track of time and continuously acquires echo signals from temporary obstacles. When the acquired obstacle echo signals disappear or weaken to below a safe threshold, the drone automatically resumes the previously interrupted inspection route from the hovering point and continues to perform the inspection task. S43. While flying along the inspection route, control the camera device on the drone to take pictures of each key inspection area in sequence according to the inspection route.

10. A tethered unmanned aerial vehicle (UAV) based on ultrasonic positioning, employing the control method for a tethered UAV based on ultrasonic positioning as described in any one of claims 1-9, characterized in that, Includes an ultrasonic module, a tether cable connection component, an integrated control module, and a camera device: The ultrasonic module is used to emit ultrasonic detection signals with specific emission parameters and receive their echo information, while simultaneously processing and analyzing the echo information. The mooring cable connection component is used to connect with the mooring cable, thereby establishing a physical connection between the UAV and the ground UAV, maintaining the task of continuous power transmission from the ground to the UAV and the data transmission task between the ground and the UAV. The integrated control module is used to receive echo information processed and analyzed by the ultrasonic module, UAV attitude data and ground data transmission information, and to plan and schedule tasks to drive the ultrasonic module and camera device. The camera device is used to perform specific inspection tasks. It is suspended below the tethered drone by a stable gimbal and, with the scheduling of the integrated control module and the stable communication provided by the tethering cable, it performs high-definition image acquisition and video streaming of the target area.

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