An autonomous inspection and charging nest system based on unmanned aerial vehicle ad hoc network

Abstract

The present application relates to the technical field of unmanned aerial vehicle cluster energy management, and discloses an autonomous inspection and charging nest system based on unmanned aerial vehicle ad hoc network, comprising: each unmanned aerial vehicle in the cluster calculates and broadcasts a freely disposable time dynamic scalar in real time, and when the scalar of a certain unmanned aerial vehicle is lower than a threshold value, a task and energy opportunity package are broadcasted, wireless energy supplement and task takeover are executed by a neighboring unmanned aerial vehicle with the minimum response time cost, and the present application drives distributed decision mechanism by energy potential difference, so that cluster energy dynamically flows along the path with the minimum resistance like liquid, energy consumption caused by fixed return paths in traditional inspection is avoided, and reliable energy cooperative transmission can still be maintained in complex environments by combining double-channel communication arbitration and mechanical locking units.

Description

TECHNICAL FIELD

[0001] The present application relates to an autonomous inspection and charging nest system based on unmanned aerial vehicle ad hoc network, belonging to the technical field of unmanned aerial vehicle cluster energy management. BACKGROUND

[0002] The current requirement is that the unmanned aerial vehicle interrupts the task and returns to charge when the remaining energy is lower than the safety threshold. The essence of its design is to force the energy supply and the fixed physical node to be bound. As the inspection range expands to complex terrain or large-span scenarios, this mode gradually exposes systematic limitations: the inspection path is forced to compromise due to the need for return, and a large amount of flight resources are consumed for non-employment round trip, and the redundant energy during return cannot contribute to task execution; it is difficult to schedule the unmanned aerial vehicle in return when a sudden task occurs, and the cluster response capability is physically fragmented; strong electromagnetic interference or airflow disturbance easily leads to charging interruption, and the existing scheme improves stability by superimposing sensors and algorithms, which aggravates the resource burden of edge devices.

[0003] Although some researches try to optimize the charging pile layout or introduce mobile charging platforms, they still cannot break through the underlying logic of static binding of energy and tasks, and cause secondary contradictions due to the increase of hardware cost and communication load. Therefore, how to realize the self-organization of cluster energy and task in a dynamic environment, so as to eliminate the dependence on return path and guarantee the robust execution in complex conditions, has become a technical problem to be solved by the present application. SUMMARY

[0004] The present application provides an autonomous inspection and charging nest system based on unmanned aerial vehicle ad hoc network, which mainly aims to solve the problems of cluster energy rigidity and task response delay caused by fixed charging paradigm.

[0005] To achieve the above purpose, the autonomous inspection and charging nest system based on unmanned aerial vehicle ad hoc network provided by the present application is characterized by comprising:

[0006] An energy potential evaluation unit is configured in each unmanned aerial vehicle in the cluster, which is used to calculate and output a dynamic state scalar representing the freely disposable time based on the current remaining energy of the unmanned aerial vehicle, the position information of the unmanned aerial vehicle and the pre-stored charging nest position information. The dynamic state scalar is the difference between the current remaining energy and the energy required to return to the nearest charging nest divided by the average cruising power consumption;

[0007] A task and energy opportunity generation unit is configured in any first unmanned aerial vehicle in the cluster, which is used to generate and broadcast an opportunity package containing the task information to be executed by the first unmanned aerial vehicle and the remaining flight time information to the ad hoc network when the dynamic state scalar of the first unmanned aerial vehicle is lower than the preset safety threshold;

[0008] The distributed decision unit is arranged in any second unmanned aerial vehicle in the ad hoc network, and is configured to receive the opportunity package and calculate a time cost required by the second unmanned aerial vehicle to respond to the opportunity package based on a state of the second unmanned aerial vehicle and information of the opportunity package, wherein the time cost is a sum of time required by the second unmanned aerial vehicle to fly to the first unmanned aerial vehicle for energy supplement and to fly away, and deducts a dynamic state scalar of the second unmanned aerial vehicle before the response.

[0009] The cooperative execution unit is arranged in the second unmanned aerial vehicle with the minimum time cost in the ad hoc network, and is configured to respond to the first unmanned aerial vehicle, drive the second unmanned aerial vehicle to move to the first unmanned aerial vehicle for non-contact wireless induction energy supplement, and take over the first unmanned aerial vehicle for a task to be performed.

[0010] Preferably, the dynamic state scalar is calculated in the following manner: wherein, represents current residual energy of the unmanned aerial vehicle, represents energy required by the unmanned aerial vehicle to return to a nearest charging nest, represents average cruising power consumption of the unmanned aerial vehicle.

[0011] Preferably, the system further comprises a dual-channel communication arbitration unit arranged in each unmanned aerial vehicle, configured to set a high-bandwidth communication channel and a narrow-band heartbeat communication channel; the dual-channel communication arbitration unit monitors communication quality of the high-bandwidth communication channel, and when the communication quality of the high-bandwidth communication channel is lower than a preset threshold, suspends broadcasting of the opportunity package, and switches to the narrow-band heartbeat communication channel to only transmit a heartbeat package containing an identity of the unmanned aerial vehicle and a quantized dynamic state scalar level, so as to realize basic energy supplement and task distribution in a communication resource limited mode.

[0012] Preferably, the dynamic state scalar level includes a safe level, a critical level and a dangerous level; in the communication resource limited mode, the unmanned aerial vehicle in the dangerous level is configured to move to and perform basic energy supplement on a nearest unmanned aerial vehicle satisfying a safe level determination among surrounding unmanned aerial vehicles.

[0013] Preferably, the system further comprises an energy transfer physical locking unit arranged in each unmanned aerial vehicle, configured to perform electric control locking of a physical hook and an anchor point in a non-contact wireless induction energy supplement process, so as to ensure successful physical connection when the two unmanned aerial vehicles are close; the energy transfer physical locking unit authorizes a wireless induction charging module in the cooperative execution unit to perform energy transmission at a maximum power after confirming successful physical connection.

[0014] Preferably, when the energy transfer physical locking unit fails to successfully lock for multiple times, the energy transfer physical locking unit rejects the current energy supplement task, instructs the first unmanned aerial vehicle to perform a return or in-place landing program, and broadcasts an energy transfer failure event to the ad hoc network.

[0015] Preferably, the system further comprises a micro-vibration adaptive compensation unit arranged on the first unmanned aerial vehicle, configured to actively send a phase detection pulse through the communication module during the energy supplement process, and inversely determine the relative vibration parameters between the two unmanned aerial vehicles according to the received carrier signal phase difference sequence, the relative vibration parameters being the determined main frequency and amplitude; and the micro-vibration adaptive compensation unit adjusts the rotation speed of the rotor of the first unmanned aerial vehicle based on the relative vibration parameters to generate a reverse torque, thereby suppressing the relative vibration.

[0016] Preferably, the micro-vibration adaptive compensation unit is further arranged on the second unmanned aerial vehicle, configured to adjust the hovering height of the second unmanned aerial vehicle according to the relative vibration parameters, so as to ensure that the average coupling distance of the coils of the two unmanned aerial vehicles remains at an optimal value under vibration conditions.

[0017] Preferably, the system further comprises an energy flow residual self-diagnosis unit arranged on the unmanned aerial vehicle performing energy supplement, configured to monitor the output current ripple of the non-contact wireless induction charging process in real time; and when the output current ripple exceeds a preset alarm threshold, the energy flow residual self-diagnosis unit judges that the communication channel quality of the ad hoc network is degraded, and triggers a preset communication link protection or switching strategy.

[0018] Preferably, the cooperative execution unit is further configured to, after taking over the task, package part or all of the task to a third unmanned aerial vehicle in the ad hoc network according to the dynamic state scalar and the geographical position of the third unmanned aerial vehicle, the dynamic state scalar of the third unmanned aerial vehicle being higher and the geographical position of the third unmanned aerial vehicle being more in line with the task demand.

[0019] Compared with the prior art, the present application has the following beneficial effects:

[0020] 1. By converting the energy state of each unmanned aerial vehicle into a unified dynamic scalar, the cluster system spontaneously forms an energy potential difference gradient, and when the disposable time of a certain node is lower than a threshold value, the task and the remaining energy of the node are automatically converted into an opportunity package, triggering a distributed decision of surrounding nodes based on opportunity cost. This mechanism makes energy and tasks flow in the cluster along the path of least resistance, reduces the energy consumption caused by fixed return paths, and improves the utilization efficiency of total energy of the system.

[0021] 2. The narrowband heartbeat channel maintains basic energy scheduling when communication deteriorates, and the physical locking of the mechanical hook improves the connection reliability during energy transmission. The combination of the two enables the system to maintain core functions through a degraded but not failed operation mode in a strong electromagnetic interference or severe airflow environment, avoiding systemic collapse caused by the breakage of a single technology chain.

[0022] 3, the power machine counteracts the main vibration frequency through the rotor reverse torque, the power machine dynamically adjusts the hovering height to maintain the optimal coupling distance, the vibration suppression mechanism based on the communication physical layer signal makes the wireless charging efficiency change from being affected by the environmental stability to being able to actively suppress and compensate the dynamic disturbance; when the charging current ripple abnormally increases, the system automatically predicts the communication quality degradation and switches the transmission channel in advance, so that the communication link protection mechanism is upgraded from passive response to active prevention, the multi-path cooperation of cross-domain perception and execution limits local faults to a single link without spreading, and significantly improves the survival ability of the cluster in unpredictable scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0023] Fig. 1 It is a task cooperation and energy supplement process schematic diagram of the autonomous inspection and charging nest system of the application;

[0024] Fig. 2 It is a response distance and time cost relationship comparison diagram of the unmanned aerial vehicle under different terrain risk levels of the application;

[0025] Fig. 3 It is a state level identification and basic energy supplement flowchart based on the heartbeat channel of the application.

[0026] The purpose of the application, functional characteristics and advantages will be further described with reference to the accompanying drawings. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical scheme and advantages of the application more clear, the technical scheme of the application will be described clearly and completely with reference to the accompanying drawings and in combination with specific embodiments. It should be noted that the embodiments in the application and the features in the embodiments can be combined with each other without conflict, and all other embodiments derived from the embodiments in the application without creative labor should belong to the scope protected by the application.

[0028] The application embodiment provides an autonomous inspection and charging nest system based on unmanned aerial vehicle ad hoc network. The system is built on an unmanned aerial vehicle cluster, and the core operation mechanism is a set of distributed energy and task cooperation scheduling procedures, mainly including four logical modules of energy potential evaluation unit, task and energy opportunity generation unit, distributed decision unit and cooperative execution unit. They cooperate to convert the energy resources in the cluster into a kind of dynamic flow and reorganization asset, rather than static binding to a single unmanned aerial vehicle or a charging facility fixed in a geographical position.

[0029] In a typical patrol task scenario, the specific operation process of the system begins with the continuous work of the energy potential evaluation unit deployed in each UAV in the cluster. To avoid the limitation of traditional solutions that only rely on the remaining battery percentage and cannot reflect the real task endurance capability, the energy potential evaluation unit is configured to calculate and output a dynamic state scalar representing the freely disposable time of the UAV based on the current remaining energy of the UAV , the real-time position information of the UAV itself, and all the charging nest geographic location information stored in the on-board database . The specific calculation procedure of the scalar follows the formula: , wherein represents the minimum energy required to return to the nest based on the distance from the current position to the nearest charging nest and other factors such as the current environmental wind resistance, and is the average cruising power consumption of the UAV model under standard cruising conditions, which is pre-calibrated or obtained through historical data statistics. In this way, the dynamic state scalar is not an isolated energy value, but a comprehensive index containing geographical and time dimensions. It accurately quantifies the time window in which the UAV can perform additional tasks or provide external support under the premise of ensuring safe return, thereby establishing a unified and quantifiable energy potential difference gradient for the entire cluster.

[0030] When the dynamic state scalar calculated by the energy potential evaluation unit of any first UAV in the cluster is less than the dynamic state scalar calculated by the energy potential evaluation unit of any second UAV in the cluster When the descent reaches a preset safety threshold, for example, 120 seconds, the task and energy opportunity generation unit is triggered, which aims to avoid the UAV entering an emergency state that must be immediately returned, but to convert potential energy demand into an opportunity that can be solved by network collaboration in advance; the unit generates a standardized data structure, namely the opportunity package, and broadcasts it to the adjacent UAVs within its communication range using the broadcast channel of the ad hoc network. The data field of the opportunity package is precisely defined to contain at least three core information: the unique identity of the initiator UAV, the task information package to be transferred, and the remaining flight time information accurate to seconds. In this way, the potential task failure risk of a single node that is about to fall into an energy dilemma is actively converted into a collaboration request that is open and bidable to the entire distributed network. Any second UAV in the ad hoc network starts the response evaluation program immediately after receiving the broadcasted opportunity package, which aims to complete the collaboration at the lowest total system cost, rather than simply selecting the nearest UAV. To achieve this goal, the unit will perform a time cost calculation based on the real-time state of the second UAV itself and the information in the opportunity package. The calculation procedure is defined as follows: first, predict and accumulate the total time cost required to perform the support task, which includes the time to fly from the current location to the first UAV location, the estimated time to perform non-contact wireless induction energy replenishment, and the time required to safely fly away to avoid flight conflicts; then, subtract the second UAV's own dynamic state scalar before responding from this total The result is the time cost of this response. The logic behind this design is that a UAV with very abundant energy reserves has a lower intrinsic cost to participate in rescue. By including the own scalar as a negative cost item, the system can encourage the most energy-rich and geographically suitable UAV to respond. Finally, among all the UAVs that receive the opportunity package and complete the calculation, the one with the smallest calculated time cost will obtain the execution right of the collaborative task.

[0031] The second unmanned aerial vehicle that obtains the execution right activates its cooperative execution unit, which is responsible for the closed loop from decision to physical execution. The unit first drives the second unmanned aerial vehicle to move to a safe distance near the first unmanned aerial vehicle according to the planned path, then performs a series of precise non-contact wireless induction energy supplement actions, and after the energy transmission is completed, it takes over the task defined in the opportunity package through the communication link and continues to complete the inspection. In other words, to pursue system efficiency, the cooperative execution unit is also configured to perform secondary packaging of the task after taking over the task, that is, according to its own dynamic state scalar after taking over the task and the current geographical location, it determines whether it is necessary to package part or all of the taken-over task again in the form of an opportunity package to a third unmanned aerial vehicle with a higher dynamic state scalar and a geographical location more suitable for the subsequent task demand in the ad hoc network, so as to realize the optimal drift of the task in the cluster. To cope with the disturbance of the complex electromagnetic and airflow environment to the system during the inspection process, the system also integrates a triple fault-tolerant system of communication-control-energy. At the communication level, each unmanned aerial vehicle is equipped with a dual-channel communication arbitration unit, which sets and monitors a high-bandwidth communication channel and a narrow-band heartbeat communication channel. Under normal working conditions, complex information such as opportunity packages is transmitted through the high-bandwidth channel, and when the unit detects that the communication quality of the high-bandwidth channel is lower than a preset threshold, it will suspend the broadcast of the opportunity package and automatically switch to the narrow-band heartbeat communication channel. In this mode, the unmanned aerial vehicle only transmits a heartbeat package containing its identity and the quantized dynamic state scalar level. The level is divided into safe level, critical level and dangerous level. At this time, the unmanned aerial vehicle in the dangerous level is programmed to move to the one with the shortest physical distance among the ones in the safe level within its communication range to perform basic energy supplement. This mechanism ensures that even in harsh environments where communication resources are severely limited, the basic energy cooperation and survival ability of the cluster can be maintained.

[0032] At the physical control level, to solve the problem of relative displacement and unstable connection caused by air flow disturbance when two drones charge wirelessly in the air, each drone is equipped with an energy transfer physical locking unit and a micro-vibration self-adaptive compensation unit. Before entering the energy supplement stage, the energy transfer physical locking unit is responsible for the electric control locking operation of the physical hook and the corresponding anchor point. Through mechanical structure, it ensures the reliable physical connection of the two machines after approaching. Only after the unit confirms the successful physical locking, it will send an authorization signal to the wireless induction charging module to allow it to transmit energy at the maximum power. If the locking fails after multiple attempts, for example, three times, the unit will veto this energy supplement task and instruct the first drone to perform the preset homeward or in-place safe landing procedure, and broadcast the energy transfer failure event to the ad hoc network at the same time. At the same time, the micro-vibration self-adaptive compensation unit works continuously during the locking period to suppress the influence of micro-vibration on charging efficiency. As the energy receiver, the first drone actively sends phase detection pulses to the energy supplier drone, and according to the phase difference sequence of the received return carrier signal, it inverses the relative vibration main frequency and amplitude between the two machines through a Kalman filter algorithm. Subsequently, based on these vibration parameters, the unit real-time adjusts the differential speed of its rotor to generate a reverse compensation torque, thereby actively suppressing the relative vibration. As the energy supplier, the second drone dynamically adjusts its hovering height based on the same relative vibration parameters to ensure that the average coupling distance of the wireless charging coils on the two drones remains near the pre-marked optimal value under the condition of residual vibration. This combination of hard and soft mechanisms changes the success rate of wireless charging from passive dependence on environmental stability to active immunity to dynamic disturbances.

[0033] At the deep diagnostic level of energy transmission, the system also includes an energy flow residual self-diagnostic unit configured in the drone performing energy supplement. Since the communication quality of the ad hoc network indirectly affects the real-time performance of the wireless charging control instruction, which is then reflected in the stability of the charging current, the unit is configured to monitor the output current ripple in real time using a high-frequency sampler during the charging process. When the effective value of the output current ripple exceeds a preset alarm threshold, for example, 5% of the nominal current, the system does not attribute it to charging hardware failure first, but judges it as a sign of deterioration or congestion of the communication channel quality of the ad hoc network. This judgment will immediately trigger the preset communication link protection or switching strategy, such as notifying the dual-channel communication arbitration unit to prepare to switch to the narrowband heartbeat channel in advance. This cross-domain perception mechanism, which predicts communication domain risks by monitoring subtle changes in the energy domain, upgrades the system's fault response from passive response to active prevention, significantly improving the system-level resilience of the entire cluster in unpredictable scenarios.

[0034] Embodiment 1: In a power grid line autonomous inspection task covering a large area of high mountain valley region, a first UAV is hovering for data collection at a critical power transmission tower located in a remote valley bottom. A sudden local strong airflow causes the UAV to consume energy rapidly to maintain stable hovering, and the dynamic state scalar calculated by the energy potential evaluation unit of the UAV breaks the preset safety threshold in a short time. At this time, the UAV not only exceeds the required safe return energy from any preset charging nest, but also has a high-priority data collection task that cannot be interrupted. In view of this situation, the task and energy opportunity generation unit of the first UAV is triggered and broadcasts an opportunity package containing its own identity, the remaining collection point sequence of the critical power transmission tower to be handed over, and the accurate remaining flight time to the ad hoc network. At this time, a second UAV is performing a regular inspection task on another ridge. Because it has a higher dynamic state scalar , the distributed decision unit of the second UAV determines that it is the optimal responder after receiving the opportunity package and responds. However, when the second UAV flies into the valley region, the dual-channel communication arbitration unit monitors that the communication quality of the high-bandwidth communication channel has decreased significantly due to the complex terrain obstruction. This mechanism is triggered to actively switch to the narrow-band heartbeat communication channel to maintain basic contact with the first UAV, ensuring the execution basis of the core goal of energy support task under the condition of communication degradation.

[0035] After the second UAV approaches and mechanically connects with the first UAV through the energy transfer physical locking unit, the non-contact wireless induction energy supplement process starts, at this moment, the energy flow residual error self-diagnosis unit configured in the second UAV starts to work, the output current ripple monitored by it slightly exceeds the preset alarm threshold due to the unstable communication link interference to the charging control command, this physical representation in the energy domain objectively provides evidence from another physical dimension for the previous judgment of the communication domain state by the dual-channel communication arbitration unit, forming a factual cross-domain information verification closed loop, further strengthening the comprehensive perception ability of the system in complex environment; In this process, the micro-vibration self-adaptive compensation units carried by the two machines work cooperatively, use the communication carrier phase difference to inverse the relative vibration parameters caused by the valley wind, and respectively suppress and compensate by adjusting the rotor speed and hovering height, so as to simultaneously cope with the energy transmission stability under physical disturbance and the communication reliability under weak signal environment within a single system architecture; After the energy supplement is completed, the cooperative execution unit not only completes the takeover of the tasks to be executed by the first UAV, but also directly continues to execute the subsequent inspection task because the geographical position of the second UAV is closer to the next task point at this time, and the first UAV which obtains energy supplement is in standby or return state; By converting potential single point failure into dynamic reorganization of energy and tasks within the cluster, the system provides a mechanism so that the coverage boundary and duration of the inspection operation is no longer limited by the endurance of a single node under the worst working condition, but depends on the energy reserve and cooperative efficiency of the whole cluster, the constraint of return charging which must be considered in the original task planning is replaced by a dynamic and on-demand energy network in the task execution stage.

[0036] Example 2: To quantitatively verify the effectiveness of the micro-vibration adaptive compensation unit of the present application in maintaining the efficiency of non-contact wireless induction energy replenishment under dynamic disturbance, a ground hardware-in-the-loop simulation test platform is constructed, which consists of a second unmanned aerial vehicle as the energy supplier and a first unmanned aerial vehicle as the energy receiver. Both vehicles are equipped with complete energy transfer physical locking units and micro-vibration adaptive compensation units. The first unmanned aerial vehicle is fixed on a six-degree-of-freedom motion platform, which can accurately reproduce the vibration caused by air flow disturbance according to a pre-set program. The second unmanned aerial vehicle hovers above the first unmanned aerial vehicle and establishes mechanical connection with it through the physical locking unit. The test environment is equipped with a high-precision power analyzer and a laser displacement sensor, which are used to record the energy transmission efficiency and the relative displacement between the charging coils of the two vehicles in real time, respectively. The core parameter of the test, i.e. the vibration profile simulated by the six-degree-of-freedom motion platform, is set according to a decision logic aimed at engineering reality. The technical factors that affect the setting of this parameter include the disturbance frequency range caused by wind shear in the real flight environment and the sensitivity of the unmanned aerial vehicle's rotor system to specific frequencies. The parameter setting ensures that the applied vibration is sufficient to challenge the alignment of the wireless charging coils to test the performance of the compensation unit, and avoids introducing abnormal vibrations that trigger the flight control system into protection mode to ensure the effectiveness of the test results for typical application scenarios. Based on this, the decision rule is set to select the main vibration frequency from the frequency spectrum range of 0.5 Hz to 5 Hz, which covers most of the atmospheric turbulence frequencies commonly encountered in low-altitude airspace. In this test, a sinusoidal vibration with a main frequency of 2.5 Hz and an amplitude of ±1.5 cm is selected for a simulated urban high-rise building wind field scenario, and a low-frequency random drift signal is superimposed as a benchmark challenge for the compensation system.

[0037] The test is divided into a control group and a test group for comparison. Both groups of tests are performed under the condition that the above vibration profile is activated. The first unmanned aerial vehicle and the second unmanned aerial vehicle in the control group disable the function of the micro-vibration adaptive compensation unit and rely only on the rigid connection of the physical locking unit to resist vibration. The test group enables the complete compensation function. After the test starts, in the control group, the relative displacement between the charging coils of the two vehicles changes dramatically with the vibration of the platform. The peak displacement recorded by the laser displacement sensor reaches 14.8 mm, and the power analyzer shows that the energy transmission efficiency fluctuates greatly with the displacement, dropping to 37% of the nominal value at the lowest. In the test group, when the vibration is applied, the micro-vibration adaptive compensation unit of the energy-receiving first unmanned aerial vehicle analyzes the communication carrier phase difference and inverses the 2.5 Hz main vibration frequency, and drives the rotor to generate a counteracting moment. At the same time, the energy-supplying second unmanned aerial vehicle also dynamically adjusts the hover height according to the shared vibration parameters. Under the cooperative action of the two vehicles, the relative displacement between the coils is significantly suppressed, with a peak displacement of no more than 3.5 mm, and the energy transmission efficiency is stably maintained above 85% of the nominal value.

[0038] Table 1: Data sampling comparison table for some key states in the test process.

[0039]

[0040] The test data, see Table 1, shows that the stability of the energy transmission efficiency in the test group has a direct correspondence with the activation state of the micro-vibration adaptive compensation unit, which suppresses the influence of external physical disturbance on the energy transmission efficiency within a certain range through active compensation; The results of this test confirm the feasibility of the cooperative mechanism proposed in this invention based on communication physical layer signal feedback and active compensation of flight dynamics in engineering, which provides a technical approach for realizing efficient energy supplement between unmanned aerial vehicles in dynamic and non-ideal environments.

[0041] Example 3: This example combines Figs. 1 to 3 , a kind of based on unmanned aerial vehicle ad hoc network's autonomous inspection and charging nest system is described, as shown in Fig. 1 , first, the unmanned aerial vehicle 1 is in normal inspection, the energy potential evaluation unit in its interior will calculate the dynamic state scalar If the calculation result is less than the safety threshold, start the task and energy opportunity generation unit, the unit broadcasts the opportunity package containing task information and remaining flight time information to the ad hoc network, the adjacent unmanned aerial vehicles 2, 3... N receive the opportunity package, and the distributed decision unit of each will calculate and submit the response time cost based on the local state and opportunity package information, the system selects the one with the minimum time cost as the responder, the selected unmanned aerial vehicle 2 responder starts the cooperative execution unit immediately, drives itself to fly to the target position, that is, close to the unmanned aerial vehicle 1, after completing the approach, the non-contact wireless energy supplement is executed, the process includes physical locking and energy transmission, which ensures efficient energy supplement, at the same time, the micro-vibration adaptive compensation unit based on communication phase difference inversion vibration parameters, actively suppresses disturbance through reverse torque and height adjustment, further guarantees the energy transmission efficiency, the system also includes a dual-channel communication arbitration unit, which monitors the high-bandwidth communication channel quality in real time, if it is lower than the preset threshold, it will automatically degrade to the narrowband heartbeat channel to maintain the basic energy cooperation in the harsh communication environment, after completing the energy supply, enter the task takeover and dynamic reorganization phase: UAV2 takes over the task, UAV1 obtains energy, and according to the situation, selects to continue to execute the task or return to standby, thereby completing the task and energy cooperation reconstruction between unmanned aerial vehicles.

[0042] As shown in Fig. 2As shown, three types of typical terrain environments, low-risk plains, medium-risk mountains, and high-risk canyons, are identified by three broken lines in the figure. On the X-axis, the coordinate axis of the response distance, the distance range is from 5 km to 40 km, and the sampling interval is 5 km. On the Y-axis, the time cost is represented in seconds, which quantifies the comprehensive time cost required for the UAV to complete the support task from receiving the opportunity package. The solid line dots in the figure represent the time cost curve in the low-risk plain environment. As the response distance increases, the time cost in this environment shows a linear slow upward trend. The dashed line triangle represents the medium-risk mountain environment, which shows a moderate increase in cost due to complex terrain. The dashed line box represents the high-risk canyon environment, and the curve has the largest slope, indicating that the time cost of the UAV responding to the task in the high-risk canyon terrain is significantly higher than in other terrain types.

[0043] As shown in Fig. 3 First, the dangerous level UAV detects the communication quality through its dual-channel communication arbitration unit. If it finds that the current high-bandwidth channel communication quality is lower than the threshold, the system will automatically execute the operation of suspending the opportunity package broadcast and switching to the narrow-band channel. Then, the dangerous level UAV broadcasts the heartbeat package identity / danger level to the network through the switched narrow-band heartbeat channel. The heartbeat package is sent to the nearby safe level UAV through the narrow-band channel forwarding heartbeat package process. After receiving the heartbeat package, the system of the safe level UAV immediately identifies the dangerous level signal and confirms that it is a safe level. Then, through the algorithm logic of calculating the closest distance, it judges the relative distance relationship between the dangerous level UAV and the safe level UAV. After confirming the response, the safe level UAV will autonomously move close to the dangerous level UAV and perform the basic energy supplement operation after approaching. After the supplement is completed, the system will update the state level and notify the confirmation of the supplement completion. At this time, the communication link returns to the basic maintenance to ensure the system survivability under the minimum communication resources.

[0044] In order to ensure that the autonomous inspection and charging nest system of the present application has engineering certainty and environmental adaptability in specific deployment, this embodiment discloses an offline calibration and parameter self-tuning method applied before system deployment. After loading a set of general system software on a specific model of UAV hardware, a set of running parameters is generated for the specific software and hardware combination through a series of standardized tests. First, the base cruising power consumption of the UAV is calibrated. In an open test field under standard atmospheric conditions, the UAV is instructed to perform a closed-loop flight path with a total range of five kilometers at its typical inspection speed. The onboard power management unit records the instantaneous power consumption during flight at a frequency of 1 Hz. After flight, the power consumption data during takeoff and landing is removed, and the arithmetic mean of the power consumption data points during cruising is taken. This result is determined as the baseline cruising power consumption of the UAV under this standard condition. In order to ensure that the autonomous inspection and charging nest system of the present application has engineering certainty and environmental adaptability in specific deployment, this embodiment discloses an offline calibration and parameter self-tuning method applied before system deployment. After loading a set of general system software on a specific model of UAV hardware, a set of running parameters is generated for the specific software and hardware combination through a series of standardized tests. First, the base cruising power consumption of the UAV is calibrated. In an open test field under standard atmospheric conditions, the UAV is instructed to perform a closed-loop flight path with a total range of five kilometers at its typical inspection speed. The onboard power management unit records the instantaneous power consumption during flight at a frequency of 1 Hz. After flight, the power consumption data during takeoff and landing is removed, and the arithmetic mean of the power consumption data points during cruising is taken. This result is determined as the baseline cruising power consumption of the UAV under this standard condition. ; in other words, to make the energy required for the return flight to be able to dynamically reflect the influence of wind resistance, the system in real-time flight, according to the motor output power reported by the flight control system and the difference between the reference cruise power consumption , through a look-up table to solve a dimensionless wind resistance compensation coefficient in real time, the coefficient is used to dynamically correct the calculation results; then, the dynamic state scalar safety threshold triggering the opportunity package broadcast is not fixed, the setting principle of the threshold is to balance the margin of task execution and the correlation of potential risk, the specific procedure is to divide the inspection area into multiple grid of different risk levels according to the density of obstacles, electromagnetic interference intensity and the distribution of non-landing area in the task planning stage, and set a basic time margin for each risk level, the basic margin of an open area marked as low risk is set to 120 seconds, and the basic margin of a complex obstacle area marked as high risk is set to 300 seconds, the safety threshold of the unmanned aerial vehicle will be adaptively adjusted according to the risk level of the grid it is currently in during operation.

[0045] For the calculation of response time cost in the distributed decision unit, the estimated energy supplement time is determined by dividing the energy difference required for the first unmanned aerial vehicle of the request party to recover to the safety threshold of the area where it is located by the wireless charging power of the model unmanned aerial vehicle under nominal conditions, and the result is the estimated energy supplement time; the triggering logic of the second sub-contract of the task that the cooperative execution unit may execute is determined as follows: when the second unmanned aerial vehicle takes over the task, if its dynamic state scalar decreases due to support tasks, and the system detects that there is a third unmanned aerial vehicle with a better geographical location and its dynamic state scalar is higher than that of the second unmanned aerial vehicle by a certain percentage, i.e. 150%, the second sub-contract mechanism is activated; finally, the linkage threshold of the dual-channel communication arbitration unit and the energy flow residual self-diagnosis unit is cooperatively calibrated; in the shielded laboratory, the signal-to-noise ratio of the high-bandwidth communication channel between the unmanned aerial vehicles is gradually reduced by using a programmable signal attenuator, and at each signal-to-noise ratio level, a standard power wireless charging process is performed, and the effective value of the charging output current is recorded. Through this process, a mapping relationship database of channel signal-to-noise ratio and current ripple value is established. Then, a signal-to-noise ratio defined as the critical point of communication quality, i.e. 12dB, is selected. The current ripple value corresponding to the critical point in the database, i.e. 4.5% of the nominal current, is fixed as the alarm threshold of the energy flow residual self-diagnosis unit. In this way, when the unmanned aerial vehicle monitors that the current ripple reaches the threshold in actual flight, it can infer that the communication channel quality has deteriorated to the critical point according to the database relationship, and trigger the preset communication link switching strategy.

[0046] In a specific deployment scenario, the procedure for generating the geospatial risk map on which the system relies for its operation is as follows: first, obtain the digital elevation model and three-dimensional obstacle data of the target inspection area, and calculate the ground complexity and flight clearance parameters of each geographic grid through geometric analysis; second, send a survey drone equipped with a spectrum analyzer to fly along the predetermined grid path, collect and record the broadband electromagnetic interference field strength at different locations, and form an electromagnetic environment interference layer; finally, normalize the original data of the three dimensions of terrain complexity, obstacle density, and electromagnetic interference strength, and calculate a comprehensive risk index for each geographic grid through a preset weighted sum function. The risk indices of all grids together constitute the risk level map of the region, which is distributed to each drone in the cluster.

[0047] To deal with the potential situation where there are no nodes in the network that can safely perform the support task, the system is built with a set of collaborative task veto and self-rescue upgrade procedures. After calculating the response time cost, a second drone needs to perform an additional safety self-assessment. This assessment will predict the expected dynamic state scalar of the second drone after completing the energy replenishment of the first drone and returning to its safe area If the expected scalar is lower than the safety threshold of its current location, the second drone will voluntarily give up the response. If the first drone does not receive any response from a drone that has passed the safety self-assessment within a specific time window, i.e., 60 seconds, after broadcasting the opportunity package, its task and energy opportunity generation unit will automatically revoke the opportunity package and immediately trigger the highest priority autonomous return or emergency landing program. This mechanism provides a definite final action plan for the system when it faces a situation that cannot be solved through collaborative energy replenishment.

[0048] In order to determine the geometry of the energy transfer physical locking unit and the control law of the micro-vibration adaptive compensation unit, the following procedure was adopted in a hardware-in-the-loop simulation and control parameter calibration scenario; the anchor point of the locking unit was designed as an inverted conical funnel with an internal angle of thirty degrees, and the depth and the engagement length of the hook were determined in an offline optimization program, which searched for the geometry combination that maximized the physical capture success rate by simulation calculation under a given set of initial relative displacement and velocity disturbances, and finally determined the funnel depth as five centimeters and the effective engagement length of the hook as three centimeters; the control law for suppressing relative vibration in the micro-vibration adaptive compensation unit was calibrated, which was determined as a proportional-integral-derivative controller whose input was the coil relative displacement derived from the communication carrier phase difference and whose output was the adjustment amount of the unmanned aerial vehicle rotor speed; during the calibration process, the six-degree-of-freedom motion platform was instructed to apply a series of single-frequency harmonic vibrations, with the frequency range covering 0.5Hz to 5Hz, and at each frequency point, the proportional, integral, and derivative gain parameters of the controller were adjusted iteratively, and the root mean square value of the residual relative displacement measured by the laser displacement sensor was taken as the performance indicator to find an optimal gain combination that minimized the indicator, which was then solidified into the flight control software of the unmanned aerial vehicle.

[0049] In the same simulation environment, the Kalman filter algorithm adopted by the micro-vibration adaptive compensation unit has an internal state vector defined as a four-dimensional vector containing the relative position and relative velocity in the two-dimensional plane between the charging coils, and its state transition matrix is constructed based on a discrete-time model of Newton's law, which assumes that the relative acceleration is Gaussian white noise in a short time, and the measurement matrix defines the linear relationship between the observation value of the communication carrier phase difference and the relative position component in the state vector; to deal with the abnormal situation of complete communication interruption that may occur during energy replenishment, the system sets up a two-way heartbeat guardian mechanism; during the physical locking process of the two machines, if either party does not receive the heartbeat packet from the other party within 500 milliseconds, it is judged that complete communication interruption has occurred, at which time the two machines will immediately suspend the locking and energy transmission actions, and at the same time trigger a pre-set, communication-coordinated-free emergency separation procedure, i.e. the energy receiver unmanned aerial vehicle vertically climbs five meters, and the energy supplier unmanned aerial vehicle vertically descends five meters, thereby establishing a safe distance in the shortest time.

[0050] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0051] Finally, it should be noted that the above examples are merely intended to illustrate the technical solutions of the present application and not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. An autonomous inspection and charging nesting system based on a drone self-organizing network, characterized in that, include: The energy potential assessment unit, configured in each drone in the cluster, is used to calculate and output a dynamic state scalar representing the available time in real time based on the drone's current remaining energy, drone location information, and pre-stored charging nest location information. The dynamic state scalar is the result of dividing the difference between the current remaining energy and the energy required to return to the nearest charging nest by the average cruise power consumption. The task and energy opportunity generation unit is configured in any of the first UAVs in the cluster. When the dynamic state scalar of the first UAV is lower than a preset safety threshold, it generates and broadcasts an opportunity packet containing the task information to be performed by the first UAV and the remaining flight time information to the ad hoc network. The distributed decision unit, configured in any second UAV in the ad hoc network, is used to receive opportunity packets and calculate the time cost required for the second UAV to respond to the opportunity packet based on its own state and opportunity packet information. The time cost is the sum of the time required for the second UAV to fly to the first UAV for energy replenishment and fly away, minus the dynamic state scalar of the second UAV before responding. The collaborative execution unit is configured in the second drone with the lowest time cost in the ad hoc network. It is used to respond to the first drone, drive the second drone to move to the vicinity of the first drone for non-contact wireless inductive energy replenishment, and take over the tasks to be performed by the first drone.

2. The autonomous inspection and charging nesting system based on UAV self-organizing network according to claim 1, characterized in that, Dynamic state scalar The calculation method is as follows: ,in, This indicates the drone's current remaining energy. This indicates the energy required for the drone to return to its nearest charging base. This indicates the average cruise power consumption of the drone.

3. The autonomous inspection and charging nesting system based on a drone self-organizing network according to claim 1, characterized in that, The system also includes a dual-channel communication arbitration unit, configured on each UAV, used to set up a high-bandwidth communication channel and a narrowband heartbeat communication channel; the dual-channel communication arbitration unit monitors the communication quality of the high-bandwidth communication channel, and when the communication quality of the high-bandwidth communication channel is lower than a preset threshold, it suspends the broadcast of opportunity packets and switches to the narrowband heartbeat communication channel, transmitting only heartbeat packets containing the UAV's identity and quantized dynamic status scalar level.

4. The autonomous inspection and charging nesting system based on UAV self-organizing network according to claim 3, characterized in that, The dynamic state scalar levels include safety level, critical level, and danger level; in communication resource-constrained mode, a drone with a danger level is configured to move to the nearest drone among those that meet the safety level criteria and perform basic energy replenishment.

5. The autonomous inspection and charging nesting system based on UAV self-organizing network according to claim 1, characterized in that, The system also includes: an energy transfer physical locking unit, configured on each drone, used to perform electronic locking of the physical grappling hook and anchor point during non-contact wireless inductive energy replenishment, to ensure a successful physical connection when the two drones approach each other; after confirming a successful physical connection, the energy transfer physical locking unit authorizes the wireless inductive charging module in the cooperative execution unit to transfer energy at maximum power.

6. The autonomous inspection and charging nesting system based on a drone self-organizing network according to claim 5, characterized in that, If the energy transfer physical locking unit fails to lock after multiple attempts, it rejects the energy replenishment mission and instructs the first UAV to perform a return-to-home or on-site landing procedure, while broadcasting the energy transfer failure event to the ad hoc network.

7. The autonomous inspection and charging nesting system based on UAV self-organizing network according to claim 1, characterized in that, The system also includes: a micro-vibration adaptive compensation unit, configured on the first UAV, used to actively send phase detection pulses using its communication module during the energy replenishment process, and to determine the relative vibration parameters between the two UAVs based on the phase difference sequence of the received carrier signal. The relative vibration parameters are the determined main frequency and amplitude. The micro-vibration adaptive compensation unit adjusts the rotational speed of the rotor of the first UAV to generate a reverse torque based on the relative vibration parameters.

8. The autonomous inspection and charging nesting system based on UAV self-organizing network according to claim 7, characterized in that, The micro-vibration adaptive compensation unit is also configured on the second UAV to adjust the hovering height of the second UAV according to the relative vibration parameters.

9. The autonomous inspection and charging nesting system based on UAV self-organizing network according to claim 1, characterized in that, The system also includes: an energy flow residual self-diagnostic unit, configured on the drone performing energy replenishment, used to monitor the output current ripple during the contactless wireless inductive charging process in real time; when the output current ripple exceeds the preset alarm threshold, the energy flow residual self-diagnostic unit determines that the communication channel quality of the self-organizing network has deteriorated and triggers the preset communication link protection or switching strategy.

10. The autonomous inspection and charging nesting system based on UAV self-organizing network according to claim 1, characterized in that, After taking over the task, the collaborative execution unit is also configured to subcontract part or all of the task to a third UAV in the ad hoc network with a higher dynamic state scalar and a geographical location that better meets the task requirements, based on its own dynamic state scalar and geographical location.

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