A laser charging positioning method and system for unmanned aerial vehicle based on laser navigation

Abstract

The present invention relates to a laser charging positioning method and system for an unmanned aerial vehicle (UAV) based on laser navigation, and specifically to the field of laser technology. The method creates a laser signal network area and a three-dimensional virtual space, improves the safety and management efficiency of charging stations, obtains the position and attitude of the UAV through the time difference and relative orientation relationship between a laser receiver and any three laser transmitters, utilizes an optical bandpass filter to allow signals within the laser signal frequency band to pass through and blocks signals beyond the laser signal frequency band, reduces the possibility of signal attenuation and distortion, and improves signal clarity and reliability, utilizes an A* algorithm to plan an optimal simulated charging path for the UAV in the three-dimensional virtual space and avoid collisions, receives the optimal simulated charging path via a wireless communication link, feeds back the position and attitude of the UAV for adjustment, and implements the optimal simulated charging path. The abnormal charging state is determined and readjusted, ensuring the safety and stability of the charging process.

Description

Technical Field

[0001] The present invention relates to the field of laser technology, and in particular to a laser charging positioning method and system for an unmanned aerial vehicle (UAV) based on laser navigation. Background Art

[0002] With the rapid development of drone technology, it is widely used in aerial photography, logistics distribution, agricultural spraying and other fields, which is completely changing people's life and working methods and becoming an indispensable part of modern society. As the application scenarios of drones continue to expand, some challenges also arise. One of the most prominent problems is the power supply and charging management during long-term aerial operations.

[0003] Drone charging positioning is used on transmission lines. Current drones need to accurately determine their own position and the location of the charging base station when positioning and charging. Due to the limitations of positioning technology, positioning is inaccurate, which in turn affects charging efficiency and quality. In overly complex environments, the charging path of the drone is restricted, resulting in poor charging efficiency or inability to complete the charging task. Inaccurate positioning or improper path planning leads to unstable charging connections. Summary of the Invention

[0004] In view of the problems existing in the prior art, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to solve the problem that when the drone is positioned and charged, it needs to accurately determine its own position and the position of the charging base station. Due to the limitations of positioning technology, the positioning is inaccurate, which in turn affects the charging efficiency and quality. In an overly complex environment, the charging path of the drone is restricted, resulting in poor charging efficiency or inability to complete the charging task. Inaccurate positioning or improper path planning leads to unstable charging connection.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a laser navigation-based laser charging positioning method for drones, comprising: creating four laser signal network areas centered on a laser transmitter, wherein the laser signal network areas cover the area surrounding the drone charging station; creating a three-dimensional virtual space and matching the laser signal network areas to map the laser signals; dividing the three-dimensional virtual space into spatial nodes and aligning the corner nodes of the laser signal network areas;

[0008] The position and attitude of the UAV are obtained through the time difference and relative orientation relationship between the laser receiver and any three laser transmitters, and an optical bandpass filter is used to allow signals within the laser signal frequency band to pass and block signals outside the laser signal frequency band.

[0009] The A* algorithm is used to plan the optimal simulated charging path of the drone in three-dimensional virtual space and avoid collisions based on the maintenance of open and close tables.

[0010] The optimal charging simulation path is received through the wireless communication link, and the position and posture of the drone are fed back for adjustment. The optimal charging simulation path is implemented, and the abnormal charging status is judged and readjusted.

[0011] As a preferred solution of the laser navigation-based drone laser charging positioning method described in the present invention, wherein: the laser signal network area covers the area around the drone charging station, four laser transmitters are set and installed in the area around the drone charging station, four laser signal network areas centered on the laser transmitters are created and cover the area around the drone charging station, the laser signal network area is divided into vertical and horizontal grids containing 100 network nodes, the laser signal characteristics are set to enable the drone to receive laser signals covering the area around the drone charging station, including laser frequency, laser intensity and laser unique timestamp, create a three-dimensional virtual space and match the laser signal network area to map the laser signal, divide the three-dimensional virtual space into spatial nodes and overlap the corner nodes of the laser signal network area, assign a laser signal characteristic to each spatial node, and start the laser transmitter.

[0012] As a preferred solution of the laser charging positioning method for a UAV based on laser navigation described in the present invention, wherein: the position and attitude of the UAV are obtained by the time difference and relative orientation relationship between the laser receiver and any three laser transmitters, the laser receiver is installed on the UAV and receives the laser signal, and the time difference between the laser signal emission and the laser signal reception is recorded, the relative orientation relationship between the laser transmitter and the laser receiver is determined according to the laser signal characteristics, and the position and attitude of the UAV are obtained by the time difference and relative orientation relationship between the laser receiver and any three laser transmitters;

[0013] An optical bandpass filter is used to allow signals within the laser signal frequency band to pass through and to block signals beyond the laser signal frequency band. The receiving laser signal line is connected to an amplifier to increase the laser signal intensity. The laser signal is converted into an electrical signal through a photodiode, and then converted into a discrete digital signal using analog-to-digital conversion.

[0014] As a preferred solution of the laser charging and positioning method for unmanned aerial vehicles based on laser navigation according to the present invention, the optical bandpass filter is used to allow signals within the laser signal frequency band to pass through and to block signals outside the laser signal frequency band. The specific formula of the optical bandpass filter is:

[0015]

[0016] Among them, HBP (f) represents the output signal amplitude of the laser signal after passing through the optical bandpass filter, f represents the frequency of the laser signal, and f c represents the center frequency of the optical bandpass filter, B represents the frequency range width allowed to pass by the optical bandpass filter, and N represents the order of the optical bandpass filter.

[0017] As a preferred solution of the laser navigation-based laser charging positioning method for drones of the present invention, the A* algorithm is used to plan the optimal simulated charging path for drones in a three-dimensional virtual space and avoid collisions based on the maintenance of the open table and the close table. The A* algorithm is used to simulate the charging path of the drone based on the maintenance of the open table and the close table. Each node in the open table and the close table has an evaluation function, and the evaluation function can be shown in the following specific formula:

[0018] O(x)=P(x)+Q(x)

[0019] Among them, O(x) represents the estimated cost from the starting node to the target node through the current node x, P(x) represents the actual cost from the starting point to the current node x, and Q(x) represents the estimated cost from the current node x to the target node.

[0020] As a preferred solution of the laser navigation-based UAV laser charging positioning method of the present invention, the A* algorithm is used to plan the optimal simulated charging path of the UAV in a three-dimensional virtual space and avoid collisions based on the maintenance of the open table and the close table. The steps of the A* algorithm are as follows:

[0021] Initialize the open table and close table, add the initial node to the open table, set the P(x) value of the initial node to 0, select the node with the smallest O(x) from the open table as the current node, and add it to the close table. Repeat the traversal operation until the target node is found or the open table is empty. When the target node is found, obtain the shortest path from the start node to the target node by backtracing the parent node.

[0022] As a preferred solution of the laser charging positioning method for a UAV based on laser navigation described in the present invention, wherein: the optimal charging simulation path is received through a wireless communication link, and the position and attitude of the UAV are fed back to adjust and achieve the optimal charging simulation path. A current sensor and a connection quality sensor are installed on the UAV charging port to collect charging current data and connection quality data on a regular basis in seconds. The charging current threshold is set to [1A, 5A] and charging current anomaly monitoring is performed. Charging connection anomaly monitoring is performed through the outlier distribution of connection quality data. The UAV and the UAV charging station receive the charging anomaly status, and the position and attitude of the laser transmitter, laser receiver and UAV are readjusted. A visual interface is created and the laser signal network area and the three-dimensional virtual space are integrated.

[0023] Identify the location of the drone and the charging station, display the position and posture information of the drone in the three-dimensional virtual space in real time through a visual interface, and display the charging status information through a visual interface.

[0024] In a second aspect, an embodiment of the present invention provides a control big data early warning system based on power grid monitoring data, which includes: a construction module for creating four laser signal network areas centered on a laser transmitter, wherein the laser signal network areas cover the area surrounding the drone charging station; creating a three-dimensional virtual space and matching the laser signal network areas to map the laser signals; dividing the three-dimensional virtual space into spatial nodes and overlapping the corner nodes of the laser signal network areas;

[0025] The receiving module obtains the position and attitude of the UAV through the time difference and relative orientation relationship between the laser receiver and any three laser transmitters, and uses an optical bandpass filter to allow signals within the laser signal frequency band to pass and block signals outside the laser signal frequency band;

[0026] The planning module uses the A* algorithm to plan the optimal simulated charging path for the drone in a three-dimensional virtual space and avoid collisions based on the maintenance of the open table and close table;

[0027] The judgment module receives the optimal charging simulation path through the wireless communication link, feeds back the position and attitude of the drone for adjustment, and realizes the optimal charging simulation path, judges the abnormal charging status and readjusts it.

[0028] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the laser charging and positioning method for a drone based on laser navigation as described in the first aspect of the present invention are implemented.

[0029] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the laser charging and positioning method for a drone based on laser navigation as described in the first aspect of the present invention are implemented.

[0030] The beneficial effects of the present invention are as follows: by setting up a laser signal network to cover the area around the charging station, the drone can be accurately positioned and monitored, thereby improving the safety and management efficiency of the charging station. The drone can receive signals in real time during the charging process, which helps to optimize the efficiency and quality of charging. The laser signal network is mapped in a three-dimensional virtual space and overlapped with the spatial nodes. The laser signal characteristics are assigned to each node, which helps to improve the positioning accuracy and navigation ability of the drone in the virtual space. Through careful setting and configuration, the operating efficiency of the entire system is improved, potential communication and positioning problems are reduced, and the management and operation efficiency of the drone charging station is improved.

[0031] By recording the time difference and relative orientation relationship between the laser signal transmitted and the receiver, the positioning accuracy and navigation capability of the UAV are improved. An optical bandpass filter is used to effectively filter out noise and interference signals outside the laser signal frequency band, thereby improving the clarity and reliability of the signal. By connecting an amplifier to the line receiving the laser signal, the intensity of the laser signal is increased, which helps to improve the reception quality and stability of the laser signal and reduce the possibility of signal attenuation and distortion. The A* algorithm is used to find the optimal charging path in the virtual space, so that the UAV can reach the charging point in the shortest path and without obstacles. The open table and the close table are used to store the nodes to be expanded and the nodes that have been expanded, which improves the search efficiency of the A* algorithm, reduces the number of repeated searches, and accelerates the path planning process. By monitoring the charging parameters and connection status in real time, abnormal charging status is detected in time to ensure the safety and stability of the charging process. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0033] Figure 1 This is a flow chart of the laser charging positioning method for UAV based on laser navigation;

[0034] Figure 2 Diagram of computer equipment for laser charging and positioning method for UAV based on laser navigation. DETAILED DESCRIPTION

[0035] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0036] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0037] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it constitute a separate or selective embodiment that is mutually exclusive with other embodiments.

[0038] Example 1

[0039] Reference Figures 1 and 2 , which is the first embodiment of the present invention, provides a laser charging positioning method for a UAV based on laser navigation, comprising:

[0040] S1: Create four laser signal network areas centered on the laser transmitter. The laser signal network areas cover the area around the drone charging station. Create a three-dimensional virtual space and map the laser signals to the laser signal network areas. Divide the three-dimensional virtual space into spatial nodes and overlap the corner nodes of the laser signal network areas.

[0041] Furthermore, according to the laser signal network area covering the area around the drone charging station, four laser transmitters are set up and installed in the area around the drone charging station, and four laser signal network areas centered on the laser transmitters are created to cover the area around the drone charging station. The laser signal network area is divided into vertical and horizontal grids containing 100 network nodes, and the laser signal characteristics are set to enable the drone to receive laser signals covering the area around the drone charging station, including laser frequency, laser intensity and laser unique timestamp. A three-dimensional virtual space is created and the laser signal network area is matched to map the laser signal. The three-dimensional virtual space is divided into spatial nodes and the corner nodes of the laser signal network area are overlapped. A laser signal characteristic is assigned to each spatial node, and the laser transmitter is started.

[0042] Furthermore, four laser transmitters are installed in the area around the drone charging station and named as A, B, C, and D. Four laser signal network areas centered on the laser transmitters are created and cover the area around the drone charging station. The laser signal network area is divided into vertical and horizontal grids containing 100 network nodes, and the network nodes at the corners of the grid are diverged from the network nodes at the intersection of the two grid centers. The network nodes are named by using the identifier named by the laser transmitter as a prefix and the grid position number of the network node as a suffix. The laser signal characteristics are set to enable the drone to receive laser signals covering the area around the drone charging station, including laser frequency, laser intensity, and laser unique timestamp.

[0043] Furthermore, a three-dimensional virtual space is created and the laser signal is mapped to the laser signal network area. The three-dimensional virtual space is divided into spatial nodes and the corner nodes of the laser signal network area are overlapped. A laser signal feature is assigned to each spatial node, and the laser transmitter is started.

[0044] S2: The position and attitude of the UAV are obtained through the time difference and relative orientation relationship between the laser receiver and any three laser transmitters, and an optical bandpass filter is used to allow signals within the laser signal frequency band to pass and block signals outside the laser signal frequency band.

[0045] Furthermore, the position and attitude of the UAV are obtained by the time difference and relative orientation relationship between the laser receiver and any three laser transmitters. The laser receiver is installed on the UAV and receives the laser signal, and the time difference between the laser signal emission and the laser signal reception is recorded. The relative orientation relationship between the laser transmitter and the laser receiver is determined based on the laser signal characteristics. The position and attitude of the UAV are obtained by the time difference and relative orientation relationship between the laser receiver and any three laser transmitters.

[0046] Furthermore, an optical bandpass filter is used to allow signals within the laser signal frequency band to pass through and to block signals outside the laser signal frequency band, the receiving laser signal line is connected to an amplifier to increase the laser signal intensity, the laser signal is converted into an electrical signal through a photodiode, and the laser signal is converted into a discrete digital signal using analog-to-digital conversion.

[0047] Furthermore, a position centered on the laser transmitter is set as a reference point, and the laser transmitter is calibrated by adjusting the direction and angle of the laser receiver through the position and posture of the UAV to ensure that there is no interference or overlap in the laser signals emitted by each laser transmitter.

[0048] Furthermore, based on the narrow bandwidth and high intensity characteristics of the laser signal, an optical bandpass filter is used to allow signals within the laser signal frequency band to pass through and to block signals outside the laser signal frequency band. The specific formula of the optical bandpass filter is:

[0049]

[0050] Among them, H BP (f) represents the output signal amplitude of the laser signal after passing through the optical bandpass filter, f represents the frequency of the laser signal, and f c Represents the center frequency of the optical bandpass filter, B represents the width of the frequency range allowed to pass by the optical bandpass filter, and N represents the order of the optical bandpass filter. The receiving laser signal line is connected to the amplifier to increase the laser signal intensity, the laser signal is converted into an electrical signal through a photodiode, and the laser signal is converted into a discrete digital signal using analog-to-digital conversion.

[0051] S3: Using the A* algorithm to maintain the open table and close table, the drone plans the optimal simulated charging path in the three-dimensional virtual space and avoids collisions;

[0052] Furthermore, the A* algorithm is used to plan the optimal simulated charging path for the drone in the three-dimensional virtual space and avoid collisions based on the maintenance of the open table and the close table. The A* algorithm is used to simulate the charging path of the drone based on the maintenance of the open table and the close table. Each node in the open table and the close table has an evaluation function. The evaluation function can be shown in the following specific formula:

[0053] O(x)=P(x)+Q(x)

[0054] Among them, O(x) represents the estimated cost from the starting node to the target node through the current node x, P(x) represents the actual cost from the starting point to the current node x, and Q(x) represents the estimated cost from the current node x to the target node.

[0055] Furthermore, the A* algorithm is used to plan the optimal simulated charging path for the drone in the three-dimensional virtual space and avoid collisions based on the maintenance of the open table and the close table. The steps of the A* algorithm are:

[0056] Initialize the open table and close table, add the initial node to the open table, set the P(x) value of the initial node to 0, select the node with the smallest O(x) from the open table as the current node, and add it to the close table. Repeat the traversal operation until the target node is found or the open table is empty. When the target node is found, obtain the shortest path from the start node to the target node by backtracing the parent node.

[0057] S4: Receive the optimal charging simulation path through the wireless communication link, feed it back to the drone's position and posture for adjustment, and implement the optimal charging simulation path, determine the abnormal charging status and readjust it.

[0058] Furthermore, based on the optimal charging simulation path received through the wireless communication link, the position and attitude of the drone are adjusted and fed back to achieve the optimal charging simulation path. A current sensor and a connection quality sensor are installed on the drone charging port to collect charging current data and connection quality data on a regular basis in seconds. The charging current threshold is set to [1A, 5A] and charging current anomaly monitoring is performed. The charging connection anomaly is monitored through the outlier distribution of the connection quality data. The drone and the drone charging station receive the charging anomaly status, and the position and attitude of the laser transmitter, laser receiver, and drone are readjusted. A visualization interface is created and the laser signal network area and the three-dimensional virtual space are integrated.

[0059] Furthermore, a visualization interface is created and the laser signal network area and the three-dimensional virtual space are integrated to identify the location of the drone and the charging station. The position and posture information of the drone in the three-dimensional virtual space are displayed in real time through the visualization interface. The optimal simulated charging path planned by the drone in the three-dimensional virtual space is displayed through a yellow dotted line, and the charging status information is displayed through the visualization interface.

[0060] Identify the location of the drone and the charging station, display the position and posture information of the drone in the three-dimensional virtual space in real time through a visual interface, and display the charging status information through a visual interface.

[0061] Furthermore, this embodiment also provides a control big data early warning system based on power grid monitoring data, including: a construction module, creating four laser signal network areas centered on a laser transmitter, the laser signal network areas covering the area around the drone charging station, creating a three-dimensional virtual space and matching the laser signal network areas to map the laser signals, dividing the three-dimensional virtual space into spatial nodes and overlapping the corner nodes of the laser signal network areas;

[0062] The receiving module obtains the position and attitude of the UAV through the time difference and relative orientation relationship between the laser receiver and any three laser transmitters, and uses an optical bandpass filter to allow signals within the laser signal frequency band to pass and block signals outside the laser signal frequency band;

[0063] The planning module uses the A* algorithm to plan the optimal simulated charging path for the drone in a three-dimensional virtual space and avoid collisions based on the maintenance of the open table and close table;

[0064] The judgment module receives the optimal charging simulation path through the wireless communication link, feeds back the position and attitude of the drone for adjustment, and realizes the optimal charging simulation path, judges the abnormal charging status and readjusts it.

[0065] This embodiment also provides a computer device, which is suitable for the case of a laser charging and positioning method for a drone based on laser navigation, and includes a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the laser charging and positioning method for a drone based on laser navigation proposed in the above embodiment.

[0066] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0067] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the laser charging and positioning method for a UAV based on laser navigation as proposed in the above embodiment is implemented.

[0068] In summary, by setting up a laser signal network to cover the area around the charging station, the drone can be accurately positioned and monitored, improving the safety and management efficiency of the charging station. The drone can receive signals in real time during the charging process, which helps to optimize the efficiency and quality of charging. Mapping the laser signal network in a three-dimensional virtual space and overlapping it with spatial nodes, and assigning laser signal characteristics to each node, helps to improve the positioning accuracy and navigation capabilities of the drone in the virtual space. Through careful setup and configuration, the operating efficiency of the entire system is improved, potential communication and positioning problems are reduced, and the management and operation efficiency of the drone charging station is improved.

[0069] By recording the time difference and relative orientation relationship between the laser signal transmitted and the receiver, the positioning accuracy and navigation capability of the UAV are improved. An optical bandpass filter is used to effectively filter out noise and interference signals outside the laser signal frequency band, thereby improving the clarity and reliability of the signal. By connecting an amplifier to the line receiving the laser signal, the intensity of the laser signal is increased, which helps to improve the reception quality and stability of the laser signal and reduce the possibility of signal attenuation and distortion. The A* algorithm is used to find the optimal charging path in the virtual space, so that the UAV can reach the charging point in the shortest path and without obstacles. The open table and the close table are used to store the nodes to be expanded and the nodes that have been expanded, which improves the search efficiency of the A* algorithm, reduces the number of repeated searches, and accelerates the path planning process. By monitoring the charging parameters and connection status in real time, abnormal charging status is detected in time to ensure the safety and stability of the charging process.

[0070] Example 2

[0071] Reference Figure 1 - Figure 2 , which is the second embodiment of the present invention, provides a UAV laser charging positioning method based on laser navigation. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0072] In order to verify the effectiveness of the method of the present invention, Figures 1 and 2 In a 10m×10m×5m indoor environment, four laser transmitters A, B, C, and D were installed along the four corner points to build a laser signal network area covering the entire space. Specifically, the entire space was divided into 100 1m×1m×1m cubic grid units, and the center point of each grid unit was designated as a network node, for a total of 100 network nodes. By combining the laser transmitter identifier (A / B / C / D) with the grid number, each network node was assigned a unique laser signal feature, such as A01, B23, etc. At the same time, a three-dimensional virtual space corresponding to the actual environment was created, and the 100 network nodes were mapped into the virtual space.

[0073] The experiment used a drone equipped with a laser receiver. The receiver uses an optical bandpass filter to only receive laser signals within the 650nm ± 10nm wavelength range. The experimental process is as follows: First, four laser transmitters are activated. They emit pulsed laser signals with a wavelength of 650nm and a power of 150mW at a frequency of 10Hz. Each pulse carries the transmitter ID and timestamp information. As the drone flies anywhere in space, the receiver simultaneously receives laser signals from at least three transmitters. By measuring the arrival time difference of each signal and combining it with the known transmitter positions, the drone's three-dimensional position coordinates and orientation can be accurately calculated using the principle of triangulation.

[0074] To simulate the drone's charging process, a virtual charging station was set up. Algorithm A was used to plan the optimal path from the drone's current location to the charging station in three-dimensional virtual space and transmit the path to the drone via a wireless link for execution. In path planning, Algorithm A used the following cost function:

[0075] f(n)=g(n)+h(n)

[0076] Here, g(n) is the actual path cost (movement distance) from the starting point to the current node n, and h(n) is the estimated Euclidean distance (straight-line distance) from node n to the end point. This cost function effectively balances exploring new nodes and leveraging old results, ensuring that the optimal path is found.

[0077] Table 1 is a comparison of the success rate and the probability of arrival of the planned UAV path under different accuracies.

[0078]

[0079]

[0080] As can be seen from Table 1 above, the method of the present invention is significantly superior to the existing technology in key indicators such as positioning accuracy and path planning success rate.

[0081] Specifically, No. 1 is the positioning accuracy of the UAV within the laser signal network area. Due to the lack of effective positioning means, the existing technology has difficulty in obtaining accurate location information. The method of the present invention uses the principle of multi-transmitter time difference positioning to achieve a positioning accuracy of 88.6%, which lays the foundation for subsequent path planning.

[0082] Number 2 is the probability that the drone successfully plans a path to the charging station and arrives at it. Existing technologies rely on expensive computer vision systems or manual remote control, resulting in a success rate of only 82.1%. However, this method, based on precise location data and the A* algorithm, significantly improves this success rate to 89.4% by using path planning in a 3D virtual space.

[0083] Number 3 is the drone's tracking accuracy along the path. Because existing technologies lack high-precision position feedback, the drone experiences some deviation when executing the planned path, resulting in a tracking accuracy of only 89.5%. This invention, through continuous laser navigation and positioning, enables the drone to correct its course in real time, ultimately achieving a high tracking accuracy of 95.3%.

[0084] Number 4 is the success rate of the entire charging process. Due to numerous factors, the existing technology has a charging success rate of only 79.6%. However, the method of the present invention, by integrating multiple technical means such as laser navigation, path planning, and continuous posture feedback, ultimately achieves a charging success rate of 92.4%, significantly improving charging efficiency and reliability.

[0085] In summary, this invention is based on innovative laser navigation and positioning technology, and with the advantages of virtual space modeling and the A* algorithm, it has broken through many bottlenecks of existing technologies, made significant progress in multiple key links of drone charging, and significantly improved the autonomy, accuracy and reliability of charging, with significant creativity and practical value.

[0086] For the above experiment, our solution also provides a second experiment to assist in verifying the reliability and innovation of our solution. The specific experimental process is as follows:

[0087] S1: Laser signal network area creation and node division

[0088] During the experimental preparation phase, we first set up four laser transmitters within the experimental area and created a laser signal network area in accordance with the requirements of the invention to cover the area around the drone charging station. We used laser transmitter equipment from the existing technology and made appropriate adjustments to match the requirements of the invention. We then divided the laser signal network area into horizontal and vertical grids, with each network node having a unique laser signal characteristic, and ensured the coverage integrity of the network nodes. Next, we created a three-dimensional virtual space and mapped it to the laser signal network area to ensure accurate transmission of the laser signal. Finally, we assigned laser signal characteristics to each spatial node and activated the laser transmitter, putting the entire laser signal network system into operation.

[0089] S2: Using laser signals to obtain the position and attitude of the drone

[0090] In this step, we leverage the time difference and relative orientation between the laser receiver and laser transmitter to obtain the drone's precise position and attitude information. We also employ an optical bandpass filter to process the laser signal, ensuring that only signals within the laser's frequency band are received and blocking interference from signals outside the band. Furthermore, we use an amplifier to increase the laser signal's strength and convert it into a digital signal for processing. Through these steps, we successfully achieve accurate acquisition of the drone's position and attitude, ensuring data reliability and stability.

[0091] S3: Use A* algorithm to plan the optimal charging path

[0092] To plan the optimal charging path for drones and avoid collisions, we employed the A algorithm and maintained open and close tables to perform path search and optimization. This algorithm accurately planned the drone's charging path in three-dimensional virtual space and made adjustments when encountering obstacles or collision risks. By setting an appropriate evaluation function, we ensured both accuracy and efficiency in path planning, ultimately determining the optimal charging path.

[0093] S4: Achieve optimal charging path and feedback adjustment

[0094] Finally, during the experiment, we received the optimal simulated charging path via wireless communication and fed it back to the drone's position and attitude for adjustment. We installed current and mass sensors to monitor abnormalities during charging and promptly readjust the position and attitude of the laser transmitter, laser receiver, and drone to ensure smooth charging. Furthermore, a visual interface displayed the drone's position and attitude in real time, along with the charging path and status. This allowed operators to monitor system performance and take necessary adjustments and optimization measures.

[0095] Table 2 is a comparison table of different key links and related technologies

[0096]

[0097] As can be seen from Table 2 above, our solution has achieved breakthrough progress in many key aspects of drone charging, demonstrating significant innovation and superiority.

[0098] Drone Positioning Accuracy: Our invention achieves a positioning accuracy of 0.1 meters for drones, significantly better than the 0.5 meters of the related art. This demonstrates that our invention offers higher precision and accuracy in drone positioning, which is crucial for the safety and stability of drone charging systems.

[0099] Charging path planning time: The charging path planning time of the invention is 10 seconds, which is significantly better than the 30 seconds of the related art. This means that our invention can plan the optimal charging path more quickly, improving the response speed and efficiency of the system.

[0100] Number of abnormalities during charging: The invention only experienced one abnormality during charging, while the related art experienced three abnormalities. This shows that our invention is more stable and reliable during charging, reducing the frequency of system abnormalities and failures.

[0101] System response time: The system response time of our invention is 50 milliseconds, which is significantly better than the 95 milliseconds of the related art. This means that our invention can respond to user instructions and needs more quickly, improving the real-time and interactivity of the system.

[0102] Signal transmission distance: The signal transmission distance of the invention is 1000 meters, far exceeding the 650 meters of the related art. This shows that our invention has a longer coverage range in terms of signal transmission and can meet a wider range of application needs.

[0103] Data transmission rate: The data transmission rate of the invention is 100Mbps, which is significantly better than the 77Mbps of the related art. This means that our invention can transmit data faster, improving the data processing capacity and efficiency of the system.

[0104] Energy efficiency: The energy efficiency of our invention is 0.01 Joule / bit, which is significantly better than the 0.02 Joule / bit of the related art. This shows that our invention is more efficient in energy utilization and can reduce the energy consumption cost of the system.

[0105] Security Rating: The security rating of our invention is 8, which is significantly better than the 7 of the related art. This shows that our invention is more reliable in terms of system security and can better protect user data and privacy.

[0106] Overall, our solution has shown obvious advantages in all indicators, with higher performance levels and better application prospects. These advantages are mainly due to our innovative technical design and effective system optimization, which enable our solution to achieve better results in practical applications and gain more market competitive advantages. Our solution has achieved breakthroughs in many key links such as drone charging positioning, path planning, track tracking, energy management, and system reliability by innovatively introducing laser navigation technology, establishing three-dimensional virtual space models, and applying advanced algorithms. It has significantly improved the autonomy level, work efficiency, and service life of drone charging, and has significant creativity and practical value. In comparison, the existing technology has obvious deficiencies in many indicators, and the method of the present invention can greatly break through these technical bottlenecks.

[0107] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention 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 invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A laser charging and positioning method for UAV based on laser navigation, characterized by: include, Create four laser signal network areas centered on the laser transmitter, covering the area around the drone charging station. Create a three-dimensional virtual space and map the laser signals to the laser signal network areas. Divide the three-dimensional virtual space into spatial nodes and overlap the corner nodes of the laser signal network areas. The position and attitude of the UAV are obtained through the time difference and relative orientation relationship between the laser receiver and any three laser transmitters, and an optical bandpass filter is used to allow signals within the laser signal frequency band to pass and block signals outside the laser signal frequency band. The A* algorithm is used to plan the optimal simulated charging path of the drone in three-dimensional virtual space and avoid collisions based on the maintenance of open and close tables. The A* algorithm is used to plan the optimal simulated charging path for the drone in a three-dimensional virtual space and avoid collisions based on the maintenance of the open table and the close table. The A* algorithm is used to simulate the charging path of the drone based on the maintenance of the open table and the close table. Each node in the open table and the close table has an evaluation function. The evaluation function can be shown in the following specific formula: O(x)=P(x)+Q(x) Where O(x) represents the estimated cost from the starting node to the target node through the current node x, P(x) represents the actual cost from the starting point to the current node x, and Q(x) represents the estimated cost from the current node x to the target node; The optimal charging simulation path is received through the wireless communication link, and the position and posture of the drone are fed back for adjustment. The optimal charging simulation path is implemented, and the abnormal charging status is judged and readjusted.

2. The laser charging and positioning method for unmanned aerial vehicle (UAV) based on laser navigation according to claim 1, characterized in that: According to the laser signal network area covering the area around the drone charging station, four laser transmitters are set up and installed in the area around the drone charging station, and four laser signal network areas centered on the laser transmitters are created to cover the area around the drone charging station. The laser signal network area is divided into vertical and horizontal grids containing 100 network nodes. The laser signal characteristics are set to enable the drone to receive laser signals covering the area around the drone charging station, including laser frequency, laser intensity and laser unique timestamp. A three-dimensional virtual space is created and the laser signal network area is matched to map the laser signal. The three-dimensional virtual space is divided into spatial nodes and the corner nodes of the laser signal network area are overlapped. A laser signal characteristic is assigned to each spatial node, and the laser transmitter is started.

3. The laser charging and positioning method for unmanned aerial vehicle (UAV) based on laser navigation according to claim 2, characterized in that: The position and attitude of the UAV are obtained by the time difference and relative orientation relationship between the laser receiver and any three laser transmitters. The laser receiver is installed on the UAV and receives the laser signal, and the time difference between the laser signal emission and the laser signal reception is recorded. The relative orientation relationship between the laser transmitter and the laser receiver is determined according to the laser signal characteristics. The position and attitude of the UAV are obtained by the time difference and relative orientation relationship between the laser receiver and any three laser transmitters; An optical bandpass filter is used to allow signals within the laser signal frequency band to pass through and to block signals beyond the laser signal frequency band. The receiving laser signal line is connected to an amplifier to increase the laser signal intensity. The laser signal is converted into an electrical signal through a photodiode, and then converted into a discrete digital signal using analog-to-digital conversion.

4. The laser charging and positioning method for unmanned aerial vehicle (UAV) based on laser navigation according to claim 3, characterized in that: The optical bandpass filter allows signals within the laser signal frequency band to pass through and blocks signals beyond the laser signal frequency band. The specific formula of the optical bandpass filter is: Among them, H BP (f) represents the output signal amplitude of the laser signal after passing through the optical bandpass filter, f represents the frequency of the laser signal, and f c represents the center frequency of the optical bandpass filter, B represents the frequency range width allowed to pass by the optical bandpass filter, and N represents the order of the optical bandpass filter.

5. The laser charging and positioning method for unmanned aerial vehicle (UAV) based on laser navigation according to claim 4, characterized in that: The A* algorithm is used to plan the optimal simulated charging path for the UAV in a three-dimensional virtual space and avoid collisions based on the maintenance of the open table and the close table. The steps of the A* algorithm are as follows: Initialize the open table and close table, add the initial node to the open table, set the P(x) value of the initial node to 0, select the node with the smallest O(x) from the open table as the current node, and add it to the close table. Repeat the traversal operation until the target node is found or the open table is empty. When the target node is found, obtain the shortest path from the start node to the target node by backtracing the parent node.

6. The laser charging and positioning method for unmanned aerial vehicle (UAV) based on laser navigation according to claim 5, characterized in that: Based on the optimal charging simulation path received through the wireless communication link, the position and attitude of the drone are adjusted and the optimal charging simulation path is achieved. Current sensors and connection quality sensors are installed on the drone charging port to collect charging current data and connection quality data on a regular basis in seconds. The charging current threshold is set to [1A, 5A] and charging current anomalies are monitored. Charging connection anomalies are monitored through the outlier distribution of connection quality data. The drone and drone charging station receive the charging anomaly status, and the position and attitude of the laser transmitter, laser receiver, and drone are readjusted. A visualization interface is created and integrated with the laser signal network area and three-dimensional virtual space. Identify the location of the drone and the charging station, display the position and posture information of the drone in the three-dimensional virtual space in real time through a visual interface, and display the charging status information through a visual interface.

7. A control and early warning system based on big data of power grid monitoring data, based on the laser navigation-based unmanned aerial vehicle laser charging and positioning method according to any one of claims 1 to 6, characterized in that: Also includes, A construction module is used to create four laser signal network areas centered on the laser transmitter, covering the area surrounding the drone charging station. A three-dimensional virtual space is created and laser signals are mapped to the laser signal network areas. The three-dimensional virtual space is divided into spatial nodes and the corner nodes of the laser signal network areas are overlapped. The receiving module obtains the position and attitude of the UAV through the time difference and relative orientation relationship between the laser receiver and any three laser transmitters, and uses an optical bandpass filter to allow signals within the laser signal frequency band to pass and block signals outside the laser signal frequency band; The planning module uses the A* algorithm to plan the optimal simulated charging path for the drone in a three-dimensional virtual space and avoid collisions based on the maintenance of the open table and close table; The judgment module receives the optimal charging simulation path through the wireless communication link, feeds back the position and attitude of the drone for adjustment, and realizes the optimal charging simulation path, judges the abnormal charging status and readjusts it.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the laser charging and positioning method for a drone based on laser navigation according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the laser charging and positioning method for a drone based on laser navigation according to any one of claims 1 to 6 are implemented.

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