Split type mobile charging robot based on automatic combination connection technology and intelligent management and control system thereof

Abstract

The invention relates to the technical field of split type charging robots, in particular to a split type mobile charging robot based on an automatic combination connection technology and an intelligent management and control system of the split type mobile charging robot. The method comprises the following steps: a spatial modeling unit performs real-time modeling on a parking lot by using a cloud large model; the visual positioning unit adopts a PnP algorithm to identify the positions of the to-be-charged vehicle and the mobile charging robot based on the spatial modeling unit; the automatic planning and scheduling unit automatically plans and schedules the nearest mobile charging robot based on the visual positioning unit according to the call of the user, and is used for quickly responding to the charging demand of the user; the charging management and control unit manages the charging process of the mobile charging robot and monitors various parameters in the charging process in real time. According to the design, the nearest charging robot, the mobile battery box and the automatic gun inserting and pulling robot are automatically planned and dispatched through the Di jkstra algorithm, so that the waiting time of a user is remarkably shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of split-type charging robots, and in particular to a split-type mobile charging robot based on automatic combination connection technology and an intelligent management and control system thereof. Background Art

[0002] With the popularity of electric vehicles, traditional fixed charging stations are difficult to meet the growing demand for charging. Especially in urban central areas, due to limited land resources, the cost of building fixed charging stations is high, and often cannot cover all areas that need charging; many users face difficulties in finding available charging stations, especially during peak hours, when there may be queues at charging stations, resulting in inconvenience in charging. Fixed charging stations usually require users to drive their cars to designated locations to charge, which not only increases the user's waiting time, but also requires users to manually operate the charging process in some cases, reducing charging efficiency; the location of fixed charging stations is usually fixed, which limits users' charging options in emergency situations; most existing charging solutions lack sufficient intelligent functions and cannot be dynamically scheduled and optimized according to real-time needs. Therefore, a split mobile charging robot based on automatic combination connection technology and its intelligent management and control system are designed. Summary of the invention

[0003] The purpose of the present invention is to provide a split mobile charging robot and an intelligent management and control system based on automatic combination connection technology, so as to solve the problems of inconvenient charging, low charging efficiency, insufficient charging flexibility and low intelligence level proposed in the above background technology.

[0004] To achieve the above object, the present invention aims to provide an intelligent management and control system for a split mobile charging robot based on automatic combination connection technology, comprising:

[0005] A space modeling unit, wherein the space modeling unit uses a cloud-based large model to perform real-time modeling of the parking lot;

[0006] A visual positioning unit, wherein the visual positioning unit uses a PnP algorithm to identify the positions of the vehicle to be charged and the mobile charging robot based on the spatial modeling unit, and optimizes the PnP algorithm by considering the influence of noise and specular reflection of light under low light conditions on the recognition accuracy;

[0007] An automatic planning and scheduling unit, which automatically plans and schedules the nearest mobile charging robot based on the visual positioning unit and the user's call, so as to quickly respond to the user's charging needs;

[0008] A charging control unit manages the charging process of the mobile charging robot and monitors various parameters during the charging process in real time.

[0009] As a further improvement of the technical solution, the space modeling unit uses a cloud-based large model to perform real-time modeling of the parking lot, including the following steps:

[0010] S1.1. Define a three-dimensional rectangular coordinate system for locating all objects in the parking lot, and select the center of the parking lot as the origin;

[0011] S1.2. Collect the basic structural information of the parking lot, including the location, size, shape and entrance and exit locations of the parking spaces, create a basic map of the parking lot using the basic structural information data, and digitize it to form a three-dimensional model;

[0012] S1.3, use 3D modeling software Blender to build a 3D model of the parking lot, import the digitized basic map data into the 3D modeling software, and build a 3D model based on the basic map data, including parking spaces, pillars, and walls;

[0013] S1.4. Upload the 3D model to the cloud and use the computing resources of the cloud to perform large-scale data processing and model updating.

[0014] As a further improvement of the technical solution, the visual positioning unit identifies the positions of the vehicle to be charged and the mobile charging robot, including the following steps:

[0015] S2.1. Continuously capture image data in the parking lot through the visual acquisition module, identify the charging equipment logo through the ORB algorithm, and extract the feature points of the vehicle to be charged and the mobile charging robot in the image;

[0016] S2.2, use OCR technology to automatically identify the license plate number and bind the license plate number to the corresponding vehicle;

[0017] S2.3, using the known parking lot 3D model and the extracted vehicle information to calculate the actual position of the vehicle through the PnP algorithm, and at the same time, considering the influence of noise and specular reflection of light on recognition accuracy under low light conditions, the PnP algorithm is optimized;

[0018] Among them, considering the influence of noise and specular reflection of light on recognition accuracy under low light conditions, the PnP algorithm is optimized as follows:

[0019]

[0020] Where N represents the number of feature points of the vehicle to be charged and the mobile charging robot in the image, i represents the index variable, represents the minimization objective function, R represents the rotation matrix; w i (x i ) represents the weight of the i-th feature point; E represents the expected value operator; xi represents the two-dimensional coordinates of the i-th feature point in the image; K represents the camera intrinsic parameter matrix; R represents the rotation matrix from the center position coordinates of the parking lot to the camera coordinates; t represents the translation vector from the center position coordinates of the parking lot to the camera coordinates; X i represents the three-dimensional coordinates of the i-th feature point in the image; ΔX i Indicates the deviation between the actual position and the theoretical position of the i-th feature point. The deviation is used to reflect the influence of the specular reflection of the light. i represents an additional error term used to compensate for the effects of noise and specular reflection of light in low light conditions;

[0021] S2.4. When a vehicle enters or leaves a parking space or a charging device moves, the vehicle's location information is updated in real time;

[0022] S2.5. Once the vehicle is parked in a parking space, the vehicle is automatically bound to the corresponding parking space based on the license plate recognition result. The mobile charging robot uploads the location to the cloud parking model through its own GPS and updates the location information in the system in real time.

[0023] As a further improvement of the technical solution, in S2.1, identifying the charging device identifier by using the ORB algorithm includes the following steps:

[0024] S2.11, first use the FAST method to quickly detect corner points, and then assign a direction to the detected corner points so that the feature description has rotation invariance;

[0025] S2.12. For each detected feature point, use the BRIEF descriptor to generate a fixed-length binary string;

[0026] S2.13. Matching is performed by comparing the BRIEF descriptors. If the match is successful, it is considered that the corresponding charging device identifier has been found.

[0027] As a further improvement of the technical solution, in S2.2, the automatic recognition of the license plate number using OCR technology includes the following steps:

[0028] S2.21, using the visual acquisition module to collect images containing the license plate, and intercepting frames containing the license plate from the video stream as input;

[0029] S2.22, preprocessing the video stream;

[0030] S2.23, use the Canny edge detection algorithm to find the edges in the image, and filter out the license plate area based on the size and proportion characteristics of the license plate;

[0031] S2.24, calculate the pixel distribution in the vertical direction, find the gaps between the characters, and further perform horizontal segmentation on the characters in the license plate based on the vertical segmentation, and detect and segment each character on the license plate;

[0032] S2.25. Use the identified license plate number to query the vehicle information in the database and match the license plate number with the personal information of the vehicle owner.

[0033] As a further improvement of the technical solution, in S2.23, using the Canny edge detection algorithm to find the edge in the image includes the following steps:

[0034] S2.231, use a Gaussian filter to smooth the grayscale image to remove noise;

[0035] S2.232, apply the Sobel filter to calculate the gradient magnitude and direction of each pixel in the image, the gradient magnitude represents the edge strength, and the gradient direction points to the direction of the edge;

[0036] S2.233, perform local maximum detection on the gradient amplitude according to the gradient direction, retain only the local maximum value as the edge candidate, and set the remaining pixels to zero;

[0037] S2.234, set two thresholds: a low threshold and a high threshold. The high threshold is used to determine strong edge points, while the low threshold is used to determine weak edge points. Strong edge points are directly considered as edge points. If weak edge points are connected to strong edge points, they are considered as edge points; otherwise, they will be eliminated.

[0038] S2.235. Use a tracking algorithm to connect edge points to form continuous edge segments.

[0039] As a further improvement of the technical solution, the automatic planning and scheduling unit includes an automatic scheduling module and an emergency scheduling module;

[0040] Among them, the automatic scheduling module is used to automatically plan and call the nearest mobile charging robot and automatic plug-in gun robot for quick response and plan the operation path through the Dijkstra algorithm to reach the designated location in the shortest time. Considering the influence of obstacles, road surface flatness and road surface width on path planning, the above influence quantities are substituted into the Dijkstra algorithm process, and considering the influence of rainfall and snow depth on the road surface in extreme rain and snow weather, the Dijkstra algorithm process is further optimized;

[0041] The emergency dispatch module is used to automatically call service personnel to the designated location for service when there is no automatic plugging and unplugging robot, and the charging robot fails to recognize humans when it arrives at the designated location, and no one responds to the operation within 2 minutes.

[0042] As a further improvement of the technical solution, considering the influence of obstacles, road surface flatness and road surface width on path planning, the above influence quantities are substituted into the Dijkstra algorithm process, specifically:

[0043] w′(e,o)=w(e,o)+f obs (e,o)+F surf (e,o)+f width (e, o);

[0044] Considering the impact of rainfall and snow depth on the road surface in extreme rain and snow weather, the Dijkstra algorithm process is further optimized as follows:

[0045] w″(e,o)=w(e,o)+f obs (e,o)+f surf (e,o)+f width (e,o)+k r ·r(o)+k s s(o);

[0046] Among them, w'(e, o) represents the edge weight after considering the influence of obstacles, road surface flatness and road surface width on path planning; w”(e, o) represents the edge weight after considering the influence of rainfall and snow depth on the road surface in extreme rain and snow weather; w(e, o) represents the original edge weight; f obs (e, o) represents the obstacle influence function; f surf (e, o) represents the road surface roughness function; f width (e, o) represents the road width influence function; e represents the edge; o represents the observation condition; k r represents the rainfall coefficient; r(o) represents the rainfall; k s represents the snow depth coefficient; s(o) represents the snow depth.

[0047] As a further improvement of the technical solution, the charging control unit manages the charging process of the mobile charging robot, including the following steps:

[0048] S3.1. The automatic gun insertion and extraction robot inserts the charging gun of the charging robot into the vehicle; the mobile battery box automatically connects to the charging robot; the charging process begins, the charging robot capacity is used first for charging, and the mobile charging robot's bidirectional DC / DC is used to automatically control the input voltage range to achieve wide-range voltage compatibility to meet the use of DC low-voltage piles and DC high-voltage piles; if the capacity of a single mobile battery box is insufficient, the remaining mobile battery boxes are automatically called to come over for replacement;

[0049] S3.2. After the constant power charging phase ends, the charging robot switches to slow charging with its own battery, and the mobile battery box automatically detaches to serve other vehicles.

[0050] S3.3. When charging is about to end, the vehicle owner is automatically notified and the mobile automatic gun insertion and extraction robot is notified to return to the vehicle. When charging is completed, the gun is automatically extracted to enter the next round of service.

[0051] On the other hand, the present invention provides a split mobile charging robot based on automatic combination connection technology, and the intelligent management and control system of the split mobile charging robot based on automatic combination connection technology described in any one of claims 1-9 is applied to the split mobile charging robot based on automatic combination connection technology.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] 1. The split mobile charging robot based on automatic combination connection technology and its intelligent management and control system can quickly respond to users' charging requests, automatically plan and dispatch the nearest charging robot, mobile battery box and automatic plug-in gun robot through the Dijkstra algorithm, thereby significantly shortening the user's waiting time; by optimizing the weight function in the Dijkstra algorithm and considering factors such as rainy and snowy weather, obstacles, road surface flatness and road surface width, the system can plan the optimal path for the charging robot to ensure that it reaches the destination quickly and safely.

[0054] 2. The split mobile charging robot and its intelligent management and control system based on automatic combination connection technology adopt the PnP algorithm and take into account the influence of noise and mirror reflection of light on position recognition under low-light conditions. This enables the system to more accurately identify the positions of vehicles and mobile charging robots, reduce position recognition errors, and improve system reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is the overall flow chart of the present invention;

[0056] The meaning of each number in the figure is:

[0057] 1. Spatial modeling unit; 2. Visual positioning unit; 3. Automatic planning and scheduling unit; 4. Charging control unit. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] Embodiment 1:

[0060] See also Figure 1 As shown, this embodiment provides an intelligent management and control system for a split mobile charging robot based on automatic combination connection technology, including:

[0061] The space modeling unit 1 uses a large cloud model to model the parking lot in real time;

[0062] In this embodiment, the cloud server usually has powerful computing resources and can process large amounts of data and complex models, which is essential for real-time modeling and updating the status of the parking lot. The cloud model can realize real-time data sharing and synchronization, ensuring that the latest environmental information can be obtained between various system components (such as mobile charging robots, automatic plug-in and unplug-out robots, etc.). Through the cloud, it is easy to achieve collaborative work between multiple terminals, such as coordinated scheduling between multiple robots. The cloud-based modeling method is easier to integrate new technologies and functions. The cloud model can be updated at any time, which means that the model can be adjusted in time with the changes in the parking lot. Using the cloud large model to model the parking lot in real time includes the following steps:

[0063] S1.1. Define a three-dimensional rectangular coordinate system for locating all objects in the parking lot, and select the center of the parking lot as the origin;

[0064] S1.2. Collect the basic structural information of the parking lot, including the location, size, shape and entrance and exit locations of the parking spaces, create a basic map of the parking lot using the basic structural information data, and digitize it to form a three-dimensional model;

[0065] S1.3, use 3D modeling software Blender to build a 3D model of the parking lot, import the digitized basic map data into the 3D modeling software, and build a 3D model based on the basic map data, including parking spaces, pillars, and walls;

[0066] S1.4. Upload the 3D model to the cloud and use the computing resources of the cloud to perform large-scale data processing and model updating.

[0067] The visual positioning unit 2 uses the PnP algorithm to identify the positions of the vehicle to be charged and the mobile charging robot based on the spatial modeling unit 1;

[0068] In this embodiment, identifying the position of the vehicle to be charged and the mobile charging robot includes the following steps:

[0069] S2.1. Continuously capture image data in the parking lot through the visual acquisition module, identify the charging equipment logo through the ORB algorithm, and extract the feature points of the vehicle to be charged and the mobile charging robot in the image;

[0070] The visual acquisition module includes multi-directional cameras;

[0071] Among them, the ORB algorithm combines the advantages of FAST corner detection and BRIEF descriptor, and can effectively detect and describe feature points in the image. It adopts efficient corner detection and descriptor generation methods. The FAST detection method is very fast, and the BRIEF descriptor only needs simple bit operations to complete, which makes the whole process can be executed quickly in the real-time system, and the location information of the charging device can be updated in real time, which is very important for the dynamic scheduling of mobile charging robots. The identification of the charging device through the ORB algorithm includes the following steps:

[0072] S2.11, first use the FAST method to quickly detect corner points, and then assign a direction to the detected corner points so that the feature description has rotation invariance;

[0073] Among them, the FAST method is a corner detection method based on pixel intensity contrast. It checks a certain number of continuous pixels (usually 16) around a pixel. If a sufficient number of these pixels (such as 9 or more) are brighter or darker than the central pixel by more than a certain threshold, the central pixel is considered to be a corner. In order to make the detected corners rotationally invariant, a direction needs to be assigned to each corner, which is done by calculating the gradient direction of the pixels around the corner. Specifically, the gradient direction of a group of pixels around each corner is calculated, and then a dominant direction is found as the direction of the corner. In this way, even if the image is rotated, the descriptor of the same feature point will remain unchanged.

[0074] S2.12. For each detected feature point, use the BRIEF descriptor to generate a fixed-length binary string that can describe the appearance information around the feature point;

[0075] The descriptor generation process is as follows: first, a set of pixel pairs are selected, which are usually randomly selected in the neighborhood of the feature point; then, for each pair of pixels, their grayscale values ​​are compared. If the grayscale value of the first pixel is greater than the second pixel, 1 is recorded in the binary string; otherwise, 0 is recorded; finally, all these bits are combined into a binary string, namely the BRIEF descriptor;

[0076] S2.13, matching is performed by comparing the BRIEF descriptors. If the match is successful, it is considered that the corresponding charging device identifier is found;

[0077] The matching process is as follows: the Hamming distance between the BRIEF descriptors of the corresponding feature points in the two images is calculated, that is, the number of mismatched bits in the two binary strings. If the Hamming distance between the two descriptors is lower than a preset threshold, the two feature points are considered to be matched;

[0078] S2.2, use OCR technology to automatically identify the license plate number and bind the license plate number to the corresponding vehicle;

[0079] Among them, OCR technology can realize automatic recognition of license plate numbers, reduce the need for manual intervention, and improve work efficiency. The OCR system can process a large amount of image data in a short time and quickly identify the license plate number. It can also be used for high-speed vehicle recognition. Once the OCR system is installed and configured, it can run continuously without additional labor costs, which can save a lot of labor costs in the long run. The OCR system can easily add new license plate style recognition functions through software upgrades. It is suitable for many occasions, including parking lot management. Using OCR technology to automatically identify license plate numbers includes the following steps:

[0080] S2.21, using the visual acquisition module to collect images containing the license plate, and intercepting frames containing the license plate from the video stream as input;

[0081] S2.22, preprocessing the video stream, converting the color image into a grayscale image, converting the color image into a grayscale image, converting the color image into a grayscale image, converting the color image into a grayscale image;

[0082] S2.23, use the Canny edge detection algorithm to find the edges in the image, and filter out the license plate area based on the size and proportion characteristics of the license plate;

[0083] Furthermore, the Canny edge detection algorithm can effectively identify true edges while reducing the cases where noise or texture is incorrectly marked as edges. Through the non-maximum suppression step, the Canny algorithm ensures that each edge is marked only once, and a single edge response is generated even if the edge is wide. This helps to avoid the problem of multiple responses during edge detection. The Canny algorithm can locate the center of the edge very accurately. This is because it uses a Gaussian filter to smooth the image and uses the gradient direction to determine the exact location of the edge. Using the Canny edge detection algorithm to find the edge in the image includes the following steps:

[0084] S2.231, use a Gaussian filter to smooth the grayscale image to remove noise;

[0085] Among them, the Gaussian filter is a linear filter that uses the Gaussian distribution function as a weight coefficient to smooth the image;

[0086] S2.232, apply the Sobel filter to calculate the gradient magnitude and direction of each pixel in the image, the gradient magnitude represents the edge strength, and the gradient direction points to the direction of the edge;

[0087] The gradient magnitude represents the strength of the edge and is calculated from the horizontal and vertical gradient components: The gradient direction refers to the direction of the edge and is calculated by the following formula:

[0088] S2.233, perform local maximum detection on the gradient amplitude according to the gradient direction, retain only the local maximum value as the edge candidate, and set the remaining pixels to zero;

[0089] When performing maximum value detection, the gradient magnitude of each pixel is compared with its two neighboring pixels along the gradient direction; only when the gradient magnitude of a pixel is larger than that of its two neighbors along the gradient direction, the pixel is considered to be part of the edge; all pixels that do not meet the conditions are set to zero, that is, they are considered not to be edges;

[0090] S2.234, set two thresholds: a low threshold and a high threshold. The high threshold is used to determine strong edge points, while the low threshold is used to determine weak edge points. Strong edge points are directly considered as edge points. If weak edge points are connected to strong edge points, they are considered as edge points; otherwise, they will be eliminated.

[0091] S2.235, use a tracking algorithm to connect edge points to form continuous edge segments;

[0092] Among them, once the strong edge points and the connected weak edge points are determined, a tracking algorithm can be used to connect these edge points. The tracking algorithm can move along the edge points until it encounters the next strong edge point or reaches the end of the edge. These continuous edge points can form a complete edge segment, thereby better depicting the outline of the object;

[0093] S2.24, calculate the pixel distribution in the vertical direction, find the gaps between the characters, and further perform horizontal segmentation on the characters in the license plate based on the vertical segmentation, and detect and segment each character on the license plate;

[0094] S2.25. Use the identified license plate number to query the vehicle information in the database and match the license plate number with the personal information of the vehicle owner.

[0095] S2.3, using the known parking lot 3D model and the extracted vehicle information to calculate the actual position of the vehicle through the PnP algorithm, and at the same time, considering the influence of noise and specular reflection of light on recognition accuracy under low light conditions, the PnP algorithm is optimized;

[0096] Among them, the PnP algorithm is used to estimate the pose of the camera relative to the three-dimensional model of the parking lot, so as to calculate the actual position of the object. The PnP algorithm uses the known 3D-2D correspondence to estimate the pose and can provide high-precision position information, which is essential for accurately determining the vehicle position. The PnP algorithm can be applied to different camera types and different scenes, and has high versatility. Whether it is a static scene or a dynamic scene, the PnP algorithm can work well, which is very important for mobile robots because they may need to navigate in a constantly changing environment. It can be combined with other visual algorithms, such as feature point detection and descriptor extraction algorithms, to improve positioning accuracy and robustness.

[0097] By optimizing the PnP algorithm, the position error caused by noise and specular reflection of light can be reduced, thereby improving the accuracy of position recognition. In low-light conditions, image noise is usually high, and specular reflection of light may produce false feature points, which will affect the performance of the PnP algorithm. By optimizing the algorithm, its robustness under complex lighting conditions can be improved; the optimized PnP algorithm can better handle light changes and environmental noise, thereby improving the consistency of recognition in different scenes; the optimized algorithm can better adapt to different environmental conditions, whether indoors or outdoors, day or night, it can maintain a high recognition accuracy. Considering the impact of noise and specular reflection of light under low-light conditions on recognition accuracy, the PnP algorithm is optimized as follows:

[0098]

[0099] Where N represents the number of feature points of the vehicle to be charged and the mobile charging robot in the image, i represents the index variable, represents the minimization objective function, R represents the rotation matrix; w i (x i ) represents the weight of the i-th feature point; E represents the expected value operator; x i represents the two-dimensional coordinates of the i-th feature point in the image; K represents the camera internal parameter matrix, which contains information such as the focal length and principal point position of the camera; R represents the rotation matrix from the center position coordinates of the parking lot to the camera coordinates; t represents the translation vector from the center position coordinates of the parking lot to the camera coordinates; X i represents the three-dimensional coordinates of the i-th feature point in the image; ΔX iIndicates the deviation between the actual position and the theoretical position of the i-th feature point. The deviation is used to reflect the influence of the specular reflection of the light. i represents an additional error term, which is used to compensate for the effects of noise and specular reflection of light under low light conditions; θ represents an adjustment parameter, which is used to balance the effects of the original error and the additional error term; represents the noise variance; γ represents the coefficient used to adjust the contribution of the reflection effect; represents the additional error caused by the specular reflection of light;

[0100] Furthermore, if there is a feature point X i The actual position in the three-dimensional space, but due to the specular reflection of the light, the observed position of the feature point on the image is x i There is a deviation from the actual position, and the error ΔX is gradually approached by the following methods i :First estimate the camera intrinsic parameter matrix (including focal length, image center and other parameters, obtained during the calibration process) and establish the rotation and translation relationship (need to obtain the rotation matrix R and translation vector t from the center position coordinate system of the parking lot to the camera coordinate system); use R and t to calculate the feature point X i Ideal image position Right now To find ΔX i , minimize x i and The error between Then solve for the deviation ΔX i , making the above error as small as possible.

[0101] S2.4. When a vehicle enters or leaves a parking space or a charging device moves, the vehicle's location information is updated in real time;

[0102] S2.5. Once the vehicle is parked in a parking space, the vehicle is automatically bound to the corresponding parking space based on the license plate recognition result. The mobile charging robot uploads the location to the cloud parking model through its own GPS and updates the location information in the system in real time.

[0103] The automatic planning and scheduling unit 3 automatically plans and schedules the nearest mobile charging robot based on the visual positioning unit 2 according to the user's call, so as to quickly respond to the user's charging needs;

[0104] In this embodiment, the automatic planning and scheduling unit 3 includes an automatic scheduling module and an emergency scheduling module;

[0105] Among them, the automatic scheduling module is used to automatically plan and call the nearest mobile charging robot and automatic plug-in gun robot for quick response and plan the operation path through the Dijkstra algorithm to reach the designated location in the shortest time. Considering the influence of obstacles, road surface flatness and road surface width on path planning, the above influence quantities are substituted into the Dijkstra algorithm process, and considering the influence of rainfall and snow depth on the road surface in extreme rain and snow weather, the Dijkstra algorithm process is further optimized;

[0106] The Dijkstra algorithm is a classic shortest path algorithm that ensures that the path from the starting point to any other node is the shortest, which means that the charging robot can reach the destination in the shortest distance, thereby reducing travel time and energy consumption. If the weights of some edges in the network change, the shortest path can be quickly updated by re-running the Dijkstra algorithm. In many cases, the Dijkstra algorithm can give results in a reasonable time, so it is suitable for real-time path planning scenarios;

[0107] Furthermore, in rainy and snowy weather conditions, the ground may be slippery or the line of sight may be obstructed, which will directly affect the robot's driving speed and stability. By adjusting the weight function to reflect these changes, it can ensure that the robot chooses a safer and more reliable path; if there are obstacles on the path, the robot needs to detour, which will increase the driving distance and time. By adjusting the weight function, obstacles can be effectively avoided, ensuring that the robot chooses an obstacle-free path and improving driving efficiency; uneven roads may cause the robot to drive unstably or even damaged. By adjusting the weight function, you can avoid choosing sections with poor road conditions and improve driving safety; narrow roads may not be suitable for robots to pass, especially when encountering other vehicles or pedestrians. By adjusting the weight function, you can choose a wider route and reduce potential traffic conflicts; comprehensive consideration of these factors can make the robot more intelligent when planning the path, not only simply finding the shortest path in physical distance, but also considering various restrictions in actual driving, so as to choose the optimal driving plan;

[0108] Taking into account the influence of obstacles, road surface flatness and road surface width on path planning, the above influence quantities are substituted into the Dijkstra algorithm process as follows:

[0109] w′(e,o)=w(e,o)+f obs (e,o)+f surf (e,o)+f width (e, o);

[0110] Considering the impact of rainfall and snow depth on the road surface in extreme rain and snow weather, the Dijkstra algorithm process is further optimized as follows:

[0111] w”(e,o)=w(e,o)+f obs (e,o)+f surf (e,o)+f width (e,o)+k r ·r(o)+k s s(o);

[0112] in,

[0113]

[0114] f surf (e, o) = k surf ·(5-s surf );

[0115]

[0116] Among them, w'(e, o) represents the edge weight after considering the influence of obstacles, road surface flatness and road surface width on path planning; w”(e, o) represents the edge weight after considering the influence of rainfall and snow depth on the road surface in extreme rain and snow weather; w(e, o) represents the original edge weight; f obs (e, o) represents the obstacle influence function; f surf (e, o) represents the road surface roughness function; f width (e, o) represents the road width influence function; e represents the edge; o represents the observation condition; k r represents the rainfall coefficient; r(o) represents the rainfall; k s represents the snow depth coefficient; s(o) represents the snow depth; k surf represents a constant factor; s surf Indicates the smoothness score of the road section (the standard is used to evaluate the smoothness from 1 to 5). The higher the smoothness score, the smaller the cost increment; k width represents a constant factor; t width Indicates the width of the road section. The larger the width, the smaller the cost increment. obs (e, o) means that if there is an obstacle on edge e (that is, the condition represented by o causes edge e to be affected by the obstacle), then the function f obs The value of (e, o) is d obs (d obs is a predefined positive value d, which is used to indicate the degree of influence of obstacles on the path cost). If there is no obstacle influence (that is, the condition represented by o does not cause edge e to be affected by obstacles), then the function f obs The value of (e, o) is 0, indicating that no additional cost is added due to obstacles;

[0117] When there is a graph containing four vertices: A, B, C, and D (vertices include parking spaces, charging stations, and the current location of the robot), and the edges between them (connections between two points), and a set of weather influence coefficients, obstacle influence coefficients, road surface flatness coefficients, and road surface width coefficients,

[0118] Set the distances of all vertices to infinity, except the distance from the starting point A to itself, which is set to 0. Create a priority queue that initially contains all vertices and sorts them by distance value.

[0119] Take the vertex with the minimum distance from the priority queue as vertex A; for all neighbors of vertex A (such as B and C), use the new weight function w" (e, o) to calculate the total distance of the paths passing through these neighbors; if the path passing through neighbor B is shorter than the currently known shortest path, update the shortest path distance of B and update the priority of B in the priority queue, and repeat this process until the end point D is found or the priority queue is empty;

[0120] Return the shortest path length from the starting point A to the end point D, and plan the driving path of the mobile charging robot based on the shortest path length.

[0121] The emergency dispatch module is used to automatically call service personnel to the designated location for service when there is no automatic plugging and unplugging robot, and the charging robot fails to recognize humans when it arrives at the designated location, and no one responds to the operation within 2 minutes.

[0122] The charging control unit 4 manages the charging process of the mobile charging robot and monitors various parameters during the charging process in real time;

[0123] Among them, the parameters include voltage, current, temperature, etc.;

[0124] In this embodiment, the charging process of managing the mobile charging robot includes the following steps:

[0125] S3.1. The automatic gun insertion and extraction robot inserts the charging gun of the charging robot into the vehicle; the mobile battery box automatically connects to the charging robot; the charging process begins, the charging robot capacity is used first for charging, and the mobile charging robot's bidirectional DC / DC is used to automatically control the input voltage range to achieve wide-range voltage compatibility to meet the use of DC low-voltage piles and DC high-voltage piles; if the capacity of a single mobile battery box is insufficient, the remaining mobile battery boxes are automatically called to come over for replacement;

[0126] S3.2. After the constant power charging phase ends, the charging robot switches to slow charging with its own battery. The mobile battery box automatically detaches to serve other vehicles or charges at nearby charging piles.

[0127] S3.3. When charging is about to end, the vehicle owner is automatically notified and the mobile automatic gun insertion and extraction robot is notified to return to the vehicle. When charging is completed, the gun is automatically extracted and the next round of service or charging is carried out nearby.

[0128] Embodiment 2:

[0129] This embodiment provides a split mobile charging robot based on automatic combination connection technology, and the intelligent management and control system of the split mobile charging robot based on automatic combination connection technology described in any one of the claims is applied to the split mobile charging robot based on automatic combination connection technology.

[0130] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. An intelligent management and control system for a split mobile charging robot based on automatic combination connection technology, characterized in that: include: A space modeling unit (1), wherein the space modeling unit (1) uses a cloud-based large model to perform real-time modeling of the parking lot; A visual positioning unit (2), wherein the visual positioning unit (2) uses a PnP algorithm to identify the positions of the vehicle to be charged and the mobile charging robot based on the space modeling unit (1), and optimizes the PnP algorithm by taking into account the influence of noise and specular reflection of light under low light conditions on the recognition accuracy; An automatic planning and scheduling unit (3), wherein the automatic planning and scheduling unit (3) automatically plans and schedules the nearest mobile charging robot based on the visual positioning unit (2) according to the user's call, so as to quickly respond to the user's charging needs; A charging control unit (4), wherein the charging control unit (4) manages the charging process of the mobile charging robot and monitors various parameters during the charging process in real time.

2. According to claim 1, the intelligent management and control system of the split mobile charging robot based on the automatic combination connection technology is characterized in that: The space modeling unit (1) uses a cloud-based large model to perform real-time modeling of the parking lot, including the following steps: S1.

1. Define a three-dimensional rectangular coordinate system for locating all objects in the parking lot, and select the center of the parking lot as the origin; S1.

2. Collect the basic structural information of the parking lot, including the location, size, shape and entrance and exit locations of the parking spaces, create a basic map of the parking lot using the basic structural information data, and digitize it to form a three-dimensional model; S1.3, use 3D modeling software Blender to build a 3D model of the parking lot, import the digitized basic map data into the 3D modeling software, and build a 3D model based on the basic map data, including parking spaces, pillars, and walls; S1.

4. Upload the 3D model to the cloud and use the computing resources of the cloud to perform large-scale data processing and model updating.

3. According to claim 1, the intelligent management and control system of the split mobile charging robot based on the automatic combination connection technology is characterized in that: The visual positioning unit (2) identifies the positions of the vehicle to be charged and the mobile charging robot, comprising the following steps: S2.

1. Continuously capture image data in the parking lot through the visual acquisition module, identify the charging equipment logo through the ORB algorithm, and extract the feature points of the vehicle to be charged and the mobile charging robot in the image; S2.2, use OCR technology to automatically identify the license plate number and bind the license plate number to the corresponding vehicle; S2.3, using the known parking lot 3D model and the extracted vehicle information to calculate the actual position of the vehicle through the PnP algorithm, and at the same time, considering the influence of noise and specular reflection of light on recognition accuracy under low light conditions, the PnP algorithm is optimized; Among them, considering the influence of noise and specular reflection of light on recognition accuracy under low light conditions, the PnP algorithm is optimized as follows: Where N represents the number of feature points of the vehicle to be charged and the mobile charging robot in the image, i represents the index variable, represents the minimization objective function, R represents the rotation matrix; w i (x i ) represents the weight of the i-th feature point; E represents the expected value operator; x i represents the two-dimensional coordinates of the i-th feature point in the image; K represents the camera intrinsic parameter matrix; R represents the rotation matrix from the center position coordinates of the parking lot to the camera coordinates; t represents the translation vector from the center position coordinates of the parking lot to the camera coordinates; X i represents the three-dimensional coordinates of the i-th feature point in the image; ΔX i Indicates the deviation between the actual position and the theoretical position of the i-th feature point. The deviation is used to reflect the influence of the specular reflection of the light. i represents an additional error term used to compensate for the effects of noise and specular reflection of light in low light conditions; S2.

4. When a vehicle enters or leaves a parking space or a charging device moves, the vehicle's location information is updated in real time; S2.

5. Once the vehicle is parked in a parking space, the vehicle is automatically bound to the corresponding parking space based on the license plate recognition result. The mobile charging robot uploads the location to the cloud parking model through its own GPS and updates the location information in the system in real time.

4. The intelligent management and control system of the split mobile charging robot based on the automatic combination connection technology according to claim 3 is characterized in that: In S2.1, identifying the charging device identifier by using the ORB algorithm includes the following steps: S2.11, first use the FAST method to quickly detect corner points, and then assign a direction to the detected corner points; S2.

12. For each detected feature point, use the BRIEF descriptor to generate a fixed-length binary string; S2.

13. Matching is performed by comparing the BRIEF descriptors. If the match is successful, it is considered that the corresponding charging device identifier has been found.

5. The intelligent management and control system of the split mobile charging robot based on the automatic combination connection technology according to claim 3 is characterized in that: In S2.2, the automatic recognition of the license plate number using OCR technology includes the following steps: S2.21, using the visual acquisition module to collect images containing the license plate, and intercepting frames containing the license plate from the video stream as input; S2.22, preprocessing the video stream; S2.23, use the Canny edge detection algorithm to find the edges in the image, and filter out the license plate area based on the size and proportion characteristics of the license plate; S2.24, calculate the pixel distribution in the vertical direction, find the gaps between the characters, and further perform horizontal segmentation on the characters in the license plate based on the vertical segmentation, and detect and segment each character on the license plate; S2.

25. Use the identified license plate number to query the vehicle information in the database and match the license plate number with the personal information of the vehicle owner.

6. The intelligent management and control system of the split mobile charging robot based on the automatic combination connection technology according to claim 5 is characterized in that: In S2.23, the Canny edge detection algorithm is used to find the edges in the image, including the following steps: S2.231, use a Gaussian filter to smooth the grayscale image to remove noise; S2.232, apply the Sobel filter to calculate the gradient magnitude and direction of each pixel in the image, the gradient magnitude represents the edge strength, and the gradient direction points to the direction of the edge; S2.233, perform local maximum detection on the gradient amplitude according to the gradient direction, retain only the local maximum value as the edge candidate, and set the remaining pixels to zero; S2.234, set two thresholds: a low threshold and a high threshold. The high threshold is used to determine strong edge points, while the low threshold is used to determine weak edge points. Strong edge points are directly considered as edge points. If weak edge points are connected to strong edge points, they are considered as edge points; otherwise, they will be eliminated. S2.

235. Use a tracking algorithm to connect edge points to form continuous edge segments.

7. The intelligent management and control system of the split mobile charging robot based on the automatic combination connection technology according to claim 1 is characterized in that: The automatic planning and scheduling unit (3) comprises an automatic scheduling module and an emergency scheduling module; Among them, the automatic scheduling module is used to automatically plan and call the nearest mobile charging robot and automatic plug-in gun robot for quick response and plan the operation path through the Dijkstra algorithm to reach the designated location in the shortest time. Considering the influence of obstacles, road surface flatness and road surface width on path planning, the above influence quantities are substituted into the Dijkstra algorithm process, and considering the influence of rainfall and snow depth on the road surface in extreme rain and snow weather, the Dijkstra algorithm process is further optimized; The emergency dispatch module is used to automatically call service personnel to the designated location for service when there is no automatic plugging and unplugging robot, and the charging robot fails to recognize humans when it arrives at the designated location, and no one responds to the operation within 2 minutes.

8. The intelligent management and control system of the split mobile charging robot based on the automatic combination connection technology according to claim 7 is characterized in that: Taking into account the influence of obstacles, road surface flatness and road surface width on path planning, the above influence quantities are substituted into the Dijkstra algorithm process, specifically: w′(e,o)=w(e,o)+f obs (e,o)+f surf (e,o)+f width (e, o); Considering the impact of rainfall and snow depth on the road surface in extreme rain and snow weather, the Dijkstra algorithm process is further optimized as follows: w″(e,o)=w(e,o)+f obs (e,o)+f surf (e,o)+f width (e,o)+k r ·r(o)+k s ·s(o); Among them, w'(e, o) represents the edge weight after considering the influence of obstacles, road surface flatness and road surface width on path planning; w”(e, o) represents the edge weight after considering the influence of rainfall and snow depth on the road surface in extreme rain and snow weather; w(e, o) represents the original edge weight; f obs (e, o) represents the obstacle influence function; f surf (e, o) represents the road surface roughness function; f width (e, o) represents the road width influence function; e represents the edge; o represents the observation condition; k r represents the rainfall coefficient; r(o) represents the rainfall; k s represents the snow depth coefficient; s(o) represents the snow depth.

9. The intelligent management and control system of the split mobile charging robot based on the automatic combination connection technology according to claim 1 is characterized in that: The charging control unit (4) manages the charging process of the mobile charging robot, including the following steps: S3.

1. The automatic gun insertion and extraction robot inserts the charging gun of the charging robot into the vehicle; the mobile battery box automatically connects to the charging robot; the charging process begins, the charging robot capacity is used first for charging, and the mobile charging robot's bidirectional DC / DC is used to automatically control the input voltage range to achieve wide-range voltage compatibility to meet the use of DC low-voltage piles and DC high-voltage piles; if the capacity of a single mobile battery box is insufficient, the remaining mobile battery boxes are automatically called to come over for replacement; S3.

2. After the constant power charging phase ends, the charging robot switches to slow charging with its own battery, and the mobile battery box automatically detaches to serve other vehicles. S3.

3. When charging is about to end, the vehicle owner is automatically notified and the mobile automatic gun insertion and extraction robot is notified to return to the vehicle. When charging is completed, the gun is automatically extracted to enter the next round of service.

10. A split mobile charging robot based on automatic combination connection technology, characterized in that: The intelligent management and control system of a split mobile charging robot based on automatic combination connection technology described in any one of claims 1 to 9 is applied to a split mobile charging robot based on automatic combination connection technology.

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